WO2015145763A1 - Respiratory sound analysis device, respiratory sound analysis method, computer program and recording medium - Google Patents

Respiratory sound analysis device, respiratory sound analysis method, computer program and recording medium Download PDF

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Publication number
WO2015145763A1
WO2015145763A1 PCT/JP2014/059279 JP2014059279W WO2015145763A1 WO 2015145763 A1 WO2015145763 A1 WO 2015145763A1 JP 2014059279 W JP2014059279 W JP 2014059279W WO 2015145763 A1 WO2015145763 A1 WO 2015145763A1
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Prior art keywords
sound
respiratory
sounds
breathing
frequency
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PCT/JP2014/059279
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French (fr)
Japanese (ja)
Inventor
石戸谷　耕一
隆真 亀谷
英幸 大久保
友博 三浦
長谷部　剛
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パイオニア株式会社
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Priority to PCT/JP2014/059279 priority Critical patent/WO2015145763A1/en
Priority to JP2016509855A priority patent/JPWO2015145763A1/en
Publication of WO2015145763A1 publication Critical patent/WO2015145763A1/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs

Definitions

  • the present invention relates to a breathing sound analysis apparatus and a breathing sound analysis method for analyzing a breathing sound including a plurality of sound types, and a computer program and a recording medium.
  • Patent Document 1 proposes a technique of outputting an abnormal sound analysis result with a three-dimensional display device.
  • Patent Document 2 proposes a technique for detecting a rale as an abnormal sound included in a respiratory sound.
  • Japanese Patent Application Laid-Open No. 2004-228688 proposes a technique of dividing a sound waveform into a plurality of sections and determining a sound type.
  • Patent Documents 1 to 3 described above have a technical problem that a plurality of sound types included in the respiratory sound cannot be arbitrarily output.
  • a breathing sound analyzing apparatus for solving the above-described problems includes a sorting unit that sorts a breathing sound into a normal sound and an abnormal sound, and an output unit that outputs an arbitrary breathing sound among the sorted breathing sounds.
  • the respiratory sound analysis method for solving the above-described problems includes a classification process for classifying respiratory sounds into normal sounds and abnormal sounds, and an output process for outputting an arbitrary respiratory sound among the classified respiratory sounds.
  • a computer program for solving the above-described problems causes a computer to execute a classification process for classifying respiratory sounds into normal sounds and abnormal sounds, and an output process for outputting arbitrary respiratory sounds among the classified respiratory sounds.
  • the recording medium for solving the above problem is recorded with the computer program described above.
  • the respiratory sound analysis apparatus includes a classification unit that classifies a respiratory sound into a normal sound and an abnormal sound, and an output unit that outputs an arbitrary respiratory sound among the sorted respiratory sounds.
  • the respiratory sound analyzer during the operation, the respiratory sound is first classified into a normal sound and an abnormal sound.
  • the breathing sound separated by the sorting means may not be the normal sound and the abnormal sound, and each of the normal sound and the abnormal sound may be further classified into a plurality of sound types.
  • the abnormal sound may be classified into a whistle sound, an analogy sound, a haircut sound, and the like.
  • it does not specifically limit about a specific classification method, What is necessary is just to be able to classify in the state which can output the arbitrary respiratory sounds mentioned later.
  • any breathing sound among the separated breathing sounds is output. That is, a plurality of sorted respiratory sounds are selectively output.
  • a plurality of sorted respiratory sounds are selectively output.
  • it does not specifically limit about an output aspect You may output as an audio
  • diagnosis of a health condition can be easily performed.
  • diagnosis of a health condition can be easily performed.
  • a sound is heard in a state where a plurality of breathing sounds are mixed, it is difficult for a skilled doctor to distinguish and listen to each sound type. It becomes possible to easily discriminate the included sound types.
  • it is possible to easily listen to only a specific sound type included in the breathing sound, it can be utilized in doctor education and research.
  • the respiratory sound analysis apparatus since the respiratory sound can be sorted and any respiratory sound can be output, the respiratory sound including a plurality of sound types can be suitably analyzed. Is possible.
  • the output means outputs a plurality of respiratory sounds simultaneously among the sorted respiratory sounds.
  • a plurality of respiratory sounds can be output simultaneously (in other words, in a superimposed state) as arbitrary respiratory sounds, desired respiratory sounds can be combined and output as appropriate.
  • the output means outputs the arbitrary respiratory sound as a voice or a spectrum image.
  • any respiratory sound among the sorted respiratory sounds is output as sound using, for example, a speaker or headphones.
  • an arbitrary breathing sound is output as a spectrum image using a display such as a liquid crystal monitor. Therefore, any output breathing sound can be suitably used.
  • the respiratory sound analyzing apparatus further includes changing means capable of changing the output state of the arbitrary respiratory sound for each sound type.
  • the output state of any respiratory sound (for example, output volume, image display mode, etc.) can be changed for each sound type, the output arbitrary respiratory sound can be used more suitably.
  • the specific abnormal sound can be easily heard even when a plurality of respiratory sounds are mixed.
  • it is considered that it becomes easy to listen thereafter. Therefore, it can be effectively used for training of inexperienced doctors.
  • the changing means may be capable of changing the output volume of the arbitrary respiratory sound for each sound type.
  • the output volume can be changed for each sound type, the convenience can be improved by making it easy to hear only the desired breathing sound.
  • the changing means may be able to change the output volume of the arbitrary breathing sound for each predetermined frequency band.
  • the output volume for each sound type can be changed in more detail, for example, it is possible to increase only the output volume in a predetermined frequency band to make it easier to hear a desired breathing sound.
  • the predetermined frequency band may be set according to the characteristics of the sound types to be sorted.
  • the changing unit may be capable of executing predetermined image processing for each sound type on the image indicating the arbitrary breathing sound.
  • a predetermined image processing is performed on an image showing an arbitrary breathing sound (for example, a spectrogram obtained by extracting only one kind of breathing sound), and a state where it can be easily recognized visually can be realized.
  • the predetermined image processing include color change (for example, RGB adjustment), binarization, edge detection, and the like.
  • the changing unit may be able to output an image obtained by performing the predetermined image processing for each sound type by superimposing the plurality of sound types.
  • the classification unit is an acquisition unit that acquires information about a frequency corresponding to a predetermined characteristic of the respiratory sound spectrum, and is a reference for classifying the respiratory sound.
  • Shift means for shifting a plurality of reference spectra in accordance with information on the frequency to obtain a frequency shift reference spectrum, and the plurality of the plurality of reference spectra included in the respiratory sound based on the respiratory sound and the frequency shift reference spectrum
  • a ratio output means for outputting a ratio of the reference spectrum.
  • the classification means first, information regarding the frequency corresponding to the predetermined characteristic of the spectrum of the respiratory sound is acquired.
  • the “predetermined feature” means a feature that occurs at a specific frequency according to the sound type included in the spectrum of the body sound, and is, for example, a peak that appears in a frequency-analyzed signal.
  • the “information about the frequency” is not limited to the information that directly indicates the frequency, but includes information that can indirectly derive the frequency.
  • a plurality of reference spectra serving as a reference for classifying the respiratory sounds are shifted according to the information about the frequency, and the frequency shift reference spectrum is acquired.
  • the “reference spectrum” here refers to each sound type in order to classify a plurality of sound types included in the respiratory sound (for example, normal respiratory sound, continuous rarity sound, haircut sound, etc.). This is a preset spectrum.
  • the reference spectrum is frequency-shifted according to, for example, a peak position, which is a predetermined feature acquired from a respiratory sound, and becomes a frequency-shifted reference spectrum.
  • a ratio of a plurality of reference spectra included in the respiratory sound is output based on the respiratory sound and the frequency shift reference spectrum. Specifically, the proportion of sound types corresponding to a plurality of reference spectra is calculated in the respiratory sound to be analyzed, and the result is output. More specifically, the ratio of the reference spectrum is calculated as a coupling coefficient by executing a calculation based on a plurality of reference spectra, for example, for the spectrum of the respiratory sound.
  • respiratory sounds including a plurality of sound types can be suitably classified.
  • the ratio of each sound type can be suitably classified.
  • the predetermined feature may be a local maximum value.
  • frequency analysis by fast Fourier transform FFT: Fourier ⁇ Transform
  • FFT Fast Fourier transform
  • information on a frequency corresponding to the maximum value (that is, peak) of the analysis result is acquired.
  • the information about the frequency is acquired as corresponding to the position of the maximum value, but even if the frequency is not completely coincident with the position of the maximum value, it is acquired as information about the frequency corresponding to the position near the maximum value. Also good.
  • the frequency-related information can be acquired more easily and accurately by using the maximum value as the predetermined characteristic of the spectrum of the respiratory sound.
  • the respiratory sound analysis method includes a classification process for classifying respiratory sounds into normal sounds and abnormal sounds, and an output process for outputting an arbitrary respiratory sound among the classified respiratory sounds.
  • the respiratory sound analysis method it is possible to suitably analyze a respiratory sound including a plurality of sound types, similarly to the respiratory sound analysis apparatus according to the present embodiment described above.
  • the computer program according to the present embodiment causes the computer to execute a classification process of classifying respiratory sounds into normal sounds and abnormal sounds, and an output process of outputting arbitrary respiratory sounds among the classified respiratory sounds.
  • the computer can execute the same processing as the above-described respiratory sound analysis method according to the present embodiment, so that a respiratory sound including a plurality of sound types can be suitably analyzed.
  • the recording medium according to the present embodiment records the above-described computer program.
  • the recording medium it is possible to suitably analyze a respiratory sound including a plurality of sound types by causing the computer program described above to be executed by a computer.
  • FIG. 1 is a block diagram showing the overall configuration of the respiratory sound analysis apparatus according to this embodiment.
  • the respiratory sound analysis apparatus includes, as main components, a body sound sensor 110, a signal storage unit 120, a signal processing unit 125, a sound output unit 130, and a base holding unit 140. , A display unit 150, an input unit 160, and a processing unit 200.
  • the body sound sensor 110 is a sensor configured to be able to detect a breathing sound of a living body.
  • the biological sound sensor 110 includes, for example, an ECM (Electret Condenser Microphone), a microphone using a piezo, a vibration sensor, and the like.
  • the signal storage unit 120 is configured as a buffer such as a RAM (Random Access Memory), for example, and temporarily stores a signal indicating a respiratory sound detected by the biological sound sensor 110 (hereinafter, referred to as a “respiratory sound signal” as appropriate). To remember.
  • the signal storage unit 120 is configured to be able to output the stored signal to the audio output unit 130 and the processing unit 200, respectively.
  • the signal processing unit 125 processes the sound acquired by the biological sound sensor 110 and outputs the processed sound to the audio output unit 130.
  • the signal processing unit 125 functions as, for example, an equalizer or a filter, and processes the acquired sound so that it can be easily heard by a person.
  • the audio output unit 130 is configured as a speaker or a headphone, for example, and outputs a respiratory sound detected by the biological sound sensor 110 and processed by the signal processing unit 125.
  • the base holding unit 140 is configured, for example, as a ROM (Read Only Memory) or the like, and stores a base corresponding to a predetermined sound type that can be included in the respiratory sound.
  • the basis according to the present embodiment is an example of the “reference spectrum” in the present invention.
  • the display unit 150 is configured as a display such as a liquid crystal monitor, for example, and displays image data output from the processing unit 200.
  • the input unit 160 is a device that accepts input by the user, and is configured as, for example, a keyboard, a mouse, a touch panel, various switches, and the like.
  • the input unit 160 is configured to be capable of performing an input operation for selecting at least a breathing sound to be output.
  • the processing unit 200 includes a plurality of arithmetic circuits and memories.
  • the processing unit 200 includes a frequency analysis unit 210, a frequency peak detection unit 220, a basis set generation unit 230, a coupling coefficient calculation unit 240, a signal intensity calculation unit 250, an image generation unit 260, and a breathing sound selection unit 270.
  • FIG. 2 is a flowchart showing the operation of the respiratory sound analysis apparatus according to this embodiment.
  • a simple description for grasping the overall flow of processing executed by the respiratory sound analysis apparatus according to the present embodiment will be given. Details of each process will be described later.
  • a respiratory sound is first detected by the biological sound sensor 110, and a respiratory sound signal is acquired by the processing unit 200 (step S101).
  • the frequency analysis unit 210 performs frequency analysis (for example, fast Fourier transform) (step S102). Further, the frequency peak detection unit 220 detects a peak (maximum value) using the frequency analysis result.
  • frequency analysis for example, fast Fourier transform
  • a base set is generated in the base set generation unit 230 (step S103). Specifically, the base set generation unit 230 generates a base set using the base stored in the base holding unit 140. At this time, the basis set generation unit 230 shifts the basis based on the peak position (that is, the corresponding frequency) obtained from the frequency analysis result.
  • the coupling coefficient calculation unit 240 calculates the coupling coefficient based on the frequency analysis result and the basis set (step S104).
  • the signal intensity calculation unit 250 calculates the signal intensity corresponding to the coupling coefficient (step S105). In other words, the ratio of each sound type included in the respiratory sound signal is calculated.
  • the image generation unit 260 When the signal strength is calculated, the image generation unit 260 generates image data indicating the signal strength. The generated image data is displayed as an analysis result on the display unit 150 (step S106).
  • step S107 After the analysis result is displayed, when the sound type to be output is input by the user (step S107: YES), the respiratory sound to be output is selected by the respiratory sound selection unit 270, and the selected sound type is the audio output unit 130. Or it outputs to the display part 150 (step S108).
  • FIG. 3 is a spectrogram showing the frequency analysis result of the breathing sound including the haircut sound
  • FIG. 4 is a spectrogram showing the frequency analysis result of the breathing sound including the whistle sound.
  • the spectrogram pattern corresponding to the haircut sound in addition to the spectrogram pattern corresponding to the normal breathing sound, the spectrogram pattern corresponding to the haircut sound that is one of the abnormal breathing sounds is observed.
  • the spectrogram pattern corresponding to the haircut sound has a shape close to a rhombus, as shown in the enlarged portion in the figure.
  • the spectrogram pattern corresponding to the whistle voice which is one of the abnormal breathing sounds is observed.
  • the spectrogram pattern corresponding to the whistle voice is shaped like a swan's neck as shown in the enlarged portion of the figure.
  • the respiratory sound analysis apparatus executes an analysis for separating a plurality of mixed sound types in this way.
  • FIG. 5 is a graph showing a spectrum at a predetermined timing of the breathing sound including the haircut sound
  • FIG. 6 is a conceptual diagram showing an approximation method of the spectrum of the breathing sound including the haircut sound.
  • FIG. 7 is a graph showing a spectrum of a breathing sound including a whistle sound at a predetermined timing
  • FIG. 8 is a conceptual diagram showing an approximation method of a spectrum of a breathing sound including a whistle sound.
  • the spectrum corresponding to the normal breathing sound and the spectrum corresponding to the haircut sound can be estimated in advance by experiments or the like. For this reason, if a pre-estimated pattern is used, it is possible to know in what ratio the component corresponding to the normal breathing sound and the component corresponding to the haircut sound are included in the above-described spectrum.
  • the spectrum corresponding to the whistle sound can be estimated in advance by experiments or the like in the same manner as the normal breathing sound and the haircut sound described above. For this reason, if a pre-estimated pattern is used, it is possible to know in what ratio the component corresponding to the normal breathing sound and the component corresponding to the whistle sound are included in the above-described spectrum.
  • FIG. 9 is a graph illustrating an example of the frequency analysis method
  • FIG. 10 is a diagram illustrating an example of the frequency analysis result
  • FIG. 11 is a conceptual diagram showing a spectrum peak detection result.
  • frequency analysis is first performed on the acquired respiratory sound signal.
  • the frequency can be obtained using an existing technique such as fast Fourier transform.
  • an amplitude value for each frequency (that is, an amplitude spectrum) is used as a frequency analysis result.
  • a window function for example, Hanning window etc.
  • the frequency analysis result is obtained as consisting of n values.
  • N is a value determined by a window size or the like in frequency analysis.
  • peak detection is performed for the spectrum obtained by frequency analysis.
  • peaks p1 to p4 are detected at positions of 100 Hz, 130 Hz, 180 Hz, and 320 Hz.
  • the peak detection process may be a simple process because it is only necessary to know at which frequency the peak exists. However, it is preferable that the peak detection parameters are set so that even a small peak is not missed.
  • N is a predetermined value
  • the maximum value is obtained from the point where the sign of the difference switches from positive to negative.
  • the second derivative is approximated by the difference.
  • N values having a value smaller than a predetermined threshold (negative value) are selected in order from the smallest, and the positions are stored.
  • FIG. 12 is a graph showing the normal alveolar respiratory sound base.
  • FIG. 13 is a graph showing the haircut sound base,
  • FIG. 14 is a graph showing the continuous ra sound base, and
  • FIG. 15 is a graph showing the white noise base.
  • FIG. 16 is a graph showing frequency-shifted continuous ra sound bases.
  • each base corresponding to each sound type has a specific shape.
  • Each base is composed of n numerical values (that is, amplitude values for each frequency) that are the same as the frequency analysis result.
  • Each base is normalized so that an area surrounded by a line indicating the amplitude value for each frequency and the frequency axis becomes a predetermined value (for example, 1).
  • the base corresponding to the continuous rale among the above-mentioned bases is frequency-shifted according to the peak position detected from the result of frequency analysis.
  • the continuous ra sound base is frequency-shifted in accordance with each of the peaks p1 to p4 shown in FIG.
  • the basis set is generated as a set of normal alveolar respiratory sound bases, hair hair sound bases, continuous ra sound bases corresponding to the number of detected peaks, and white noise bases.
  • FIG. 17 is a diagram showing the relationship between the spectrum, the basis, and the coupling coefficient
  • FIG. 18 is a diagram showing an example of the observed spectrum and the basis used for approximation
  • FIG. 19 is a diagram showing an approximation result by non-negative matrix factorization.
  • the spectrum y and each base h (f) have n values.
  • the coupling coefficient has m values. “M” is the number of bases included in the base set.
  • the coupling coefficient of each base included in the base set is calculated using non-negative matrix factorization. Specifically, u that minimizes the optimization criterion function D expressed by the following formula (2) (however, each component value of u is not negative) may be obtained.
  • general non-negative matrix factorization is a method for calculating both a base matrix representing a set of base spectra and an activation matrix representing a coupling coefficient.
  • the base matrix is fixed. Only the coupling coefficient is calculated.
  • an approximation method other than non-negative matrix factorization may be used as means for calculating the coupling coefficient.
  • the condition that it is non-negative is desired.
  • the reason for using the non-negative approximation method will be described with a specific example.
  • the expected coupling coefficient u under the condition of being non-negative is 1 for the base A, 1 for the base B, 0 for the base C, and 0 for the base D. It is 0 to do. That is, on the condition that it is non-negative, the observed spectrum is approximated as a spectrum obtained by adding the base A multiplied by 1 and the base B multiplied by 1.
  • the expected coupling coefficient u when the condition is not negative is 0 for the base A, 0 for the base B, 1 for the base C, and 1 for the base D. What to do is -0.5. That is, if the condition is not negative, the observed spectrum is approximated as a spectrum obtained by adding the base C multiplied by 1 and the base D multiplied by ⁇ 0.5.
  • the coupling coefficient u here represents the component amount for each spectrum, it must be obtained as a non-negative value. In other words, when the coupling coefficient u is obtained as a negative value, it cannot be interpreted as a component amount. On the other hand, if the approximation is performed under a non-negative condition, the coupling coefficient u corresponding to the component amount can be calculated.
  • the coupling coefficient u is calculated as having seven values from u 1 to u 7 .
  • the coupling coefficient u 1 corresponding to the normal alveolar respiratory sound base is a value indicating the ratio of the normal alveolar respiratory sound to the respiratory sound.
  • the coupling coefficient u 2 corresponding to the hair hair base the coupling coefficient u 3 corresponding to the white noise base, the continuous coefficient shifted to 100 Hz, the coupling coefficient u 4 corresponding to the sound base, and the continuous coefficient shifted to 130 Hz.
  • the coupling coefficient u 5 corresponding to the sound base the coupling coefficient u 6 corresponding to the continuous ra sound base shifted to 180 Hz
  • the coupling coefficient u 7 corresponding to the continuous ra sound base shifted to 320 Hz breathing is also performed. It can be said that the value indicates the ratio of each sound type to the sound. Therefore, the signal intensity of each sound type can be calculated from the coupling coefficient u.
  • a plurality of sound types included in the respiratory sound are classified using a plurality of bases corresponding to each sound type.
  • the classification method described above is merely an example, and a plurality of sound types may be classified using other classification methods.
  • FIGS. 20 and 21 are conceptual diagrams showing a method for separating continuous rales according to the first modification.
  • the breathing sound is classified into a continuous ra sound and other sounds. Specifically, when the peak frequency detected from the frequency analysis result of the respiratory sound signal fluctuates within a predetermined range, it is determined that the sound is a continuous rale.
  • the whistle sound and the like sound that are continuous rales fluctuate so that the peak positions continuously detected on the time axis fall within a predetermined range.
  • the peak frequency changes so as to have continuity in time. Therefore, when the continuous peak position is within the predetermined range, it can be determined that the sound is a continuous ra-tone.
  • the sounds other than the continuous rarity fluctuate so that the peak positions continuously detected on the time axis do not fall within a predetermined range.
  • the peak frequency does not have temporal continuity and changes discretely. Therefore, when the continuous peak position is not within the predetermined range, it can be determined that the sound is not a continuous rarity.
  • the determination result of multiple times can also be used for determination of continuous rales. Specifically, when the number of times the peak position continuously detected on the time axis has fluctuated so as to be within a predetermined range has continued for a predetermined number of times, the sound is a continuous rar sound. May be determined.
  • FIG. 22 is a graph showing threshold values used for classification of whistle sounds and similar sounds according to the second modification.
  • the continuous rar sound is classified into a whistle sound and a similar sound.
  • whistle voice sounds and analogy sounds can be distinguished by their pitch (ie, frequency), as whistle voice sounds are called high-pitched continuous rales and analogy sounds are called low-pitched continuous rales.
  • the peak frequency of the whistle sound and the like sound changes with time. For this reason, when trying to determine a whistle voice and similar sounds using a single threshold for the peak frequency (that is, one threshold whose value does not vary), the determination result changes over time. There is. For example, if the peak frequency changes so as to cross the determination threshold, what has been accurately determined until then will be determined as an incorrect sound type. For this reason, in the second modification, the determination threshold value is varied according to the peak frequency.
  • the threshold value fluctuates so that the ratio for determining the whistle sound and the ratio for determining the analog sound smoothly change according to the peak frequency. For example, when the peak frequency is 200 Hz, it is determined that 7% of whistle sounds are included and 93% of similar sounds are included. When the peak frequency is 250 Hz, it is determined that 50% of whistle sounds are included and 50% of similar sounds are included. When the peak frequency is 280 Hz, it is determined that 78% of whistle sounds are included and 22% of similar sounds are included.
  • the specific numerical values here are merely examples, and different values may be set. Moreover, you may make it have a variation characteristic which changes with sex, age, height, weight, etc. of the biological body which is a measuring object.
  • the threshold value for determining the whistle voice sound and the analogy sound changes so as to become an appropriate value according to the peak frequency, and thus, for example, a single threshold value that does not change is used. Compared to the case, more accurate separation can be performed.
  • FIG. 23 is a graph showing the initial value of the threshold value used for classification of the whistle sound and the analogy sound according to the third modification.
  • FIG. 24 and FIG. 25 are graphs showing the adjusted values of the threshold values used for the distinction between the whistle vocal sound and the analogy sound according to the third modification.
  • the classification method according to the third modified example is also a method of separating continuous rales into whistle sounds and similar sounds as in the second modified example already described. Moreover, it is the same as that of the 2nd modification also about the point determined using the threshold value with respect to the peak frequency obtained from the frequency analysis result.
  • the determination result is set to change at a threshold of 250 Hz. Specifically, when the peak frequency is 250 Hz or more, it is determined that the continuous rale includes 100% of the whistle sound component and does not include the analogy sound. On the other hand, when the peak frequency is less than 250 Hz, it is determined that the continuous rale includes 100% of the similar sound component and does not include the whistle sound.
  • the threshold value is lowered from 250 Hz to 220 Hz when it is determined that the whistle voice component is 100% in the immediately preceding determination. Therefore, it is easy to determine that the whistle voice component is 100%. Specifically, considering the case where the peak frequency is 230 Hz, according to the initial threshold value (see FIG. 23), it is determined as analogy, but according to the adjusted threshold value (see FIG. 24). Judged as a whistle sound.
  • the threshold value is increased from 250 Hz to 280 Hz when it is determined in the previous determination that the analog sound component is 100%. Therefore, it is easy to determine that the analog sound component is 100%. Specifically, considering the case where the peak frequency is 270 Hz, the whistle sound is determined according to the initial threshold value (see FIG. 23), but according to the adjusted threshold value (see FIG. 25). Judged as roaring.
  • the threshold value is adjusted as described above, erroneous determination due to fluctuations in peak frequency can be suitably prevented. That is, in the classification method according to the third modified example, the threshold value for determining the whistle sound and the analogy sound is adjusted to an appropriate value based on the past determination result. Compared with the case of using, more accurate determination can be performed.
  • the adjustment of the threshold value may be performed based on a plurality of past determination results, not just the previous determination result. Further, when a plurality of past determination results are used, each determination result may be weighted. For example, weighting may be performed so that the influence becomes smaller as the past determination results.
  • the smooth threshold value of the second modification may be used as the initial value of the threshold value to be adjusted (see FIG. 22).
  • FIG. 26 is a spectrogram diagram of a breathing sound including a whistle voice
  • FIG. 27 is a graph showing the peak frequency and the number of peaks of the whistle sound
  • FIG. 28 is a spectrogram diagram of a respiratory sound including a similar sound
  • FIG. 29 is a graph showing the peak frequency and the number of peaks of the similar sound.
  • the separation method according to the fourth modification is also a method of separating continuous rales into whistle sounds and similar sounds, as in the second and third modifications already described.
  • a breathing sound including a whistle voice sound is detected as a spectrum waveform having a predetermined peak.
  • a frequency-amplitude graph corresponding to a single time of the spectrum waveform that is, a region surrounded by a white frame in the figure.
  • the peak frequency F1 and the peak number N1 of the whistle voice can be detected. It is known that the distribution of the peak frequency of the whistle sound is about 180 to 900 Hz. As can be seen from the figure, the peak number N1 of the whistle voice sound is one.
  • the breathing sound including the analogy sound is detected as a spectrum waveform having a predetermined peak different from the whistle sound.
  • a frequency-amplitude graph corresponding to a single time of the spectrum waveform is similarly created.
  • the peak frequency F2 and the number of peaks N2 of the analogy sound can be detected. It is known that the distribution of the peak frequency of analogy is about 100 to 260 Hz. That is, the peak frequency F2 of the analogy sound is distributed in a region lower than the peak frequency F1 of the whistle sound. Further, as can be seen from the figure, the number N2 of analogy sounds is, for example, three. That is, the peak number N2 of analogy sounds is not one, but a plurality, like the peak number N1 of whistle sounds.
  • the determination is performed using the difference in the characteristics of the above-described whistle sound and analogy sound. Specifically, based on each of the peak frequency F and the peak number N, the whistle sound and the analogy sound are separated. In this way, for example, more accurate classification can be performed as compared with the case where only the peak frequency F is used to separate the whistle sound and the analogy sound.
  • FIG. 30 is a plan view showing a display example on the display unit.
  • the analysis results are displayed as a plurality of images in the display area 155 of the display unit 150.
  • the waveform of the acquired respiratory sound is displayed in the area 155a.
  • the spectrum of the acquired respiratory sound is displayed in the region 155b.
  • a spectrogram of the acquired respiratory sound is displayed in the region 155c.
  • a graph representing the time series change of the component amount of each classified sound type here, five sound types of normal breathing sound, analogy sound, whistle sound, haircut sound, and water bubble sound
  • the ratio of each classified sound type is displayed as a radar chart.
  • the ratio of each classified sound type may be displayed as a bar graph or a pie chart, or may be displayed as a numerical value.
  • FIG. 31 is a spectrogram showing the extraction results for each sound type.
  • FIG. 32 and FIG. 33 are conceptual diagrams showing examples of audio output for each classified sound type.
  • the spectrogram displayed in the area 155c of the display unit 150 may be displayed for each sound type selected by the user. That is, instead of the spectrogram of the original (acquired original respiratory sound) shown in FIG. 31 (a), the spectrogram of normal respiratory sound shown in FIG. 31 (b) and the spectrogram of analogy sound shown in FIG. 31 (c)
  • the spectrogram of the whistle voice shown in FIG. 31 (d), the spectrogram of the twisting sound shown in FIG. 31 (e), and the spectrogram of the water bubble sound shown in FIG. 31 (f) may be displayed. Also, a plurality of spectrograms for each sound type may be displayed side by side.
  • a graph for each sound type displayed in the region 155d of the display unit 150 may be selected, and only the selected sound type may be output as a volume.
  • a graph for each sound type displayed in the region 155d of the display unit 150 may be selected, and only the selected sound type may be output as a volume.
  • the sound output unit 130 outputs a sound obtained by synthesizing the analogy sound, the whistle sound, the haircut sound, and the water bubble sound.
  • FIG. 34 is a conceptual diagram showing a volume adjustment method for each classified sound type
  • FIG. 35 is a conceptual diagram showing a volume adjustment method for each frequency band.
  • FIG. 36 is a conceptual diagram illustrating an example of image processing executed for each sound type
  • FIG. 37 is a plan view illustrating an example of an image generated by superimposing images processed for each sound type.
  • FIG. 38 is a conceptual diagram showing a display color adjustment method for each classified sound type.
  • the output volume of each sorted sound type may be adjustable for each sound type.
  • ON / OFF for each sound type can be switched by a check box, and the volume for each sound type can be adjusted by a slider.
  • a similar sound and a whistle sound are output, and the whistle sound is output at a volume higher than the similar sound.
  • the output volume for each frequency band may be adjusted.
  • the gain can be adjusted in each frequency band of 125 Hz, 250 Hz, 500 Hz, 1 kHz, and 2 kHz.
  • image processing for example, binarization, edge detection, etc.
  • image processing may be performed on the spectrogram extracted for each sound type. In this way, what is difficult to recognize in the state of being extracted can be displayed in a more visually understandable state.
  • the image processing may be a combination of a plurality of processes. Also, different image processing may be performed depending on the sound type.
  • an image for each sound type that has undergone image processing may be displayed in a superimposed manner.
  • the spectrogram for each sound type can be collectively recognized with one image, so that visual grasp can be suitably performed.
  • the sound type to be displayed can be selected, and only the desired sound type can be appropriately selected and displayed. Can do.
  • the color of the image may be adjustable for each sound type.
  • the RGB values can be adjusted for each sound type by adjusting the sliders corresponding to R (red), G (green), and B (blue). In this way, it is possible to display a plurality of sound types in different colors, and it is possible to realize display in a state that is easier to grasp visually.
  • the respiratory sound analyzing apparatus As described above, according to the respiratory sound analyzing apparatus according to the present embodiment, after the respiratory sounds are sorted, they can be appropriately selected and output. Moreover, since the output mode can be changed for each sound type, the data for each sorted sound type can be suitably used.

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Abstract

The respiratory sound analysis device is provided with classifying means (210-250) for classifying respiratory sounds into normal sounds and abnormal sounds and outputting means (125, 260, 270) for outputting selected respiratory sounds from among the classified respiratory sounds. With said respiratory sound analysis device, it is possible to appropriately analyze respiratory sounds that comprise multiple types of sounds because it is possible to classify the respiratory sounds and output selected respiratory sounds. The respiratory sound analysis device can also be provided with modifying means (125, 260, 270) that are capable of modifying the manner of output for each sound type of the selected respiratory sounds.

Description

呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体Respiratory sound analysis device, respiratory sound analysis method, computer program, and recording medium
　本発明は、複数の音種を含む呼吸音を解析する呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体の技術分野に関する。 The present invention relates to a breathing sound analysis apparatus and a breathing sound analysis method for analyzing a breathing sound including a plurality of sound types, and a computer program and a recording medium.
　この種の装置として、電子聴診器等によって検出される生体の呼吸音について、正常呼吸音と異常呼吸音とを判別するものが知られている。例えば特許文献１では、異常音の分析結果を３次元表示装置で出力するという技術が提案されている。特許文献２では、呼吸音に含まれる異常音としてラ音を検出するという技術が提案されている。特許文献３では、音波形を複数の区分に分割して音種を判別するという技術が提案されている。 As this type of device, there is known a device that discriminates between normal breath sounds and abnormal breath sounds with respect to the breath sounds of a living body detected by an electronic stethoscope or the like. For example, Patent Document 1 proposes a technique of outputting an abnormal sound analysis result with a three-dimensional display device. Patent Document 2 proposes a technique for detecting a rale as an abnormal sound included in a respiratory sound. Japanese Patent Application Laid-Open No. 2004-228688 proposes a technique of dividing a sound waveform into a plurality of sections and determining a sound type.
特開２００２－５３８９２１号公報JP 2002-538921 A 特開２００９－１０６５７４号公報JP 2009-106574 A 特開２０１３－１２３４９４号公報JP 2013-123494 A
　しかしながら、上述した特許文献１から３に記載されているような技術では、呼吸音に含まれる複数の音種を任意に出力することができないという技術的問題点がある。 However, the techniques described in Patent Documents 1 to 3 described above have a technical problem that a plurality of sound types included in the respiratory sound cannot be arbitrarily output.
　本発明が解決しようとする課題には、上記のようなものが一例として挙げられる。本発明は、呼吸音に含まれる複数の音種を分別して出力することが可能な呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体を提供することを課題とする。 Examples of problems to be solved by the present invention include the above. It is an object of the present invention to provide a respiratory sound analysis apparatus, a respiratory sound analysis method, a computer program, and a recording medium that can separately output a plurality of sound types included in a respiratory sound.
　上記課題を解決するための呼吸音解析装置は、呼吸音を正常音及び異常音に分別する分別手段と、前記分別した呼吸音のうち任意の呼吸音を出力する出力手段とを備える。 A breathing sound analyzing apparatus for solving the above-described problems includes a sorting unit that sorts a breathing sound into a normal sound and an abnormal sound, and an output unit that outputs an arbitrary breathing sound among the sorted breathing sounds.
　上記課題を解決するための呼吸音解析方法は、呼吸音を正常音及び異常音に分別する分別工程と、前記分別した呼吸音のうち任意の呼吸音を出力する出力工程とを備える。 The respiratory sound analysis method for solving the above-described problems includes a classification process for classifying respiratory sounds into normal sounds and abnormal sounds, and an output process for outputting an arbitrary respiratory sound among the classified respiratory sounds.
　上記課題を解決するためのコンピュータプログラムは、呼吸音を正常音及び異常音に分別する分別工程と、前記分別した呼吸音のうち任意の呼吸音を出力する出力工程とをコンピュータに実行させる。 A computer program for solving the above-described problems causes a computer to execute a classification process for classifying respiratory sounds into normal sounds and abnormal sounds, and an output process for outputting arbitrary respiratory sounds among the classified respiratory sounds.
　上記課題を解決するための記録媒体は、上述したコンピュータプログラムが記録されている。 The recording medium for solving the above problem is recorded with the computer program described above.
本実施例に係る呼吸音解析装置の全体構成を示すブロック図である。It is a block diagram which shows the whole structure of the respiratory sound analyzer which concerns on a present Example. 本実施例に係る呼吸音解析装置の動作を示すフローチャートである。It is a flowchart which shows operation | movement of the respiratory sound analyzer which concerns on a present Example. 捻髪音を含む呼吸音の周波数解析結果を示すスペクトログラム図である。It is a spectrogram figure which shows the frequency analysis result of the breathing sound containing a hair hair sound. 笛声音を含む呼吸音の周波数解析結果を示すスペクトログラム図である。It is a spectrogram figure which shows the frequency analysis result of the breathing sound containing a whistle voice sound. 捻髪音を含む呼吸音の所定タイミングにおけるスペクトルを示すグラフである。It is a graph which shows the spectrum in the predetermined timing of the breathing sound containing a hair hair sound. 捻髪音を含む呼吸音のスペクトルの近似方法を示す概念図である。It is a conceptual diagram which shows the approximation method of the spectrum of the breathing sound containing a hairpin sound. 笛声音を含む呼吸音の所定タイミングにおけるスペクトルを示すグラフである。It is a graph which shows the spectrum in the predetermined timing of the breathing sound containing a whistle voice sound. 笛声音を含む呼吸音のスペクトルの近似方法を示す概念図である。It is a conceptual diagram which shows the approximation method of the spectrum of the breathing sound containing a whistle voice sound. 周波数解析方法の一例を示すグラフである。It is a graph which shows an example of a frequency analysis method. 周波数解析結果の一例を示す図である。It is a figure which shows an example of a frequency analysis result. スペクトルのピーク検出結果を示す概念図である。It is a conceptual diagram which shows the peak detection result of a spectrum. 正常肺胞呼吸音基底を示すグラフである。It is a graph which shows a normal alveolar respiratory sound base. 捻髪音基底を示すグラフである。It is a graph which shows a twist hair sound base. 連続性ラ音基底を示すグラフである。It is a graph which shows a continuous ra sound base. ホワイトノイズ基底を示すグラフである。It is a graph which shows a white noise base. 周波数シフトされた連続性ラ音基底を示すグラフである。It is a graph which shows the continuous ra sound base shifted in frequency. スペクトルと、基底及び結合係数との関係を示す図である。It is a figure which shows the relationship between a spectrum, a base, and a coupling coefficient. 観測されたスペクトル及び近似に用いられる基底の一例を示す図である。It is a figure which shows an example of the base used for the observed spectrum and approximation. スペクトルを示す各基底及び結合係数を示す図である。It is a figure which shows each base which shows a spectrum, and a coupling coefficient. 第１変形例に係る連続性ラ音の分別方法を示す概念図（その１）である。It is a conceptual diagram (the 1) which shows the classification method of the continuous rarity concerning a 1st modification. 第１変形例に係る連続性ラ音の分別方法を示す概念図（その２）である。It is a conceptual diagram (the 2) which shows the classification method of the continuous rarity concerning a 1st modification. 第２変形例に係る笛声音と類鼾音との分別に用いる閾値を示すグラフである。It is a graph which shows the threshold value used for classification of a whistle voice sound and analogy sound concerning the 2nd modification. 第３変形例に係る笛声音と類鼾音との分別に用いる閾値の初期値を示すグラフである。It is a graph which shows the initial value of the threshold value used for classification of a whistle voice sound and analogy sound concerning a 3rd modification. 第３変形例に係る笛声音と類鼾音との分別に用いる閾値の調整後の値を示すグラフ（その１）である。It is a graph (the 1) which shows the value after the adjustment of the threshold value used for classification | category with a whistle voice sound and analogy sound which concerns on a 3rd modification. 第３変形例に係る笛声音と類鼾音との分別に用いる閾値の調整後の値を示すグラフ（その２）である。It is a graph (the 2) which shows the value after the adjustment of the threshold value used for the classification | category of a whistle voice sound and analogy sound which concerns on a 3rd modification. 笛声音を含む呼吸音のスペクトログラム図である。It is a spectrogram figure of the breathing sound containing a whistle voice sound. 笛声音のピーク周波数及びピーク数を示すグラフである。It is a graph which shows the peak frequency and peak number of a whistle voice sound. 類鼾音を含む呼吸音のスペクトログラム図である。It is a spectrogram figure of the breathing sound containing a similar sound. 類鼾音のピーク周波数及びピーク数を示すグラフである。It is a graph which shows the peak frequency and number of peaks of an analogy sound. 表示部における表示例を示す平面図である。It is a top view which shows the example of a display in a display part. 音種毎の抽出結果を示すスペクトログラム図である。It is a spectrogram figure which shows the extraction result for every sound type. 分別された音種毎の音声出力の一例を示す概念図（その１）である。It is the conceptual diagram which shows an example of the audio | voice output for every classified sound type (the 1). 分別された音種毎の音声出力の一例を示す概念図（その２）である。It is a conceptual diagram (the 2) which shows an example of the audio | voice output for every classified sound type. 分別された音種毎の音量調整方法を示す概念図である。It is a conceptual diagram which shows the volume adjustment method for every classified sound type. 周波数帯域毎の音量調整方法を示す概念図である。It is a conceptual diagram which shows the volume adjustment method for every frequency band. 音種毎に実行される画像処理の一例を示す概念図である。It is a conceptual diagram which shows an example of the image process performed for every sound type. 音種毎に画像処理した画像を重ね合わせて生成した画像の一例を示す平面図である。It is a top view which shows an example of the image produced | generated by superimposing the image processed for every sound type. 分別された音種毎の表示色調整方法を示す概念図である。It is a conceptual diagram which shows the display color adjustment method for every classified sound type.
　＜１＞
　本実施形態に係る呼吸音解析装置は、呼吸音を正常音及び異常音に分別する分別手段と、前記分別した呼吸音のうち任意の呼吸音を出力する出力手段とを備える。 <1>
The respiratory sound analysis apparatus according to the present embodiment includes a classification unit that classifies a respiratory sound into a normal sound and an abnormal sound, and an output unit that outputs an arbitrary respiratory sound among the sorted respiratory sounds.
　本実施形態に係る呼吸音解析装置によれば、その動作時には、先ず呼吸音が正常音と異常音とに分別される。分別手段によって分別される呼吸音は、正常音及び異常音の２音でなくともよく、正常音及び異常音の各々が更に複数の音種に分別されてもよい。例えば、異常音を、笛声音、類鼾音及び捻髪音等に夫々分別してもよい。なお、具体的な分別手法については特に限定されるものではなく、後述する任意の呼吸音の出力が可能となるような状態で分別できればよい。 According to the respiratory sound analyzer according to the present embodiment, during the operation, the respiratory sound is first classified into a normal sound and an abnormal sound. The breathing sound separated by the sorting means may not be the normal sound and the abnormal sound, and each of the normal sound and the abnormal sound may be further classified into a plurality of sound types. For example, the abnormal sound may be classified into a whistle sound, an analogy sound, a haircut sound, and the like. In addition, it does not specifically limit about a specific classification method, What is necessary is just to be able to classify in the state which can output the arbitrary respiratory sounds mentioned later.
　呼吸音が分別されると、分別された呼吸音のうち任意の呼吸音が出力される。即ち、分別された複数の呼吸音が選択的に出力される。これにより、例えばユーザが所望する呼吸音のみを出力することが可能である。より具体的には、例えば呼吸音に含まれる笛声音のみを出力したり、笛声音及び類鼾音のみを出力したりできる。なお、出力態様については特に限定されず、音声又は画像として出力されてもよいし、他の態様で出力されてもよい。 When the breathing sound is separated, any breathing sound among the separated breathing sounds is output. That is, a plurality of sorted respiratory sounds are selectively output. Thereby, for example, it is possible to output only the breathing sound desired by the user. More specifically, for example, it is possible to output only the whistle voice included in the breathing sound, or output only the whistle voice and analogy sound. In addition, it does not specifically limit about an output aspect, You may output as an audio | voice or an image, and you may output in another aspect.
　上述したように呼吸音を分別して出力することで、例えば健康状態の診断等が容易に行える。具体的には、例えば複数の呼吸音が混ざった状態で聞こえるような場合には、熟練した医師でも各音種を区別して聴きとることが難しいが、任意の呼吸音を出力できれば、呼吸音に含まれる音種を容易に判別することが可能となる。このように、呼吸音に含まれる特定の音種だけを聴きとり易くすることができれば、医師の教育や研究においても活用することができる。 As described above, by separating and outputting respiratory sounds, for example, diagnosis of a health condition can be easily performed. Specifically, for example, when a sound is heard in a state where a plurality of breathing sounds are mixed, it is difficult for a skilled doctor to distinguish and listen to each sound type. It becomes possible to easily discriminate the included sound types. As described above, if it is possible to easily listen to only a specific sound type included in the breathing sound, it can be utilized in doctor education and research.
　以上説明したように、本実施形態に係る呼吸音解析装置によれば、呼吸音を分別して任意の呼吸音を出力できるため、複数の音種を含んでいる呼吸音を好適に解析することが可能である。 As described above, according to the respiratory sound analysis apparatus according to the present embodiment, since the respiratory sound can be sorted and any respiratory sound can be output, the respiratory sound including a plurality of sound types can be suitably analyzed. Is possible.
　＜２＞
　本実施形態に係る呼吸音解析装置の一態様では、前記出力手段は、前記分別した呼吸音のうち複数の呼吸音を同時に出力する。 <2>
In one aspect of the respiratory sound analyzing apparatus according to the present embodiment, the output means outputs a plurality of respiratory sounds simultaneously among the sorted respiratory sounds.
　この態様によれば、任意の呼吸音として複数の呼吸音を同時に（言い換えれば、重畳した状態で）出力することができるため、所望の呼吸音を適宜組み合わせて出力させることができる。 According to this aspect, since a plurality of respiratory sounds can be output simultaneously (in other words, in a superimposed state) as arbitrary respiratory sounds, desired respiratory sounds can be combined and output as appropriate.
　＜３＞
　本実施形態に係る呼吸音解析装置の他の態様では、前記出力手段は、前記任意の呼吸音を音声又はスペクトル画像として出力する。 <3>
In another aspect of the respiratory sound analyzing apparatus according to the present embodiment, the output means outputs the arbitrary respiratory sound as a voice or a spectrum image.
　この態様によれば、分別された呼吸音のうち任意の呼吸音は、例えばスピーカーやヘッドフォンを用いて音声として出力される。或いは、任意の呼吸音は、液晶モニタ等のディスプレイを用いてスペクトル画像として出力される。よって、出力された任意の呼吸音を好適に利用できる。 According to this aspect, any respiratory sound among the sorted respiratory sounds is output as sound using, for example, a speaker or headphones. Alternatively, an arbitrary breathing sound is output as a spectrum image using a display such as a liquid crystal monitor. Therefore, any output breathing sound can be suitably used.
　＜４＞
　本実施形態に係る呼吸音解析装置の他の態様では、前記任意の呼吸音の出力状態を音種毎に変更可能な変更手段を更に備える。 <4>
In another aspect of the respiratory sound analyzing apparatus according to the present embodiment, the respiratory sound analyzing apparatus further includes changing means capable of changing the output state of the arbitrary respiratory sound for each sound type.
　この態様によれば、任意の呼吸音の出力状態（例えば、出力音量や画像の表示態様等）を音種毎に変更できるため、出力された任意の呼吸音をより好適に利用できる。例えば、呼吸音に含まれる特定の異常音だけ音量を大きくして出力することで、複数の呼吸音が混ざった状態でも、特定の異常音を聴き易い状態にできる。また、一度聴き易い状態にしてから再び通常の音量に戻しても、以降は聴きとり易い状態になるものと考えられる。よって、経験の浅い医師のトレーニング等にも有効に活用することができる。 According to this aspect, since the output state of any respiratory sound (for example, output volume, image display mode, etc.) can be changed for each sound type, the output arbitrary respiratory sound can be used more suitably. For example, by increasing the volume of a specific abnormal sound included in the respiratory sound and outputting it, the specific abnormal sound can be easily heard even when a plurality of respiratory sounds are mixed. Moreover, even if it is made easy to listen once and then returned to the normal volume again, it is considered that it becomes easy to listen thereafter. Therefore, it can be effectively used for training of inexperienced doctors.
　＜５＞
　上述した変更手段を更に備える態様では、前記変更手段は、前記任意の呼吸音の出力音量を音種毎に変更可能であってもよい。 <5>
In the aspect further including the changing means described above, the changing means may be capable of changing the output volume of the arbitrary respiratory sound for each sound type.
　この場合、出力音量を音種毎に変更できるため、所望の呼吸音のみを聴き易い状態にするなどして、利便性を向上させることができる。 In this case, since the output volume can be changed for each sound type, the convenience can be improved by making it easy to hear only the desired breathing sound.
　＜６＞
　上述した出力音量を音種毎に変更可能な態様では、前記変更手段は、前記任意の呼吸音の出力音量を所定の周波数帯域毎に変更可能であってもよい。 <6>
In the above aspect in which the output volume can be changed for each sound type, the changing means may be able to change the output volume of the arbitrary breathing sound for each predetermined frequency band.
　この場合、音種毎の出力音量の変更をより詳細に行えるため、例えば所定の周波数帯域の出力音量だけを大きくして、所望の呼吸音を更に聴き易い状態にすることができる。なお、所定の周波数帯域は、分別される音種の特性等に応じて設定されればよい。 In this case, since the output volume for each sound type can be changed in more detail, for example, it is possible to increase only the output volume in a predetermined frequency band to make it easier to hear a desired breathing sound. The predetermined frequency band may be set according to the characteristics of the sound types to be sorted.
　＜７＞
　或いは変更手段を更に備える態様では、前記変更手段は、前記任意の呼吸音を示す画像に対して音種毎に所定の画像処理を実行可能であってもよい。 <7>
Alternatively, in an aspect further including a changing unit, the changing unit may be capable of executing predetermined image processing for each sound type on the image indicating the arbitrary breathing sound.
　この場合、任意の呼吸音を示す画像（例えば、一種の呼吸音のみを抽出したスペクトログラム等）について、所定の画像処理を施し、視覚的に認識し易い状態を実現できる。所定の画像処理としては、例えば、色の変更（例えば、ＲＧＢ調整）、二値化、エッジ検出等が挙げられる。 In this case, a predetermined image processing is performed on an image showing an arbitrary breathing sound (for example, a spectrogram obtained by extracting only one kind of breathing sound), and a state where it can be easily recognized visually can be realized. Examples of the predetermined image processing include color change (for example, RGB adjustment), binarization, edge detection, and the like.
　＜８＞
　上述した画像処理を音種毎に実行可能な態様では、前記変更手段は、音種毎に前記所定の画像処理を実行した画像を、複数の音種で重ね合わせて出力可能であってもよい。 <8>
In the aspect in which the above-described image processing can be performed for each sound type, the changing unit may be able to output an image obtained by performing the predetermined image processing for each sound type by superimposing the plurality of sound types. .
　この場合、別々に画像処理が施された音種毎の画像を複数重ね合わせて表示できるため、画像処理によって適切な態様で表示された複数の音種を１つの画像でまとめて認識でき、例えば複数の音種間での比較等を好適に行うことができる。 In this case, since a plurality of images for each sound type separately subjected to image processing can be displayed in a superimposed manner, a plurality of sound types displayed in an appropriate manner by image processing can be recognized together in one image, for example, Comparison between a plurality of sound types can be suitably performed.
　＜９＞
　本実施形態に係る呼吸音解析装置の他の態様では、前記分別手段は、呼吸音のスペクトルの所定の特徴に対応する周波数に関する情報を取得する取得手段と、前記呼吸音を分類する基準となる複数の基準スペクトルを、前記周波数に関する情報に応じてシフトさせ、周波数シフト基準スペクトルを取得するシフト手段と、前記呼吸音と前記周波数シフト基準スペクトルとに基づいて、前記呼吸音に含まれる前記複数の基準スペクトルの割合を出力する割合出力手段とを有する。 <9>
In another aspect of the respiratory sound analysis apparatus according to the present embodiment, the classification unit is an acquisition unit that acquires information about a frequency corresponding to a predetermined characteristic of the respiratory sound spectrum, and is a reference for classifying the respiratory sound. Shift means for shifting a plurality of reference spectra in accordance with information on the frequency to obtain a frequency shift reference spectrum, and the plurality of the plurality of reference spectra included in the respiratory sound based on the respiratory sound and the frequency shift reference spectrum And a ratio output means for outputting a ratio of the reference spectrum.
　この態様によれば、分別手段において、先ず呼吸音のスペクトルの所定の特徴に対応する周波数に関する情報が取得される。なお、ここでの「所定の特徴」とは、生体音のスペクトルに含まれる音種に応じて特定の周波数に発生する特徴を意味しており、例えば周波数解析された信号に現れるピーク等である。更に、「周波数に関する情報」とは、周波数を直接的に示す情報に限定されず、その周波数を間接的に導き出すことができるような情報を含む趣旨である。 According to this aspect, in the classification means, first, information regarding the frequency corresponding to the predetermined characteristic of the spectrum of the respiratory sound is acquired. Here, the “predetermined feature” means a feature that occurs at a specific frequency according to the sound type included in the spectrum of the body sound, and is, for example, a peak that appears in a frequency-analyzed signal. . Furthermore, the “information about the frequency” is not limited to the information that directly indicates the frequency, but includes information that can indirectly derive the frequency.
　周波数に関する情報が取得されると、呼吸音を分類する基準となる複数の基準スペクトルが、周波数に関する情報に応じてシフトされ、周波数シフト基準スペクトルが取得される。なお、ここでの「基準スペクトル」とは、呼吸音に含まれる複数の音種（例えば、正常呼吸音や連続性ラ音、捻髪音等）を分類するために、各音種に応じて予め設定されたスペクトルである。基準スペクトルは、例えば呼吸音から取得された所定の特徴であるピーク位置等に応じて周波数シフトされ、周波数シフト基準スペクトルとされる。 When the information about the frequency is acquired, a plurality of reference spectra serving as a reference for classifying the respiratory sounds are shifted according to the information about the frequency, and the frequency shift reference spectrum is acquired. Note that the “reference spectrum” here refers to each sound type in order to classify a plurality of sound types included in the respiratory sound (for example, normal respiratory sound, continuous rarity sound, haircut sound, etc.). This is a preset spectrum. The reference spectrum is frequency-shifted according to, for example, a peak position, which is a predetermined feature acquired from a respiratory sound, and becomes a frequency-shifted reference spectrum.
　周波数シフト基準スペクトルが取得されると、呼吸音と周波数シフト基準スペクトルとに基づいて、呼吸音に含まれる複数の基準スペクトルの割合が出力される。具体的には、解析対象である呼吸音に、複数の基準スペクトルに対応する音種がどのような割合で含まれているのかが算出され、その結果が出力される。より具体的には、例えば呼吸音のスペクトルに対して、複数の基準スペクトルを基底とする演算が実行されることで、基準スペクトルの割合が結合係数として算出される。 When the frequency shift reference spectrum is acquired, a ratio of a plurality of reference spectra included in the respiratory sound is output based on the respiratory sound and the frequency shift reference spectrum. Specifically, the proportion of sound types corresponding to a plurality of reference spectra is calculated in the respiratory sound to be analyzed, and the result is output. More specifically, the ratio of the reference spectrum is calculated as a coupling coefficient by executing a calculation based on a plurality of reference spectra, for example, for the spectrum of the respiratory sound.
　以上の結果、本実施形態に係る分別手段によれば、複数の音種を含む呼吸音を好適に分別できる。本実施形態では特に、複数の呼吸音が同一の周波数軸上で混じり合っている場合においても、各音種の割合を好適に分別できる。 As a result, according to the classification means according to the present embodiment, respiratory sounds including a plurality of sound types can be suitably classified. Particularly in the present embodiment, even when a plurality of breathing sounds are mixed on the same frequency axis, the ratio of each sound type can be suitably classified.
　＜１０＞
　上述した周波数シフト基準スペクトルを用いる態様では、前記所定の特徴は、極大値であってもよい。 <10>
In the aspect using the frequency shift reference spectrum described above, the predetermined feature may be a local maximum value.
　この場合、例えば呼吸音を示す信号に対して、高速フーリエ変換（ＦＦＴ：Fast Fourier Transform）等による周波数解析が実行され、解析結果の極大値（即ち、ピーク）に対応する周波数に関する情報が取得される。なお、周波数に関する情報は、極大値の位置に対応するものとして取得されるが、極大値の位置と完全に一致する周波数でなくとも、極大値の近傍位置に対応する周波数に関する情報として取得されてもよい。 In this case, for example, frequency analysis by fast Fourier transform (FFT: Fourier 等 Transform) or the like is performed on a signal indicating a respiratory sound, and information on a frequency corresponding to the maximum value (that is, peak) of the analysis result is acquired. The Note that the information about the frequency is acquired as corresponding to the position of the maximum value, but even if the frequency is not completely coincident with the position of the maximum value, it is acquired as information about the frequency corresponding to the position near the maximum value. Also good.
　上述したように、呼吸音のスペクトルの所定の特徴として極大値を利用することで、より容易且つ的確に周波数に関する情報を取得できる。 As described above, the frequency-related information can be acquired more easily and accurately by using the maximum value as the predetermined characteristic of the spectrum of the respiratory sound.
　＜１１＞
　本実施形態に係る呼吸音解析方法は、呼吸音を正常音及び異常音に分別する分別工程と、前記分別した呼吸音のうち任意の呼吸音を出力する出力工程とを備える。 <11>
The respiratory sound analysis method according to the present embodiment includes a classification process for classifying respiratory sounds into normal sounds and abnormal sounds, and an output process for outputting an arbitrary respiratory sound among the classified respiratory sounds.
　本実施形態に係る呼吸音解析方法によれば、上述した本実施形態に係る呼吸音解析装置と同様に、複数の音種を含む呼吸音を好適に解析できる。 According to the respiratory sound analysis method according to the present embodiment, it is possible to suitably analyze a respiratory sound including a plurality of sound types, similarly to the respiratory sound analysis apparatus according to the present embodiment described above.
　なお、本実施形態に係る呼吸音解析方法においても、上述した本実施形態に係る呼吸音解析装置における各種態様と同様の各種態様を採ることが可能である。 In the respiratory sound analysis method according to the present embodiment, various aspects similar to the various aspects of the respiratory sound analysis apparatus according to the present embodiment described above can be employed.
　＜１２＞
　本実施形態に係るコンピュータプログラムは、呼吸音を正常音及び異常音に分別する分別工程と、前記分別した呼吸音のうち任意の呼吸音を出力する出力工程とをコンピュータに実行させる。 <12>
The computer program according to the present embodiment causes the computer to execute a classification process of classifying respiratory sounds into normal sounds and abnormal sounds, and an output process of outputting arbitrary respiratory sounds among the classified respiratory sounds.
　本実施形態に係るコンピュータプログラムによれば、上述した本実施形態に係る呼吸音解析方法と同様の処理をコンピュータに実行させることができるため、複数の音種を含む呼吸音を好適に解析できる。 According to the computer program according to the present embodiment, the computer can execute the same processing as the above-described respiratory sound analysis method according to the present embodiment, so that a respiratory sound including a plurality of sound types can be suitably analyzed.
　なお、本実施形態に係るコンピュータプログラムにおいても、上述した本実施形態に係る呼吸音解析装置における各種態様と同様の各種態様を採ることが可能である。 Note that the computer program according to the present embodiment can also adopt various aspects similar to the various aspects of the respiratory sound analyzer according to the present embodiment described above.
　＜１３＞
　本実施形態に係る記録媒体は、上述したコンピュータプログラムが記録されている。 <13>
The recording medium according to the present embodiment records the above-described computer program.
　本実施形態に係る記録媒体によれば、上述したコンピュータプログラムをコンピュータにより実行させることにより、複数の音種を含む呼吸音を好適に解析することが可能となる。 According to the recording medium according to the present embodiment, it is possible to suitably analyze a respiratory sound including a plurality of sound types by causing the computer program described above to be executed by a computer.
　本実施形態に係る呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体の作用及び他の利得については、以下に示す実施例において、より詳細に説明する。 The respiratory sound analysis apparatus and the respiratory sound analysis method according to the present embodiment, the operation of the computer program and the recording medium, and other gains will be described in more detail in the following examples.
　以下では、図面を参照して呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体の実施例について詳細に説明する。 Hereinafter, embodiments of a respiratory sound analysis device, a respiratory sound analysis method, a computer program, and a recording medium will be described in detail with reference to the drawings.
　＜全体構成＞
　先ず、本実施例に係る呼吸音解析装置の全体構成について、図１を参照して説明する。ここに図１は、本実施例に係る呼吸音解析装置の全体構成を示すブロック図である。 <Overall configuration>
First, the overall configuration of the respiratory sound analyzer according to the present embodiment will be described with reference to FIG. FIG. 1 is a block diagram showing the overall configuration of the respiratory sound analysis apparatus according to this embodiment.
　図１において、本実施例に係る呼吸音解析装置は、主な構成要素として、生体音センサ１１０と、信号記憶部１２０と、信号処理部１２５と、音声出力部１３０と、基底保持部１４０と、表示部１５０と、入力部１６０と、処理部２００とを備えて構成されている。 In FIG. 1, the respiratory sound analysis apparatus according to the present embodiment includes, as main components, a body sound sensor 110, a signal storage unit 120, a signal processing unit 125, a sound output unit 130, and a base holding unit 140. , A display unit 150, an input unit 160, and a processing unit 200.
　生体音センサ１１０は、生体の呼吸音を検出可能に構成されたセンサである。生体音センサ１１０は、例えばＥＣＭ（Electret Condenser Microphone）やピエゾを利用したマイク、振動センサ等で構成されている。 The body sound sensor 110 is a sensor configured to be able to detect a breathing sound of a living body. The biological sound sensor 110 includes, for example, an ECM (Electret Condenser Microphone), a microphone using a piezo, a vibration sensor, and the like.
　信号記憶部１２０は、例えばＲＡＭ（Random Access Memory）等のバッファとして構成されており、生体音センサ１１０で検出された呼吸音を示す信号（以下、適宜「呼吸音信号」と称する）を一時的に記憶する。信号記憶部１２０は、記憶した信号を、音声出力部１３０及び処理部２００に夫々出力可能に構成されている。 The signal storage unit 120 is configured as a buffer such as a RAM (Random Access Memory), for example, and temporarily stores a signal indicating a respiratory sound detected by the biological sound sensor 110 (hereinafter, referred to as a “respiratory sound signal” as appropriate). To remember. The signal storage unit 120 is configured to be able to output the stored signal to the audio output unit 130 and the processing unit 200, respectively.
　信号処理部１２５は、生体音センサ１１０で取得した音を加工して音声出力部１３０に出力する。信号処理部１２５は、例えばイコライザーやフィルターとして機能し、取得した音を人が聴き易い状態に加工する。 The signal processing unit 125 processes the sound acquired by the biological sound sensor 110 and outputs the processed sound to the audio output unit 130. The signal processing unit 125 functions as, for example, an equalizer or a filter, and processes the acquired sound so that it can be easily heard by a person.
　音声出力部１３０は、例えばスピーカーやヘッドフォンとして構成されており、生体音センサ１１０で検出され、信号処理部１２５で加工された呼吸音を出力する。 The audio output unit 130 is configured as a speaker or a headphone, for example, and outputs a respiratory sound detected by the biological sound sensor 110 and processed by the signal processing unit 125.
　基底保持部１４０は、例えばＲＯＭ（Read Only Memory）等として構成されており、呼吸音に含まれ得る所定の音種に対応する基底を記憶している。なお、本実施例に係る基底は、本発明の「基準スペクトル」の一例である。 The base holding unit 140 is configured, for example, as a ROM (Read Only Memory) or the like, and stores a base corresponding to a predetermined sound type that can be included in the respiratory sound. The basis according to the present embodiment is an example of the “reference spectrum” in the present invention.
　表示部１５０は、例えば液晶モニタ等のディスプレイとして構成されており、処理部２００から出力される画像データを表示する。 The display unit 150 is configured as a display such as a liquid crystal monitor, for example, and displays image data output from the processing unit 200.
　入力部１６０は、ユーザによる入力を受け付けるデバイスであり、例えばキーボード、マウス、タッチパネル、各種スイッチ等として構成されている。入力部１６０は、少なくとも出力すべき呼吸音を選択するための入力操作が可能なものとして構成されている。 The input unit 160 is a device that accepts input by the user, and is configured as, for example, a keyboard, a mouse, a touch panel, various switches, and the like. The input unit 160 is configured to be capable of performing an input operation for selecting at least a breathing sound to be output.
　処理部２００は、複数の演算回路やメモリ等を含んで構成されている。処理部２００は、周波数解析部２１０、周波数ピーク検出部２２０、基底集合生成部２３０、結合係数算出部２４０、信号強度算出部２５０、画像生成部２６０、及び呼吸音選択部２７０を備えている。 The processing unit 200 includes a plurality of arithmetic circuits and memories. The processing unit 200 includes a frequency analysis unit 210, a frequency peak detection unit 220, a basis set generation unit 230, a coupling coefficient calculation unit 240, a signal intensity calculation unit 250, an image generation unit 260, and a breathing sound selection unit 270.
　処理部２００の各部の動作については後に詳述する。 The operation of each part of the processing unit 200 will be described in detail later.
　＜動作説明＞
　次に、本実施例に係る呼吸音解析装置の動作について、図２を参照して説明する。ここに図２は、本実施例に係る呼吸音解析装置の動作を示すフローチャートである。ここでは、本実施例に係る呼吸音解析装置が実行する処理の全体的な流れを把握するための簡単な説明を行う。各処理の詳細については、後述する。 <Description of operation>
Next, the operation of the respiratory sound analyzer according to the present embodiment will be described with reference to FIG. FIG. 2 is a flowchart showing the operation of the respiratory sound analysis apparatus according to this embodiment. Here, a simple description for grasping the overall flow of processing executed by the respiratory sound analysis apparatus according to the present embodiment will be given. Details of each process will be described later.
　図２において、本実施例に係る呼吸音解析装置の動作時には、先ず生体音センサ１１０において呼吸音が検出され、処理部２００による呼吸音信号の取得が行われる（ステップＳ１０１）。 In FIG. 2, during the operation of the respiratory sound analyzing apparatus according to the present embodiment, a respiratory sound is first detected by the biological sound sensor 110, and a respiratory sound signal is acquired by the processing unit 200 (step S101).
　呼吸音信号が取得されると、周波数解析部２１０において周波数解析（例えば、高速フーリエ変換）が実行される（ステップＳ１０２）。また、周波数ピーク検出部２２０において、周波数解析結果を用いてピーク（極大値）の検出が実行される。 When the respiratory sound signal is acquired, the frequency analysis unit 210 performs frequency analysis (for example, fast Fourier transform) (step S102). Further, the frequency peak detection unit 220 detects a peak (maximum value) using the frequency analysis result.
　続いて、基底集合生成部２３０において基底集合が生成される（ステップＳ１０３）。具体的には、基底集合生成部２３０は、基底保持部１４０に記憶されている基底を用いて基底集合を生成する。この際、基底集合生成部２３０は、周波数解析結果から得られたピーク位置（即ち、対応する周波数）に基づいて、基底をシフトさせる。 Subsequently, a base set is generated in the base set generation unit 230 (step S103). Specifically, the base set generation unit 230 generates a base set using the base stored in the base holding unit 140. At this time, the basis set generation unit 230 shifts the basis based on the peak position (that is, the corresponding frequency) obtained from the frequency analysis result.
　基底集合が生成されると、結合係数算出部２４０において、周波数解析結果及び基底集合に基づく結合係数の算出が実行される（ステップＳ１０４）。 When the basis set is generated, the coupling coefficient calculation unit 240 calculates the coupling coefficient based on the frequency analysis result and the basis set (step S104).
　結合係数が算出されると、信号強度算出部２５０において、結合係数に応じた信号強度が算出される（ステップＳ１０５）。言い換えれば、呼吸音信号に含まれる各音種の割合が算出される。 When the coupling coefficient is calculated, the signal intensity calculation unit 250 calculates the signal intensity corresponding to the coupling coefficient (step S105). In other words, the ratio of each sound type included in the respiratory sound signal is calculated.
　信号強度が算出されると、画像生成部２６０において、信号強度を示す画像データが生成される。生成された画像データは、表示部１５０において解析結果として表示される（ステップＳ１０６）。 When the signal strength is calculated, the image generation unit 260 generates image data indicating the signal strength. The generated image data is displayed as an analysis result on the display unit 150 (step S106).
　解析結果表示後は、ユーザによって出力すべき音種が入力されると（ステップＳ１０７：ＹＥＳ）、呼吸音選択部２７０により出力すべき呼吸音が選択され、選択された音種が音声出力部１３０又は表示部１５０に出力される（ステップＳ１０８）。 After the analysis result is displayed, when the sound type to be output is input by the user (step S107: YES), the respiratory sound to be output is selected by the respiratory sound selection unit 270, and the selected sound type is the audio output unit 130. Or it outputs to the display part 150 (step S108).
　その後、解析処理を継続するか否かの判定が実行される（ステップＳ１０９）。解析処理を継続すると判定された場合（ステップＳ１０９：ＹＥＳ）、ステップＳ１０１からの処理が再び実行される。解析処理を継続しないと判定された場合（ステップＳ１０９：ＮＯ）、一連の処理は終了する。 Thereafter, it is determined whether or not to continue the analysis process (step S109). If it is determined to continue the analysis process (step S109: YES), the process from step S101 is executed again. If it is determined not to continue the analysis process (step S109: NO), the series of processes ends.
　＜呼吸音信号の具体例＞
　次に、本実施例に係る呼吸音解析装置で解析される呼吸音信号の具体例について、図３及び図４を参照して説明する。ここに図３は、捻髪音を含む呼吸音の周波数解析結果を示すスペクトログラム図であり、図４は、笛声音を含む呼吸音の周波数解析結果を示すスペクトログラム図である。 <Specific example of respiratory sound signal>
Next, a specific example of the respiratory sound signal analyzed by the respiratory sound analyzer according to the present embodiment will be described with reference to FIGS. FIG. 3 is a spectrogram showing the frequency analysis result of the breathing sound including the haircut sound, and FIG. 4 is a spectrogram showing the frequency analysis result of the breathing sound including the whistle sound.
　図３に示す例では、正常呼吸音に対応するスペクトログラムパターンに加えて、異常呼吸音の１つである捻髪音に対応するスペクトログラムパターンが観測されている。捻髪音に対応するスペクトログラムパターンは、図中の拡大部分に示すように、菱形に近い形状である。 In the example shown in FIG. 3, in addition to the spectrogram pattern corresponding to the normal breathing sound, the spectrogram pattern corresponding to the haircut sound that is one of the abnormal breathing sounds is observed. The spectrogram pattern corresponding to the haircut sound has a shape close to a rhombus, as shown in the enlarged portion in the figure.
　図４に示す例では、正常呼吸音に対応するスペクトログラムパターンに加えて、異常呼吸音の１つである笛声音に対応するスペクトログラムパターンが観測されている。笛声音に対応するスペクトログラムパターンは、図中の拡大部分に示すように、白鳥の首のような形状である。 In the example shown in FIG. 4, in addition to the spectrogram pattern corresponding to the normal breathing sound, the spectrogram pattern corresponding to the whistle voice which is one of the abnormal breathing sounds is observed. The spectrogram pattern corresponding to the whistle voice is shaped like a swan's neck as shown in the enlarged portion of the figure.
　このように、異常呼吸音には複数の音種が存在し、その音種によって異なる形状のスペクトログラムパターンとして観測される。ただし、図を見ても分かるように、正常呼吸音及び異常呼吸音は互いに混じり合った状態で検出される。本実施例に係る呼吸音解析装置は、このように混じり合った複数の音種を分離するための解析を実行する。 As described above, there are a plurality of sound types in abnormal breathing sounds, and they are observed as spectrogram patterns having different shapes depending on the sound types. However, as can be seen from the figure, the normal breathing sound and the abnormal breathing sound are detected in a mixed state. The respiratory sound analysis apparatus according to the present embodiment executes an analysis for separating a plurality of mixed sound types in this way.
　＜呼吸音信号の近似方法＞
　次に、本実施例に係る呼吸音解析装置による解析方法について、図５から図８を参照して簡単に説明する。ここに図５は、捻髪音を含む呼吸音の所定タイミングにおけるスペクトルを示すグラフであり、図６は、捻髪音を含む呼吸音のスペクトルの近似方法を示す概念図である。また図７は、笛声音を含む呼吸音の所定タイミングにおけるスペクトルを示すグラフであり、図８は、笛声音を含む呼吸音のスペクトルの近似方法を示す概念図である。 <Approximation method of respiratory sound signal>
Next, an analysis method by the respiratory sound analysis apparatus according to the present embodiment will be briefly described with reference to FIGS. FIG. 5 is a graph showing a spectrum at a predetermined timing of the breathing sound including the haircut sound, and FIG. 6 is a conceptual diagram showing an approximation method of the spectrum of the breathing sound including the haircut sound. FIG. 7 is a graph showing a spectrum of a breathing sound including a whistle sound at a predetermined timing, and FIG. 8 is a conceptual diagram showing an approximation method of a spectrum of a breathing sound including a whistle sound.
　図５において、捻髪音を含む呼吸音信号（図３参照）について、捻髪音に対応するスペクトログラムパターンが強く現れているタイミングでスペクトルを抽出すると、図に示すような結果が得られる。このスペクトルは、正常呼吸音と捻髪音とを含んでいると考えられる。 In FIG. 5, when a spectrum is extracted at a timing at which a spectrogram pattern corresponding to the haircut sound appears with respect to a respiratory sound signal including the haircut sound (see FIG. 3), the result shown in the figure is obtained. This spectrum is considered to include normal breath sounds and haircut sounds.
　図６において、正常呼吸音に対応するスペクトル及び捻髪音に対応するスペクトルは、予め実験等により推定できる。このため、予め推定したパターンを利用すれば、上述したスペクトルについて、正常呼吸音に対応する成分と捻髪音に対応する成分とがどのような割合で含まれているかを知ることができる。 In FIG. 6, the spectrum corresponding to the normal breathing sound and the spectrum corresponding to the haircut sound can be estimated in advance by experiments or the like. For this reason, if a pre-estimated pattern is used, it is possible to know in what ratio the component corresponding to the normal breathing sound and the component corresponding to the haircut sound are included in the above-described spectrum.
　図７において、笛声音を含む呼吸音信号（図４参照）について、笛声音に対応するスペクトログラムパターンが強く現れているタイミングでスペクトルを抽出すると、図に示すような結果が得られる。このスペクトルは、正常呼吸音と笛声音とを含んでいると考えられる。 In FIG. 7, when a spectrum is extracted at a timing at which a spectrogram pattern corresponding to the whistle voice appears strongly with respect to a respiratory sound signal including the whistle voice sound (see FIG. 4), the result shown in the figure is obtained. This spectrum is considered to include normal breath sounds and whistle sounds.
　図８において、上述した正常呼吸音及び捻髪音と同様に、笛声音に対応するスペクトルについても、予め実験等により推定できる。このため、予め推定したパターンを利用すれば、上述したスペクトルについて、正常呼吸音に対応する成分と笛声音に対応する成分とがどのような割合で含まれているかを知ることができる。 In FIG. 8, the spectrum corresponding to the whistle sound can be estimated in advance by experiments or the like in the same manner as the normal breathing sound and the haircut sound described above. For this reason, if a pre-estimated pattern is used, it is possible to know in what ratio the component corresponding to the normal breathing sound and the component corresponding to the whistle sound are included in the above-described spectrum.
　以下では、このような解析を実現するための各処理について、より具体的に説明する。 Hereinafter, each process for realizing such an analysis will be described more specifically.
　＜周波数解析＞
　呼吸音信号の周波数解析及び解析結果におけるピークの検出について、図９から図１１を参照して詳細に説明する。ここに図９は、周波数解析方法の一例を示すグラフであり、図１０は、周波数解析結果の一例を示す図である。また図１１は、スペクトルのピーク検出結果を示す概念図である。 <Frequency analysis>
The frequency analysis of the respiratory sound signal and the detection of the peak in the analysis result will be described in detail with reference to FIGS. FIG. 9 is a graph illustrating an example of the frequency analysis method, and FIG. 10 is a diagram illustrating an example of the frequency analysis result. FIG. 11 is a conceptual diagram showing a spectrum peak detection result.
　図９において、取得された呼吸音信号に対しては、先ず周波数解析が実行される。周波数は、高速フーリエ変換等の既存の技術を利用して行うことができる。本実施例では、周波数毎の振幅値（即ち、振幅スペクトル）を周波数解析結果として用いている。なお、データ取得時のサンプリング周波数、窓サイズ、窓関数（例えば、ハニング窓等）については、適宜決定すればよい。 In FIG. 9, frequency analysis is first performed on the acquired respiratory sound signal. The frequency can be obtained using an existing technique such as fast Fourier transform. In the present embodiment, an amplitude value for each frequency (that is, an amplitude spectrum) is used as a frequency analysis result. In addition, what is necessary is just to determine suitably about the sampling frequency at the time of data acquisition, window size, and a window function (for example, Hanning window etc.).
　図１０に示すように、周波数解析結果は、ｎ個の値で構成されるものとして得られる。なお、「ｎ」は、周波数解析における窓サイズ等によって決まる値である。 As shown in FIG. 10, the frequency analysis result is obtained as consisting of n values. “N” is a value determined by a window size or the like in frequency analysis.
　図１１において、周波数解析によって得られたスペクトルについては、ピークの検出が実行される。図に示す例では、１００Ｈｚ、１３０Ｈｚ、１８０Ｈｚ，及び３２０Ｈｚの位置にピークｐ１～ｐ４が検出されている。なお、ピークの検出処理については、どの周波数にピークが存在するかだけわかればよいため、簡易的な処理でも構わない。ただし、小さなピークでも取りこぼしがないよう、ピーク検出のパラメータ設定されていることが好ましい。 In FIG. 11, peak detection is performed for the spectrum obtained by frequency analysis. In the example shown in the figure, peaks p1 to p4 are detected at positions of 100 Hz, 130 Hz, 180 Hz, and 320 Hz. The peak detection process may be a simple process because it is only necessary to know at which frequency the peak exists. However, it is preferable that the peak detection parameters are set so that even a small peak is not missed.
　本実施例では、極大値を取る点を求め、更にその点の２階微分値の小さいもの（即ち、絶対値が大きいもの）から順に最大Ｎ個（Ｎは所定の値）を検出している。極大値は、差分の符号が正から負に切り替わる点から求められる。２階微分値は差分の差分で近似する。この値が所定の閾値（負の値）より小さいものを、小さいものから順に最大Ｎ個選び、その位置を記憶する。 In the present embodiment, a point having a maximum value is obtained, and N points (N is a predetermined value) are detected in order from the smallest second-order differential value (that is, the absolute value is large). . The maximum value is obtained from the point where the sign of the difference switches from positive to negative. The second derivative is approximated by the difference. N values having a value smaller than a predetermined threshold (negative value) are selected in order from the smallest, and the positions are stored.
　＜基底集合の生成＞
　次に、基底集合の生成について、図１２から図１６を参照して詳細に説明する。ここに図１２は、正常肺胞呼吸音基底を示すグラフである。また図１３は、捻髪音基底を示すグラフであり、図１４は、連続性ラ音基底を示すグラフであり、図１５は、ホワイトノイズ基底を示すグラフである。図１６は、周波数シフトされた連続性ラ音基底を示すグラフである。 <Generation of base set>
Next, generation of a base set will be described in detail with reference to FIGS. FIG. 12 is a graph showing the normal alveolar respiratory sound base. FIG. 13 is a graph showing the haircut sound base, FIG. 14 is a graph showing the continuous ra sound base, and FIG. 15 is a graph showing the white noise base. FIG. 16 is a graph showing frequency-shifted continuous ra sound bases.
　図１２から図１５に示すように、各音種に対応する基底は、特有の形状を有している。なお、各基底は周波数解析結果と同じｎ個の数値（即ち、周波数ごとの振幅値）で構成されている。なお、各基底は、周波数毎の振幅値を示す線と周波数軸とで囲まれた面積が所定の値（例えば１）になるように正規化されている。 As shown in FIGS. 12 to 15, the base corresponding to each sound type has a specific shape. Each base is composed of n numerical values (that is, amplitude values for each frequency) that are the same as the frequency analysis result. Each base is normalized so that an area surrounded by a line indicating the amplitude value for each frequency and the frequency axis becomes a predetermined value (for example, 1).
　ちなみに、ここでは正常肺胞呼吸音基底、捻髪音基底、連続性ラ音基底、ホワイトノイズ基底の４つの基底を示しているが、１つの基底しかない場合でも解析を実行することができる。また、ここで挙げた基底以外の基底を用いることもできる。なお、ここで挙げた呼吸音に対応する基底に代えて、例えば心拍音や腸音に対応する基底を用いれば、心拍音や腸音の解析を実行することが可能となる。 Incidentally, here, four bases of a normal alveolar respiratory sound base, a haircut sound base, a continuous ra sound base, and a white noise base are shown, but the analysis can be executed even when there is only one base. In addition, bases other than those listed here can be used. Note that if a base corresponding to a heartbeat sound or an intestinal sound is used instead of the base corresponding to the respiratory sound mentioned here, for example, an analysis of the heartbeat sound or the bowel sound can be performed.
　図１６において、上述した基底のうち連続性ラ音に対応する基底は、周波数解析の結果から検出されたピーク位置に合わせて周波数シフトされる。ここでは、図１１で示したピークｐ１～ｐ４の各々に合わせて、連続性ラ音基底を周波数シフトさせた例を示している。なお、連続性ラ音に対応する基底以外の基底を周波数シフトさせてもよい。 In FIG. 16, the base corresponding to the continuous rale among the above-mentioned bases is frequency-shifted according to the peak position detected from the result of frequency analysis. Here, an example is shown in which the continuous ra sound base is frequency-shifted in accordance with each of the peaks p1 to p4 shown in FIG. In addition, you may frequency-shift bases other than the base corresponding to a continuous rales.
　以上の結果、基底集合は、正常肺胞呼吸音基底、捻髪音基底、ピーク検出個数分の連続性ラ音基底、及びホワイトノイズ基底の集合として生成される。 As a result of the above, the basis set is generated as a set of normal alveolar respiratory sound bases, hair hair sound bases, continuous ra sound bases corresponding to the number of detected peaks, and white noise bases.
　＜結合係数の算出＞
　次に、結合係数の算出について、図１７から図１９を参照して詳細に説明する。ここに図１７は、スペクトルと、基底及び結合係数との関係を示す図であり、図１８は、観測されたスペクトル及び近似に用いられる基底の一例を示す図である。また図１９は、非負値行列因子分解による近似結果を示す図である。 <Calculation of coupling coefficient>
Next, calculation of the coupling coefficient will be described in detail with reference to FIGS. FIG. 17 is a diagram showing the relationship between the spectrum, the basis, and the coupling coefficient, and FIG. 18 is a diagram showing an example of the observed spectrum and the basis used for approximation. FIG. 19 is a diagram showing an approximation result by non-negative matrix factorization.
　解析対象であるスペクトルｙ、基底ｈ（ｆ）、及び結合係数ｕの関係は、以下の数式（１）で表すことができる。 The relationship between the spectrum y, the basis h (f), and the coupling coefficient u that are the analysis target can be expressed by the following mathematical formula (1).
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
　図１７に示すように、スペクトルｙ及び各基底ｈ（ｆ）は、ｎ個の値を有している。他方、結合係数は、ｍ個の値を有している。なお、「ｍ」は、基底集合に含まれる基底の数である。 As shown in FIG. 17, the spectrum y and each base h (f) have n values. On the other hand, the coupling coefficient has m values. “M” is the number of bases included in the base set.
　本実施例に係る呼吸音解析装置では、非負値行列因子分解を利用して基底集合に含まれる各基底の結合係数を算出する。具体的には、以下の数式（２）で示される最適化基準関数Ｄを最小化するｕ（ただし、ｕの各成分値は非負）を求めればよい。 In the respiratory sound analysis apparatus according to the present embodiment, the coupling coefficient of each base included in the base set is calculated using non-negative matrix factorization. Specifically, u that minimizes the optimization criterion function D expressed by the following formula (2) (however, each component value of u is not negative) may be obtained.
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000002
　なお、一般的な非負値行列因子分解は、基底スペクトルの集合を表す基底行列と、結合係数を表すアクティベーション行列を共に算出する手法であるが、本実施例においては、基底行列を固定して結合係数のみを算出している。 Note that general non-negative matrix factorization is a method for calculating both a base matrix representing a set of base spectra and an activation matrix representing a coupling coefficient. In this embodiment, the base matrix is fixed. Only the coupling coefficient is calculated.
　ちなみに、結合係数を算出するための手段として、非負値行列因子分解以外の近似法を用いてもよい。ただし、この場合においても非負であるという条件が望まれる。以下では、非負の近似法を用いる理由について、具体例を挙げて説明する
　図１８に示すように、観測されたスペクトルを、基底Ａ～Ｄの４つの基底で近似して結合係数を算出する場合を考える。なお、非負であることを条件とした場合の期待する結合係数ｕは、基底Ａに対応するものが１、基底Ｂに対応するものが１、基底Ｃに対応するものが０、基底Ｄに対応するもの０である。即ち、非負であることを条件とした場合、観測されたスペクトルは、基底Ａに１を乗じたものと、基底Ｂに１を乗じたものとを足し合わせたスペクトルとして近似される。 Incidentally, an approximation method other than non-negative matrix factorization may be used as means for calculating the coupling coefficient. However, even in this case, the condition that it is non-negative is desired. In the following, the reason for using the non-negative approximation method will be described with a specific example. As shown in FIG. 18, when the observed spectrum is approximated by four bases A to D, the coupling coefficient is calculated. think of. Note that the expected coupling coefficient u under the condition of being non-negative is 1 for the base A, 1 for the base B, 0 for the base C, and 0 for the base D. It is 0 to do. That is, on the condition that it is non-negative, the observed spectrum is approximated as a spectrum obtained by adding the base A multiplied by 1 and the base B multiplied by 1.
　一方、非負であることを条件としない場合の期待する結合係数ｕは、基底Ａに対応するものが０、基底Ｂに対応するものが０、基底Ｃに対応するものが１、基底Ｄに対応するものが－０．５である。即ち、非負であることを条件としない場合、観測されたスペクトルは、基底Ｃに１を乗じたものと、基底Ｄに－０．５を乗じたものとを足し合わせたスペクトルとして近似される。 On the other hand, the expected coupling coefficient u when the condition is not negative is 0 for the base A, 0 for the base B, 1 for the base C, and 1 for the base D. What to do is -0.5. That is, if the condition is not negative, the observed spectrum is approximated as a spectrum obtained by adding the base C multiplied by 1 and the base D multiplied by −0.5.
　上述した２つの例を比較した場合、非負であることを条件とする場合よりも、非負であることを条件としない場合の方が高い近似精度を得られることがある。しかしながら、ここでの結合係数ｕはスペクトルごとの成分量を表すものであるため、非負の値として得られなければならない。言い換えれば、結合係数ｕが負の値で得られた場合には、成分量としての解釈ができない。これに対し、非負の条件を課して近似を行えば、成分量に対応する結合係数ｕを算出することができる。 When comparing the two examples described above, higher approximation accuracy may be obtained when the non-negative condition is not used than when the non-negative condition is used. However, since the coupling coefficient u here represents the component amount for each spectrum, it must be obtained as a non-negative value. In other words, when the coupling coefficient u is obtained as a negative value, it cannot be interpreted as a component amount. On the other hand, if the approximation is performed under a non-negative condition, the coupling coefficient u corresponding to the component amount can be calculated.
　図１９において、本実施例に係る生体解析装置では、上述したように、正常肺胞呼吸音基底、捻髪音基底、４つの連続性ラ音基底、及びホワイトノイズ基底からなる基底集合を用いて結合係数ｕを算出するため、結合係数ｕは、ｕ１からｕ７の７個の値を有するものとして算出される。 In FIG. 19, in the biological analysis apparatus according to the present embodiment, as described above, a basis set including a normal alveolar respiratory sound base, a hair hair sound base, four continuous rar sound bases, and a white noise base is used. In order to calculate the coupling coefficient u, the coupling coefficient u is calculated as having seven values from u 1 to u 7 .
　ここで、正常肺胞呼吸音基底に対応する結合係数ｕ１は、呼吸音に対する正常肺胞呼吸音の割合を示す値であると言える。同様に、捻髪音基底に対応する結合係数ｕ２、ホワイトノイズ基底に対応する結合係数ｕ３、１００Ｈｚにシフトした連続性ラ音基底に対応する結合係数ｕ４、１３０Ｈｚにシフトした連続性ラ音基底に対応する結合係数ｕ５、１８０Ｈｚにシフトした連続性ラ音基底に対応する結合係数ｕ６、及び３２０Ｈｚにシフトした連続性ラ音基底に対応する結合係数ｕ７の各々についても、呼吸音に対する各音種の割合を示す値であると言える。従って、結合係数ｕから各音種の信号強度を算出することができる。 Here, it can be said that the coupling coefficient u 1 corresponding to the normal alveolar respiratory sound base is a value indicating the ratio of the normal alveolar respiratory sound to the respiratory sound. Similarly, the coupling coefficient u 2 corresponding to the hair hair base, the coupling coefficient u 3 corresponding to the white noise base, the continuous coefficient shifted to 100 Hz, the coupling coefficient u 4 corresponding to the sound base, and the continuous coefficient shifted to 130 Hz. For each of the coupling coefficient u 5 corresponding to the sound base, the coupling coefficient u 6 corresponding to the continuous ra sound base shifted to 180 Hz, and the coupling coefficient u 7 corresponding to the continuous ra sound base shifted to 320 Hz, breathing is also performed. It can be said that the value indicates the ratio of each sound type to the sound. Therefore, the signal intensity of each sound type can be calculated from the coupling coefficient u.
　以上のように、本実施例では、各音種に対応する複数の基底を利用して呼吸音に含まれる複数の音種を分別する。ただし、上述した分別方法はあくまで一例であり、他の分別方法を用いて複数の音種を分別しても構わない。 As described above, in this embodiment, a plurality of sound types included in the respiratory sound are classified using a plurality of bases corresponding to each sound type. However, the classification method described above is merely an example, and a plurality of sound types may be classified using other classification methods.
　＜分別方法の変形例＞
　以下では、既に説明した複数の基底を利用する分別方法以外の分別方法について、いくつか例を挙げて説明する。 <Modification of sorting method>
Hereinafter, some examples of the sorting method other than the sorting method using the plurality of bases already described will be described.
　＜第１変形例＞
　先ず、第１変形例に係る分別方法について、図２０及び図２１を参照して説明する。ここに図２０及び図２１は夫々、第１変形例に係る連続性ラ音の分別方法を示す概念図である。 <First Modification>
First, the classification method according to the first modification will be described with reference to FIGS. FIGS. 20 and 21 are conceptual diagrams showing a method for separating continuous rales according to the first modification.
　第１変形例に係る分別方法では、呼吸音を連続性ラ音とそれ以外の音に分別する。具体的には、呼吸音信号の周波数解析結果から検出されるピーク周波数が、所定の範囲内で変動している場合に連続性ラ音であると判定する。 In the classification method according to the first modification, the breathing sound is classified into a continuous ra sound and other sounds. Specifically, when the peak frequency detected from the frequency analysis result of the respiratory sound signal fluctuates within a predetermined range, it is determined that the sound is a continuous rale.
　図２０に示すように、連続性ラ音である笛声音や類鼾音は、時間軸上で連続して検出されるピークの位置が所定の範囲内に収まるように変動する。言い換えれば、ピーク周波数が時間的に連続性を有するように変化する。よって、連続するピーク位置が所定の範囲内にある場合には、その音が連続性ラ音であると判別できる。 As shown in FIG. 20, the whistle sound and the like sound that are continuous rales fluctuate so that the peak positions continuously detected on the time axis fall within a predetermined range. In other words, the peak frequency changes so as to have continuity in time. Therefore, when the continuous peak position is within the predetermined range, it can be determined that the sound is a continuous ra-tone.
　他方、図２１に示すように、連続性ラ音以外の音は、時間軸上で連続して検出されるピークの位置が所定の範囲内に収まらないように変動する。言い換えれば、ピーク周波数が時間的な連続性を有さず離散的に変化する。よって、連続するピーク位置が所定の範囲内でない場合には、その音が連続性ラ音でないと判別できる。 On the other hand, as shown in FIG. 21, the sounds other than the continuous rarity fluctuate so that the peak positions continuously detected on the time axis do not fall within a predetermined range. In other words, the peak frequency does not have temporal continuity and changes discretely. Therefore, when the continuous peak position is not within the predetermined range, it can be determined that the sound is not a continuous rarity.
　なお、連続性ラ音の判定には、複数回の判定結果を用いることもできる。具体的には、時間軸上で連続して検出されるピークの位置が所定の範囲内に収まるように変動している回数が所定回数以上継続した場合に、その音が連続性ラ音であると判定するようにしてもよい。 In addition, the determination result of multiple times can also be used for determination of continuous rales. Specifically, when the number of times the peak position continuously detected on the time axis has fluctuated so as to be within a predetermined range has continued for a predetermined number of times, the sound is a continuous rar sound. May be determined.
　＜第２変形例＞
　次に、第２変形例に係る分別方法について、図２２を参照して説明する。ここに図２２は、第２変形例に係る笛声音と類鼾音との分別に用いる閾値を示すグラフである。 <Second Modification>
Next, the classification method according to the second modification will be described with reference to FIG. FIG. 22 is a graph showing threshold values used for classification of whistle sounds and similar sounds according to the second modification.
　第２変形例に係る分別方法では、連続性ラ音を笛声音と類鼾音とに分別する。ここで、笛声音は高音性連続性ラ音、類鼾音は低音性連続性ラ音と呼ばれるように、笛声音と類鼾音とは音の高さ（即ち、周波数）で判別することが可能である。しかしながら、笛声音及び類鼾音は、ピーク周波数が時間的に変化する。このため、ピーク周波数に対する単一の閾値（即ち、値が変動しない一つの閾値）を利用して笛声音及び類鼾音を判定しようとすると、時間の経過により、判定結果が変化してしまうことがある。例えば、ピーク周波数が判定閾値を跨ぐように変化してしまうと、それまでは正確に判定されていたものが、誤った音種として判定されることになってしまう。このため第２変形例では、ピーク周波数に応じて判定閾値を変動させる。 In the classification method according to the second modification, the continuous rar sound is classified into a whistle sound and a similar sound. Here, whistle voice sounds and analogy sounds can be distinguished by their pitch (ie, frequency), as whistle voice sounds are called high-pitched continuous rales and analogy sounds are called low-pitched continuous rales. Is possible. However, the peak frequency of the whistle sound and the like sound changes with time. For this reason, when trying to determine a whistle voice and similar sounds using a single threshold for the peak frequency (that is, one threshold whose value does not vary), the determination result changes over time. There is. For example, if the peak frequency changes so as to cross the determination threshold, what has been accurately determined until then will be determined as an incorrect sound type. For this reason, in the second modification, the determination threshold value is varied according to the peak frequency.
　図２２に示すように、第２変形例に係る分別方法では、笛声音と判定する割合及び類鼾音と判定する割合がピーク周波数に応じてなめらかに変化するように閾値が変動する。例えば、ピーク周波数が２００Ｈｚの場合には、笛声音が７％含まれ、類鼾音が９３％含まれると判定する。ピーク周波数が２５０Ｈｚの場合には、笛声音が５０％含まれ、類鼾音が５０％含まれると判定する。ピーク周波数が２８０Ｈｚの場合には、笛声音が７８％含まれ、類鼾音が２２％含まれると判定する。なお、ここでの具体的な数値はあくまで一例であり、異なる値を設定してもよい。また、測定対象である生体の性別、年齢、身長、体重等によって異なる変動特性を有するようにしてもよい。 As shown in FIG. 22, in the classification method according to the second modified example, the threshold value fluctuates so that the ratio for determining the whistle sound and the ratio for determining the analog sound smoothly change according to the peak frequency. For example, when the peak frequency is 200 Hz, it is determined that 7% of whistle sounds are included and 93% of similar sounds are included. When the peak frequency is 250 Hz, it is determined that 50% of whistle sounds are included and 50% of similar sounds are included. When the peak frequency is 280 Hz, it is determined that 78% of whistle sounds are included and 22% of similar sounds are included. The specific numerical values here are merely examples, and different values may be set. Moreover, you may make it have a variation characteristic which changes with sex, age, height, weight, etc. of the biological body which is a measuring object.
　上述した変動する閾値を利用することで、ピーク周波数の変動に起因する誤判定を好適に防止することができる。即ち、第２変形例に係る分別方法では、笛声音及び類鼾音を判定するための閾値がピーク周波数に応じて適切な値にとなるよう変動するため、例えば変動しない単一の閾値を用いる場合と比較して、より正確な分別が行える。 誤 By using the above-described changing threshold, it is possible to suitably prevent erroneous determination due to peak frequency fluctuation. That is, in the classification method according to the second modified example, the threshold value for determining the whistle voice sound and the analogy sound changes so as to become an appropriate value according to the peak frequency, and thus, for example, a single threshold value that does not change is used. Compared to the case, more accurate separation can be performed.
　＜第３変形例＞
　次に、第３変形例に係る分別方法について、図２３から図２５を参照して説明する。ここに図２３は、第３変形例に係る笛声音と類鼾音との分別に用いる閾値の初期値を示すグラフである。また図２４及び図２５は夫々、第３変形例に係る笛声音と類鼾音との分別に用いる閾値の調整後の値を示すグラフである。 <Third Modification>
Next, a sorting method according to the third modification will be described with reference to FIGS. FIG. 23 is a graph showing the initial value of the threshold value used for classification of the whistle sound and the analogy sound according to the third modification. FIG. 24 and FIG. 25 are graphs showing the adjusted values of the threshold values used for the distinction between the whistle vocal sound and the analogy sound according to the third modification.
　第３変形例に係る分別方法も、既に説明した第２変形例と同様に、連続性ラ音を笛声音と類鼾音とに分別する方法である。また、周波数解析結果から得られたピーク周波数に対する閾値を用いて判定する点についても、第２変形例と同様である。 The classification method according to the third modified example is also a method of separating continuous rales into whistle sounds and similar sounds as in the second modified example already described. Moreover, it is the same as that of the 2nd modification also about the point determined using the threshold value with respect to the peak frequency obtained from the frequency analysis result.
　図２３に示すように、第３変形例に係る分別方法では、閾値である２５０Ｈｚを境にして判定結果が変化するものとして設定されている。具体的には、ピーク周波数が２５０Ｈｚ以上である場合には、連続性ラ音は笛声音成分を１００％含んでおり、類鼾音は含んでいないと判定される。一方、ピーク周波数が２５０Ｈｚ未満である場合には、連続性ラ音は類鼾音成分を１００％含んでおり、笛声音は含んでいないと判定される。 As shown in FIG. 23, in the classification method according to the third modified example, the determination result is set to change at a threshold of 250 Hz. Specifically, when the peak frequency is 250 Hz or more, it is determined that the continuous rale includes 100% of the whistle sound component and does not include the analogy sound. On the other hand, when the peak frequency is less than 250 Hz, it is determined that the continuous rale includes 100% of the similar sound component and does not include the whistle sound.
　図２４に示すように、第３変形例に係る分別方法では、直前の判定において笛声音成分を１００％含むものであると判定された場合、閾値が２５０Ｈｚから２２０Ｈｚへと低くされる。よって、笛声音成分を１００％含むものとして判定され易くなる。具体的には、ピーク周波数が２３０Ｈｚの場合を考えると、初期の閾値（図２３参照）によれば類鼾音と判定されることになるが、調整後の閾値（図２４参照）によれば笛声音と判定される。 As shown in FIG. 24, in the classification method according to the third modification, the threshold value is lowered from 250 Hz to 220 Hz when it is determined that the whistle voice component is 100% in the immediately preceding determination. Therefore, it is easy to determine that the whistle voice component is 100%. Specifically, considering the case where the peak frequency is 230 Hz, according to the initial threshold value (see FIG. 23), it is determined as analogy, but according to the adjusted threshold value (see FIG. 24). Judged as a whistle sound.
　図２５に示すように、第３変形例に係る分別方法では、直前の判定において類鼾音成分を１００％含むものであると判定された場合、閾値が２５０Ｈｚから２８０Ｈｚへと高くされる。よって、類鼾音成分を１００％含むものとして判定され易くなる。具体的には、ピーク周波数が２７０Ｈｚの場合を考えると、初期の閾値（図２３参照）によれば笛声音と判定されることになるが、調整後の閾値（図２５参照）によれば類鼾音と判定される。 As shown in FIG. 25, in the classification method according to the third modification, the threshold value is increased from 250 Hz to 280 Hz when it is determined in the previous determination that the analog sound component is 100%. Therefore, it is easy to determine that the analog sound component is 100%. Specifically, considering the case where the peak frequency is 270 Hz, the whistle sound is determined according to the initial threshold value (see FIG. 23), but according to the adjusted threshold value (see FIG. 25). Judged as roaring.
　上述したように閾値を調整すれば、ピーク周波数の変動に起因する誤判定を好適に防止することができる。即ち、第３変形例に係る分別方法では、笛声音及び類鼾音を判定するための閾値が過去の判定結果に基づいて適切なものへと調整されるため、例えば調整されない単一の閾値を用いる場合と比較して、より正確な判定が行える。 If the threshold value is adjusted as described above, erroneous determination due to fluctuations in peak frequency can be suitably prevented. That is, in the classification method according to the third modified example, the threshold value for determining the whistle sound and the analogy sound is adjusted to an appropriate value based on the past determination result. Compared with the case of using, more accurate determination can be performed.
　なお、閾値の調整は、直前の判定結果だけによらず、複数回の過去の判定結果に基づいて行われてもよい。また、複数回の過去の判定結果を利用する場合は、各判定結果に対して重み付けを行ってもよい。例えば、過去の判定結果であるほど影響が小さくなるように重み付けをおこなってもよい。また、調整する閾値の初期値として、第２変形例のなめらかな閾値を用いてもよい（図２２参照）。 It should be noted that the adjustment of the threshold value may be performed based on a plurality of past determination results, not just the previous determination result. Further, when a plurality of past determination results are used, each determination result may be weighted. For example, weighting may be performed so that the influence becomes smaller as the past determination results. In addition, the smooth threshold value of the second modification may be used as the initial value of the threshold value to be adjusted (see FIG. 22).
　＜第４変形例＞
　次に、第３変形例に係る分別方法について、図２６から図２６を参照して説明する。ここに図２６は、笛声音を含む呼吸音のスペクトログラム図であり、図２７は、笛声音のピーク周波数及びピーク数を示すグラフである。また図２８は、類鼾音を含む呼吸音のスペクトログラム図であり、図２９は、類鼾音のピーク周波数及びピーク数を示すグラフである。 <Fourth Modification>
Next, a sorting method according to the third modification will be described with reference to FIGS. FIG. 26 is a spectrogram diagram of a breathing sound including a whistle voice, and FIG. 27 is a graph showing the peak frequency and the number of peaks of the whistle sound. FIG. 28 is a spectrogram diagram of a respiratory sound including a similar sound, and FIG. 29 is a graph showing the peak frequency and the number of peaks of the similar sound.
　第４変形例に係る分別方法も、既に説明した第２及び第３変形例と同様に、連続性ラ音を笛声音と類鼾音とに分別する方法である。 The separation method according to the fourth modification is also a method of separating continuous rales into whistle sounds and similar sounds, as in the second and third modifications already described.
　図２６において、笛声音を含む呼吸音は、所定のピークを有するスペクトラム波形として検出される。ここからピーク周波数Ｆ及びピーク数Ｎを検出するには、先ずスペクトラム波形の単一時間（即ち、図中の白枠で囲った領域）に対応する周波数－振幅グラフを作成する。 In FIG. 26, a breathing sound including a whistle voice sound is detected as a spectrum waveform having a predetermined peak. In order to detect the peak frequency F and the peak number N from this, first, a frequency-amplitude graph corresponding to a single time of the spectrum waveform (that is, a region surrounded by a white frame in the figure) is created.
　図２７に示すグラフから、笛声音のピーク周波数Ｆ１及びピーク数Ｎ１が検出できる。なお、笛声音のピーク周波数の分布は、１８０～９００Ｈｚ程度であることが分かっている。また、図を見ても分かるように、笛声音のピーク数Ｎ１は１個である。 From the graph shown in FIG. 27, the peak frequency F1 and the peak number N1 of the whistle voice can be detected. It is known that the distribution of the peak frequency of the whistle sound is about 180 to 900 Hz. As can be seen from the figure, the peak number N1 of the whistle voice sound is one.
　図２８において、類鼾音を含む呼吸音は、笛声音とは異なる所定のピークを有するスペクトラム波形として検出される。ここからピーク周波数Ｆ及びピーク数Ｎを検出するには、同様にスペクトラム波形の単一時間に対応する周波数－振幅グラフを作成する。 28, the breathing sound including the analogy sound is detected as a spectrum waveform having a predetermined peak different from the whistle sound. In order to detect the peak frequency F and the peak number N from here, a frequency-amplitude graph corresponding to a single time of the spectrum waveform is similarly created.
　図２９に示すグラフから、類鼾音のピーク周波数Ｆ２及びピーク数Ｎ２が検出できる。なお、類鼾音のピーク周波数の分布は、１００～２６０Ｈｚ程度であることが分かっている。即ち、類鼾音のピーク周波数Ｆ２は、笛声音のピーク周波数Ｆ１よりも低い領域に分布していることになる。また、図を見ても分かるように、類鼾音のピーク数Ｎ２は例えば、３個である。即ち、類鼾音のピーク数Ｎ２は、笛声音のピーク数Ｎ１のように１つでなく、複数である。 From the graph shown in FIG. 29, the peak frequency F2 and the number of peaks N2 of the analogy sound can be detected. It is known that the distribution of the peak frequency of analogy is about 100 to 260 Hz. That is, the peak frequency F2 of the analogy sound is distributed in a region lower than the peak frequency F1 of the whistle sound. Further, as can be seen from the figure, the number N2 of analogy sounds is, for example, three. That is, the peak number N2 of analogy sounds is not one, but a plurality, like the peak number N1 of whistle sounds.
　第４変形例に係る分別方法では、上述した笛声音及び類鼾音の特性の違いを利用して判定が行われる。具体的には、ピーク周波数Ｆ及びピーク数Ｎの各々に基づいて、笛声音と類鼾音とが分別される。このようにすれば、例えばピーク周波数Ｆだけを利用して笛声音と類鼾音とを分別する場合と比べて、より正確な分別が行える。 In the classification method according to the fourth modification, the determination is performed using the difference in the characteristics of the above-described whistle sound and analogy sound. Specifically, based on each of the peak frequency F and the peak number N, the whistle sound and the analogy sound are separated. In this way, for example, more accurate classification can be performed as compared with the case where only the peak frequency F is used to separate the whistle sound and the analogy sound.
　＜解析結果の表示＞
　次に、解析結果の表示について、図３０を参照して詳細に説明する。ここに図３０は、表示部における表示例を示す平面図である。 <Display of analysis results>
Next, display of analysis results will be described in detail with reference to FIG. FIG. 30 is a plan view showing a display example on the display unit.
　図３０に示すように、表示部１５０の表示領域１５５には、解析結果が複数の画像として表示される。具体的には、領域１５５ａには、取得された呼吸音の波形が表示されている。領域１５５ｂには、取得された呼吸音のスペクトルが表示されている。領域１５５ｃには、取得された呼吸音のスペクトログラムが表示されている。領域１５５ｄには、分別された各音種（ここでは、正常呼吸音、類鼾音、笛声音、捻髪音、水泡音の５音種）の成分量の時系列変化を表すグラフが表示されている。領域１５５ｅには、分別された各音種の割合がレーダーチャートとして表示されている。 30, the analysis results are displayed as a plurality of images in the display area 155 of the display unit 150. Specifically, the waveform of the acquired respiratory sound is displayed in the area 155a. In the region 155b, the spectrum of the acquired respiratory sound is displayed. In the region 155c, a spectrogram of the acquired respiratory sound is displayed. In the region 155d, a graph representing the time series change of the component amount of each classified sound type (here, five sound types of normal breathing sound, analogy sound, whistle sound, haircut sound, and water bubble sound) is displayed. ing. In the area 155e, the ratio of each classified sound type is displayed as a radar chart.
　なお、このような解析結果の表示態様はあくまで一例であり、他の表示態様で解析結果が表示されてもよい。例えば、分別された各音種の割合は、棒グラフや円グラフとして表示されてもよいし、数値化して表示されてもよい。 Note that such a display form of the analysis result is merely an example, and the analysis result may be displayed in another display form. For example, the ratio of each classified sound type may be displayed as a bar graph or a pie chart, or may be displayed as a numerical value.
　＜音種の選択及び出力＞
　次に、ユーザによる音種の選択、及び選択された音種毎の出力について、図３１から図３３を参照して説明する。ここに図３１は、音種毎の抽出結果を示すスペクトログラム図である。また図３２及び図３３は夫々、分別された音種毎の音声出力の一例を示す概念図である。 <Selection and output of sound types>
Next, the selection of the sound type by the user and the output for each selected sound type will be described with reference to FIGS. FIG. 31 is a spectrogram showing the extraction results for each sound type. FIG. 32 and FIG. 33 are conceptual diagrams showing examples of audio output for each classified sound type.
　図３１に示すように、表示部１５０の領域１５５ｃに表示されるスペクトログラムは、ユーザによって選択された音種毎に表示してもよい。即ち、図３１（ａ）に示すオリジナル（取得された元の呼吸音）のスペクトログラムに代えて、図３１（ｂ）に示す正常呼吸音のスペクトログラム、図３１（ｃ）に示す類鼾音のスペクトログラム、図３１（ｄ）に示す笛声音のスペクトログラム、図３１（ｅ）に示す捻髪音のスペクトログラム、及び図３１（ｆ）に示す水泡音のスペクトログラムを表示するようにしてもよい。また、これらの音種毎のスペクトログラムを複数並べて表示するようにしてもよい。 As shown in FIG. 31, the spectrogram displayed in the area 155c of the display unit 150 may be displayed for each sound type selected by the user. That is, instead of the spectrogram of the original (acquired original respiratory sound) shown in FIG. 31 (a), the spectrogram of normal respiratory sound shown in FIG. 31 (b) and the spectrogram of analogy sound shown in FIG. 31 (c) The spectrogram of the whistle voice shown in FIG. 31 (d), the spectrogram of the twisting sound shown in FIG. 31 (e), and the spectrogram of the water bubble sound shown in FIG. 31 (f) may be displayed. Also, a plurality of spectrograms for each sound type may be displayed side by side.
　図３２及び図３３に示すように、表示部１５０の領域１５５ｄに表示される音種毎のグラフを選択して、選択された音種のみを音量出力するようにしてもよい。例えば図３２の例では、正常呼吸音だけが選択されており、その他の類鼾音、笛声音、捻髪音及び水泡音はいずれも選択されていない。このため、音声出力部１３０からは、正常呼吸音のみが音声出力される。また図３３の例では、正常呼吸音だけが選択されておらず、その他の類鼾音、笛声音、捻髪音及び水泡音がいずれも選択されている。このため、音声出力部１３０からは、類鼾音、笛声音、捻髪音及び水泡音を合成した音声が出力される。 32 and 33, a graph for each sound type displayed in the region 155d of the display unit 150 may be selected, and only the selected sound type may be output as a volume. For example, in the example of FIG. 32, only the normal breathing sound is selected, and other analogy sounds, whistle sounds, haircut sounds, and water bubble sounds are not selected. For this reason, only normal breathing sounds are output from the audio output unit 130. In the example of FIG. 33, only normal breathing sound is not selected, and other analogy sounds, whistle voice sounds, haircut sounds, and water bubble sounds are all selected. For this reason, the sound output unit 130 outputs a sound obtained by synthesizing the analogy sound, the whistle sound, the haircut sound, and the water bubble sound.
　＜出力態様の変更＞
　次に、音種毎の出力態様の変更方法について、図３４から図３８を参照して具体的に説明する。ここに図３４は、分別された音種毎の音量調整方法を示す概念図であり、図３５は、周波数帯域毎の音量調整方法を示す概念図である。また図３６は、音種毎に実行される画像処理の一例を示す概念図であり、図３７は、音種毎に画像処理した画像を重ね合わせて生成した画像の一例を示す平面図である。図３８は、分別された音種毎の表示色調整方法を示す概念図である。 <Change of output mode>
Next, a method for changing the output mode for each sound type will be specifically described with reference to FIGS. FIG. 34 is a conceptual diagram showing a volume adjustment method for each classified sound type, and FIG. 35 is a conceptual diagram showing a volume adjustment method for each frequency band. FIG. 36 is a conceptual diagram illustrating an example of image processing executed for each sound type, and FIG. 37 is a plan view illustrating an example of an image generated by superimposing images processed for each sound type. . FIG. 38 is a conceptual diagram showing a display color adjustment method for each classified sound type.
　図３４に示すように、分別された各音種の出力音量を音種毎に調整可能としてもよい。図に示す操作画面では、音種毎のＯＮ／ＯＦＦがチェックボックスにより切替え可能とされており、音種毎の音量がスライダーによって調整可能とされている。図に示す例では、類鼾音及び笛声音が夫々出力されており、笛声音が類鼾音より大きい音量で出力されている。 As shown in FIG. 34, the output volume of each sorted sound type may be adjustable for each sound type. In the operation screen shown in the figure, ON / OFF for each sound type can be switched by a check box, and the volume for each sound type can be adjusted by a slider. In the example shown in the figure, a similar sound and a whistle sound are output, and the whistle sound is output at a volume higher than the similar sound.
　図３５に示すように、音種毎の出力音量の調整に加えて、周波数帯域毎の出力音量の調整が可能とされてもよい。図に示す例では、１２５Ｈｚ、２５０Ｈｚ、５００Ｈｚ、１ｋＨｚ、２ｋＨｚの各周波数帯域でゲインが調整可能とされている。 As shown in FIG. 35, in addition to adjusting the output volume for each sound type, the output volume for each frequency band may be adjusted. In the example shown in the figure, the gain can be adjusted in each frequency band of 125 Hz, 250 Hz, 500 Hz, 1 kHz, and 2 kHz.
　図３６に示すように、音種毎に抽出されたスペクトログラムに対して、画像処理（例えば、二値化やエッジ検出等）を実行するようにしてもよい。このようにすれば、抽出しただけの状態では認識しにくかったものを、より視覚的に分かりやすい状態で表示させることができる。なお、画像処理は複数の処理を組み合わせたものであってもよい。また、音種によって異なる画像処理を施すようにしても構わない。 As shown in FIG. 36, image processing (for example, binarization, edge detection, etc.) may be performed on the spectrogram extracted for each sound type. In this way, what is difficult to recognize in the state of being extracted can be displayed in a more visually understandable state. Note that the image processing may be a combination of a plurality of processes. Also, different image processing may be performed depending on the sound type.
　図３７に示すように、画像処理が施された音種毎の画像（図３６参照）を、重ね合わせて表示するようにしてもよい。このようにすれば、音種毎のスペクトログラムを１つの画像でまとめて認識できるため、視覚的な把握が好適に行える。なお、ここでは正常呼吸音及び笛声音の画像を重ねて表示した例を示したが、重ねて表示する音種は選択可能とされており、所望の音種のみを適宜選択して表示させることができる。 As shown in FIG. 37, an image for each sound type that has undergone image processing (see FIG. 36) may be displayed in a superimposed manner. In this way, the spectrogram for each sound type can be collectively recognized with one image, so that visual grasp can be suitably performed. In addition, although the example which displayed the image of normal breath sound and the whistle voice sound superimposed is shown here, the sound type to be displayed can be selected, and only the desired sound type can be appropriately selected and displayed. Can do.
　図３８に示すように、音種毎に画像の色を調整可能としてもよい。図に示す例では、Ｒ（赤）、Ｇ（緑）、Ｂ（青）に対応するスライダーを夫々調整することで、音種毎にＲＧＢ値を調整することが可能である。このようにすれば、複数の音種を互いに異なる色で表示させることが可能となり、より視覚的に把握し易い状態での表示が実現できる。 38, the color of the image may be adjustable for each sound type. In the example shown in the figure, the RGB values can be adjusted for each sound type by adjusting the sliders corresponding to R (red), G (green), and B (blue). In this way, it is possible to display a plurality of sound types in different colors, and it is possible to realize display in a state that is easier to grasp visually.
　以上説明したように、本実施例に係る呼吸音解析装置によれば、呼吸音を分別した後、適宜選択して出力することができる。また、出力態様を音種毎に変更することができるため、分別した音種毎のデータを好適に利用することができる。 As described above, according to the respiratory sound analyzing apparatus according to the present embodiment, after the respiratory sounds are sorted, they can be appropriately selected and output. Moreover, since the output mode can be changed for each sound type, the data for each sorted sound type can be suitably used.
　本発明は、上述した実施形態に限られるものではなく、特許請求の範囲及び明細書全体から読み取れる発明の要旨或いは思想に反しない範囲で適宜変更可能であり、そのような変更を伴う呼吸音解析装置及び呼吸音解析方法、並びにコンピュータプログラム及び記録媒体もまた本発明の技術的範囲に含まれるものである。 The present invention is not limited to the above-described embodiment, and can be appropriately changed without departing from the spirit or idea of the invention that can be read from the claims and the entire specification, and respiratory sound analysis accompanying such changes The apparatus, the respiratory sound analysis method, the computer program, and the recording medium are also included in the technical scope of the present invention.
　１１０　生体音センサ
　１２０　信号記憶部
　１２５　信号処理部
　１３０　音声出力部
　１４０　基底保持部
　１５０　表示部
　１５５　表示領域
　１６０　入力部
　２００　処理部
　２１０　周波数解析部
　２２０　周波数ピーク検出部
　２３０　基底集合生成部
　２４０　結合係数算出部
　２５０　信号強度算出部
　２６０　画像生成部
　２７０　呼吸音選択部
　ｙ　スペクトル
　ｈ（ｆ）　基底
　ｕ　結合係数 DESCRIPTION OF SYMBOLS 110 Body sound sensor 120 Signal memory | storage part 125 Signal processing part 130 Audio | voice output part 140 Base holding part 150 Display part 155 Display area 160 Input part 200 Processing part 210 Frequency analysis part 220 Frequency peak detection part 230 Basis set production | generation part 240 Coupling coefficient calculation 240 Unit 250 signal intensity calculation unit 260 image generation unit 270 breath sound selection unit y spectrum h (f) basis u coupling coefficient

Claims (13)

  1. 　呼吸音を正常音及び異常音に分別する分別手段と、
    　前記分別した呼吸音のうち任意の呼吸音を出力する出力手段と
    　を備えることを特徴とする呼吸音解析装置。     A separation means for separating respiratory sounds into normal sounds and abnormal sounds;
    An output means for outputting an arbitrary breathing sound among the sorted breathing sounds.
  2. 　前記出力手段は、前記分別した呼吸音のうち複数の呼吸音を同時に出力することを特徴とする請求項１に記載の呼吸音解析装置。 2. The respiratory sound analyzing apparatus according to claim 1, wherein the output means outputs a plurality of respiratory sounds among the sorted respiratory sounds simultaneously.
  3. 　前記出力手段は、前記任意の呼吸音を音声又はスペクトル画像として出力することを特徴とする請求項１又は２に記載の呼吸音解析装置。 3. The respiratory sound analysis apparatus according to claim 1, wherein the output means outputs the arbitrary respiratory sound as a sound or a spectrum image.
  4. 　前記任意の呼吸音の出力状態を音種毎に変更可能な変更手段を更に備えることを特徴とする請求項１から３のいずれか一項に記載の呼吸音解析装置。 The respiratory sound analysis apparatus according to any one of claims 1 to 3, further comprising changing means capable of changing an output state of the arbitrary respiratory sound for each sound type.
  5. 　前記変更手段は、前記任意の呼吸音の出力音量を音種毎に変更可能であることを特徴とする請求項４に記載の呼吸音解析装置。 The respiratory sound analysis apparatus according to claim 4, wherein the changing means is capable of changing an output volume of the arbitrary respiratory sound for each sound type.
  6. 　前記変更手段は、前記任意の呼吸音の出力音量を所定の周波数帯域毎に変更可能であることを特徴とする請求項５に記載の呼吸音解析装置。 6. The respiratory sound analysis apparatus according to claim 5, wherein the changing means is capable of changing an output volume of the arbitrary respiratory sound for each predetermined frequency band.
  7. 　前記変更手段は、前記任意の呼吸音を示す画像に対して音種毎に所定の画像処理を実行可能であることを特徴とする請求項４から７のいずれか一項に記載の呼吸音解析装置。 The respiratory sound analysis according to any one of claims 4 to 7, wherein the changing unit is capable of executing predetermined image processing for each sound type on the image indicating the arbitrary respiratory sound. apparatus.
  8. 　前記変更手段は、音種毎に前記所定の画像処理を実行した画像を、複数の音種で重ね合わせて出力可能であることを特徴とする請求項７に記載の呼吸音解析装置。 The respiratory sound analysis apparatus according to claim 7, wherein the changing unit is capable of outputting an image obtained by performing the predetermined image processing for each sound type by superimposing a plurality of sound types.
  9. 　前記分別手段は、
    　呼吸音のスペクトルの所定の特徴に対応する周波数に関する情報を取得する取得手段と、
    　前記呼吸音を分類する基準となる複数の基準スペクトルを、前記周波数に関する情報に応じてシフトさせ、周波数シフト基準スペクトルを取得するシフト手段と、
    　前記呼吸音と前記周波数シフト基準スペクトルとに基づいて、前記呼吸音に含まれる前記複数の基準スペクトルの割合を出力する割合出力手段と
    　を有することを特徴とする請求項１から８のいずれか一項に記載の呼吸音解析装置。     The sorting means is
    Acquisition means for acquiring information relating to a frequency corresponding to a predetermined characteristic of the spectrum of the respiratory sound;
    Shift means for shifting a plurality of reference spectra serving as a reference for classifying the breathing sound according to information on the frequency, and obtaining a frequency shift reference spectrum;
    9. A ratio output unit that outputs a ratio of the plurality of reference spectra included in the breathing sound based on the breathing sound and the frequency shift reference spectrum. Respiratory sound analyzer according to item.
  10. 　前記所定の特徴は、極大値であることを特徴とする請求項９に記載の呼吸音解析装置。 The respiratory sound analysis apparatus according to claim 9, wherein the predetermined feature is a maximum value.
  11. 　呼吸音を正常音及び異常音に分別する分別工程と、
    　前記分別した呼吸音のうち任意の呼吸音を出力する出力工程と
    　を備えることを特徴とする呼吸音解析方法。     A separation process for separating respiratory sounds into normal and abnormal sounds;
    An output step of outputting an arbitrary breathing sound among the sorted breathing sounds.
  12. 　呼吸音を正常音及び異常音に分別する分別工程と、
    　前記分別した呼吸音のうち任意の呼吸音を出力する出力工程と
    　をコンピュータに実行させることを特徴とするコンピュータプログラム。     A separation process for separating respiratory sounds into normal and abnormal sounds;
    A computer program for causing a computer to execute an output step of outputting an arbitrary breathing sound among the sorted breathing sounds.
  13. 　請求項１２に記載のコンピュータプログラムが記録されていることを特徴とする記録媒体。 A recording medium in which the computer program according to claim 12 is recorded.
PCT/JP2014/059279 2014-03-28 2014-03-28 Respiratory sound analysis device, respiratory sound analysis method, computer program and recording medium WO2015145763A1 (en)

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