WO2000056218A1 - Dispositif d'analyse de bruits auscultatoires, en particulier de bruits respiratoires - Google Patents
Dispositif d'analyse de bruits auscultatoires, en particulier de bruits respiratoires Download PDFInfo
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- WO2000056218A1 WO2000056218A1 PCT/FR2000/000736 FR0000736W WO0056218A1 WO 2000056218 A1 WO2000056218 A1 WO 2000056218A1 FR 0000736 W FR0000736 W FR 0000736W WO 0056218 A1 WO0056218 A1 WO 0056218A1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B7/00—Instruments for auscultation
- A61B7/003—Detecting lung or respiration noise
Definitions
- the present invention relates to the field of medical analysis of acoustic noises, from a mediated auscultation. It relates more particularly to a device for analyzing acoustic noises generated by the breathing of patients suffering from respiratory pathologies such as cystic fibrosis, asthma or the like.
- cystic fibrosis is an infection of the respiratory tract. Typical bronchial sounds usually reveal such infections. In fact, patients with this pathology generate mucus which obstructs the respiratory tract by producing specific noises during breathing. The acoustic frequencies of the noises generated depend on the size of these mucus and, if necessary, the acoustic intensities associated with these frequencies can be used to estimate a degree of obstruction of the respiratory tract. A danger represented by this pathology is linked to the rapid evolution of the size of the mucus, which can cause a progressive and irreversible limitation of the patient's respiratory capacity.
- the usual protocol consists of mediated auscultation, via a stethoscope.
- the frequencies heard give an indication of the obstruction of the airways.
- this indication is only qualitative, even subjective. It is often difficult to describe sounds and communicate a diagnosis on the basis of such subjective criteria.
- the present invention then improves the situation.
- It relates to a device for analyzing median auscultation noises, in particular respiratory noises, comprising: - an input for receiving a time signal representative of an acoustic noise, sampled in a multiplicity of intensities each associated with an instant selected,
- storage means comprising a processing module arranged to cooperate with calculation means with a view to evaluating a set of transformed intensities, each associated with a predetermined sound frequency, while the storage means are also arranged to store said transformed intensities, and
- an output connected, on the one hand, to the storage means for delivering each intensity transformed in correspondence with an associated frequency, and, on the other hand, to means for representing the intensities transformed as a function of the frequencies, with a view to obtain a spectral representation of the auscultation noise.
- the processing module is arranged to evaluate a set of transformed intensities for each chosen instant, while the storage means are arranged to memorize each intensity transformed as a function of an associated frequency, on the one hand, and of a chosen instant, on the other hand, which makes it possible to obtain a three-dimensional representation in time and in frequencies of the auscultation noise.
- the processing module is arranged to evaluate the set of transformed intensities for each chosen instant, by performing, for each frequency, a sum over a multiplicity of current instants of terms resulting of a product involving, for each current instant, the intensity associated with the sum of the chosen instant and the current instant, on the one hand, and the complex exponential of a pure imaginary product between the frequency and the current moment, on the other hand.
- the processing module is further arranged to evaluate a phase for each intensity, as a function of a difference between the associated selected instant and a selected start instant, with a view to allocating at each intensity a complex value and its combined value, while the terms of said sum are representative of products involving, for each current instant: a) the complex intensity associated with the sum of the chosen instant and the instant current, b) the complex conjugate intensity, associated with the difference between the selected instant and the current instant, and c) said complex exponential, which makes it possible to obtain a spectral representation of the auscultation noise, while keeping a real time scale.
- the storage means also comprise selected coefficients, for weighting the terms of said sum, each associated with a current instant.
- the device comprises display means of the type comprising a monitor and / or a printer, while the representation means comprise a graphic representation module, capable of cooperating with the display means to graphically represent the intensities transformed, as a function of the associated frequencies, on the one hand, and of the selected instants, on the other hand part, forming three-dimensional spectra in time and frequency.
- the representation means comprise a graphic representation module, capable of cooperating with the display means to graphically represent the intensities transformed, as a function of the associated frequencies, on the one hand, and of the selected instants, on the other hand part, forming three-dimensional spectra in time and frequency.
- FIG. 1 schematically represents an analysis device according to the invention, of noises resulting from a mediated auscultation
- FIG. 2A represents the time signal of a normal breathing noise
- FIG. 2B is a spectral representation obtained by Fourier transform of the time signal of FIG. 2A
- FIG. 3A represents the time signal of a breathing noise comprising an adventitious noise of the "sibilance" type
- FIG. 3B is a spectral representation obtained by Fourier transform of the time signal of FIG. 3A
- FIG. 4A represents the time signal of a respiratory noise comprising an adventitious noise of the "crackling" type, in particular of the "large crackling” type,
- FIG. 4B is a spectral representation obtained by Fourier transform of the time signal of FIG. 4A
- FIG. 5A represents the time signal of FIG. 4A, in a reduced time scale
- FIG. 5B is a spectral representation obtained by Fourier transform of the time signal of FIG. 5A
- FIG. 6 is a spectral representation resulting from the analysis carried out by a device according to the invention, on the basis of a time signal representative of a normal breathing noise, of the type represented in FIG. 2A,
- FIG. 7 is a spectral representation resulting from the analysis carried out by a device according to the invention, on the basis of a time signal representative of a breathing noise comprising an adventitious noise of “wheezing” type, type shown in FIG. 3A,
- FIG. 8 is a spectral representation resulting from the analysis carried out by a device according to the invention, from a time signal representative of a breathing noise comprising an adventitious noise of the "fine crackling" type, and
- FIG. 9 is a spectral representation resulting from the analysis carried out by a device according to the invention, from a time signal representative of a breathing noise comprising an adventitious noise of the "large crackling" type (time signal of the type shown in Figure 4A).
- FIG. 1 a device for analyzing respiratory noises according to the invention.
- this device comprises a processing unit (central unit 10), provided with peripheral elements 90 to 97, connected to the central unit.
- the analysis device comprises a computer equipped with a monitor (screen 90), a printer 91, an input keyboard 93 and an on-screen selection device (or mouse 92).
- a harness 95 worn by the patient PA includes an electronic stethoscope provided with a plurality of sensors 94 (six in the example described). These sensors are arranged to work in a frequency band chosen so as to avoid collecting noises originating from the beating of the heart of the PA patient, as well as friction noises from the harness 95, or others. Typically, this frequency band extends from 100 Hz to approximately 2 kHz.
- the bandpass filter 96 symbolizes this function. It should be noted that, as a variant, the sensors 94 can work over a wider frequency band, while the connection between the sensors 94 and the central unit 10, or even the central unit itself, comprises means filtering on a limited frequency band, approximately between 100 Hz and 2 kHz.
- the sound sensors 94 are then arranged to convert an acoustic noise (respiratory noise) into a temporal electrical signal (arrow S).
- a respiratory flow probe 97 (pneumotachograph in the example described), implanted for example in a mask worn on the face of the patient PA.
- the probe 97 is then able to identify an instant of start of breathing, for example between an inspiration and an expiration of the patient PA, from a change in sign of the respiratory flow Q.
- the central unit 10 comprises an interface 1 provided with an Ech sampler (present for example on a sound card), connected to the sensors 94, with a view to sampling the time signal S.
- Ech sampler present for example on a sound card
- the samples of signal S are then stored in random access memory 2 (RAM), with a view to processing them by a processing module of the aforementioned type, recorded in read-only memory 4 (ROM) which the unit contains.
- RAM random access memory 2
- ROM read-only memory 4
- the central unit 10 is provided with a microprocessor 3 ( ⁇ P).
- This microprocessor is advantageously capable of working at higher calculation execution rates or of the order of 150 MHz.
- the spectra resulting from the processing can be recorded on a hard disk of the central unit 10 comprising the read-only memory 4, or also on a removable medium 5, such as a floppy disk, a writable CD-ROM, a magneto-optical disk or other.
- a communication interface 7 may also be provided, provided with a MODEM link for transmitting the content of the random access memory 2 (processed spectrum) to a remote station, such as a data interpretation station (not shown). .
- the communication interface 7 can also receive samples of time signals S, originating from the digitization of respiratory noises from a remote patient (not shown), or even spectra already processed, for interpretation.
- the link 72 between the modem 7 and the RAM 2 of the central unit 10, or even the link 12 between the sound card 1 and the RAM 2 then form, in this example, the input of the aforementioned analysis device .
- the monitor 90 as well as the printer 91, which also comprises the analysis device, make it possible to view the spectra resulting from the abovementioned processing.
- the input keyboard 93 and the mouse 92 make it possible to modify the time scales and frequencies of the spectra represented, or even to refine the choice of the abovementioned weighting coefficients.
- CM motherboard 8
- the monitor 90 is connected to the central unit 10 by a graphics card 6 (link 26), in the usual way.
- the sound card 1 receives a time signal S (t) and samples this signal in a multiplicity of intensities I (n) each associated with a measurement instant n. These intensities are recorded in RAM 2 (link 12).
- the random access memory 2 receives, via an acquisition interface 971, information taken from the ventilatory flow sensor 97, relating to a starting instant n “of the measurement of respiratory noise (start expiration or start of inspiration). From the selected starting time n “, the processing module assigns a phase ⁇ [I (n)] to each intensity (or sample I (n)). Typically, these phases are estimated as a function of the time difference ⁇ - ⁇ Q. The processing module thus forms a multiplicity of complex intensities I c (n) which results from the product between the intensities I (n) and complex exponentials of the phases associated with these intensities. These complex intensities are then written:
- the processing module of the analysis device evaluates transformed intensities I ⁇ by performing the sum:
- the frequencies f ⁇ are initially recorded in read-only memory of the central unit and are transferred by the link 42 to the random access memory so as to carry out the calculations giving the transformed intensities I - r ⁇ .
- the coefficients associated with instants n are initially recorded in read-only memory 4 and are also transferred by the link 42 for the calculation of the transformed intensities.
- These weighting coefficients h (n) can be chosen by depending on the type of breathing noise you want to treat, as we will see later.
- the transformed intensities Im then depend on a predetermined instant m, as well as on a frequency f ⁇ .
- the processing module therefore performs a "time-frequency" transformation of the sampled intensities. For example, if K frequencies f k are stored in read-only memory 4, the processing module evaluates, for each predetermined instant m, K values of transformed intensities I ⁇ . In parallel, if the signal S (t) is sampled over N predetermined times, the processing module evaluates N values of transformed intensities I ⁇ , for each of the frequencies f ⁇ . The processing module evaluates in all NxK transformed intensities.
- a spectrogram type transformation is carried out at 255 time points (including a central point) and 255 frequency points, in time weighting or "short-term observation" windows (for example Hamming windows), allowing weighting by the coefficients h (n).
- N time weighting or "short-term observation” windows
- FIGS. 6, 7, 8 and 9 Another possible representation, pseudo-two-dimensional, is shown in FIGS. 6, 7, 8 and 9. Ranges of transformed, increasing intensities are represented by different types of hatching (oblique, horizontal, crossed).
- the adventitious noises generated by his breathing include specific noises, such as wheezing (wheezing in relatively high frequency ranges), fine cracklings (short crackles over time) and large cracklings (more long crackles over time).
- Figure 7 represents the evolution in time and frequency of a sibilance.
- a substantially decreasing curve S2 equivalent to a "whistling" around a frequency close to 0.75 kHz.
- Such a change in frequency of this whistling sound is difficult to detect by the usual processing devices, which are based on fast Fourier transforms of the time signal S (t) (term translated from English “Fast Fourier Transform", designated here - after by FFT).
- FIGS. 2A and 2B represent the time signal of a normal respiratory noise (FIG. 2A) and its spectral representation (FIG. 2B) obtained by fast Fourier transforms.
- the respiratory noise does not contain components relating to a particular pathology
- the temporal signal of the respiratory noise does not have periodicity and the processing by fast Fourier transform (FFT) does not include peaks around a particular frequency.
- FFT fast Fourier transform
- FIGS. 3A and 3B represent the temporal signal of a respiratory noise comprising a sibilance and the Fourier transform of this temporal signal (FIG. 3B).
- FIGS. 3A and 3B represent the temporal signal of a respiratory noise comprising a sibilance and the Fourier transform of this temporal signal (FIG. 3B).
- Figure 3B a peak around a frequency close to 110 Hz, significant of this sibilance.
- a treatment by FFT which extends over one second of integration will give a peak of sibilance, of reduced intensity and of width extending over the whole variation temporal frequency of sibilance.
- such a peak is difficult to detect by conventional treatment using FFTs.
- FIG. 6 describes the spectrum resulting from a transformation into "time-frequency" according to the invention, from a normal vesicular murmur (respiratory noise without pathology).
- the different types of hatching are representative of ranges of intensities transformed (respectively increasing) according to a real time scale for which the temporal values are preserved, along the axis of abscissas and of a frequency scale, along the ordinate axis. We do not notice any particular spectral event.
- the "time-frequency" spectrum represented in FIG. 7 substantially highlights four sibilances which are continuous noises of duration generally greater than or equal to 125 ms:
- an FFT treatment would have given a peak around 0.55 kHz, if however this treatment is carried out on a time scale between 1.5 s and 2.5 s.
- the second sibilance treated by FFT would have given an enlarged peak and of reduced intensity around 0.75 kHz.
- FIG. 7 it is clear that the second sibilance is that of the highest intensity.
- treatment with FFT on a time scale between 1.5 s and 2.5 s would have favored the most temporally stable sibilance (the third sibilance) and would have revealed a peak at 0.55 kHz of higher intensity than the peak around 0.75 kHz.
- This type of rattle is divided into two classes according to whether they are formed by one (monophonic) or several (polyphonic) frequencies.
- the spectral representation of respiratory noise highlights a monophonic sibilance
- this spectral representation highlights polyphonic sibilance (at three frequencies in the spectrum represented).
- FIGS. 8 and 9 describe fine cracklings (FIG. 8) and large cracklings (FIG. 9).
- pulses C1 to C6 appear, to substantially vertical trends, with a maximum frequency of around 1 kHz. These temporally brief events correspond to fine crackles.
- pulses GC1 to GC8 appear, of frequency limited to approximately 600 Hz and of considerably longer time duration (around 20 ms). When listening, these impulses typically correspond to cracklings.
- a time-frequency transformation established according to formula 1, of the "pseudo igner-City" type is preferably used.
- this type of transformation generates frequency and / or time interference, and independent smoothing in time and frequency should be carried out. Transformations of the "pseudo Cho ⁇ -illiams", “Zhao-Atlas-Marks”, or even “pseudo Born-Jordan” type make it possible to reduce the interference terms of the Wigner-Ville distribution while preserving temporal marginal distributions and frequency.
- a transformation into “Morlet wavelets”, corresponding to Gaussian weightings is particularly suitable for wideband signals comprising both very high and very low frequencies. Each of these transformations can be adapted to the treatment of a particular type of adventitious noise.
- a particular set of weighting coefficients h (n) can be chosen.
- the patient PA can be asked to occupy a particular bodily position, mainly generating this adventitious noise.
- the content of the transformed intensities, stored in random access memory 2 is transferred (link 26) to the graphic card 6 in the form of graphic data, which makes it possible to visualize a first time-frequency spectrum of the signal time S (t), on the screen of the monitor 90.
- a graphical representation module stored in the read-only memory of the central unit, in order to graphically represent the intensities transformed as a function of the associated frequencies, on the one hand, and predetermined instants, on the other hand, by forming spectra of the type represented in FIGS. 6 to 9.
- an operator can choose using the keyboard 93 an adapted set of coefficients, stored in read-only memory of the central unit. For example, using the mouse 92, the operator can also select a preferred time and / or frequency scale to view a detail of the spectral representation.
- the operator can decide to store the content of the RAM 2, including the transformed intensities, in ROM 4 of the central unit (link 24). It can also decide to print the spectral representation displayed by the monitor 90, from the printer 91.
- it becomes possible to compare spectra measured at different times (for example a few days apart) from a spectrum stored in read only memory 4 or printed in 91, and from a spectrum obtained recently and stored in random access memory 2.
- time signals S (t) can be sampled and stored in read-only memory (link 14) at different times, while the spectral analysis is carried out, deferred, on a plurality of time signals.
- the communication interface 7 in connection with the central unit 10, receiving the data representative of the transformed intensities (link 27 from the RAM 2 to MODEM 7), sends them to the central unit of the doctor.
- the central unit can receive by the link 72 (from the MODEM to the random access memory) data representative of transformed intensity, or alternatively samples of a time signal S (t), measured remotely.
- the data stored on such a medium can be transferred from one analysis station to another.
- an operator wishes to listen to a sampled signal S (t)
- the choice of the transformation method is independent of the general treatment of auscultation noises, which the analysis device according to the invention performs.
- the choice of a type of transformation is manifested by a set of weighting coefficient h (n) preferred.
- h (n) For example, a time-frequency transformation in which the coefficients h (n) (formula 2) are replaced by "1" (which amounts to performing a "double transform of
- the present invention is, of course, aimed at the tools of the analysis device allowing its implementation (processing module, etc.), but also all the products resulting from its implementation (representations spectral obtained by different types of time-frequency transformations, printed on screen or on paper, as well as all comparisons of spectra obtained by an analysis device according to the invention).
- the present invention also relates to an analysis device making it possible to represent a comparison of a plurality of spectra, measured at different times, to demonstrate for example an aggravation or an improvement in the patient's condition.
- the device according to the invention is also provided with a module for comparing different spectra loaded in random access memory 2, with different naked start times, with a view to detecting an evolution of the auscultation noises by comparison of the intensities transformed and the number of adventitious noises revealed.
- a module is preferably stored in read-only memory 4. For example, an alarm signal can then be provided if between two spectra performed at different times, a worsening of the patient's condition has been observed.
- the analysis device can continuously monitor the breathing of a patient, by taking, for example at a constant time interval, a "snapshot" by time-transformation processing. breathing frequencies and by comparing, for example to predetermined thresholds, the transformed intensities and / or the frequencies of detected adventitious noises.
- This embodiment finds a particularly interesting application in the surveillance of infants suffering from pulmonary infections.
- provision may be made to immobilize on the infant's body at least one stethoscope connected to a radiofrequency transmitter, communicating with a receiver connected for example to the sound card of a home computer. This computer then performs successive spectral processing of the type described above and, if necessary, without providing a flow probe.
- the processing module stored in the main unit's read-only memory is preferably programmed in "DSP" language, and cooperates with a calculation manager, preferably an adequate version of "MATHLAB” (registered trademarks ).
- DSP digital signal processor
- a calculation manager preferably an adequate version of "MATHLAB” (registered trademarks ).
- the patient's stethoscope has six sensors.
- the number of sensors of the stethoscope can be different.
- certain sensors can be placed on the patient's back.
- the filtering 96 carried out by the stethoscope is described here by way of example.
- the analysis device according to the invention for example the analysis of auscultation noises of the beating of the heart of a patient, provision may be made to filter the time signals received over a frequency band more low, suitable for heart sounds.
- the present invention can be applied to the analysis of auscultation noises drawn from heartbeats, or any other auscultation noises. It can also be applied to the analysis of respiratory noises from patients suffering from asthma, or from another respiratory pathology.
- spectral filtering can also be provided (additional weighting coefficients h (f k ) in the sum of Formula 1) as a function of the type of auscultation noise that one wishes to analyze.
- the analysis device according to the invention is in the form of a computer connected, on the one hand, to sound pickup means and, on the other hand , to peripherals, in particular graphic representation.
- the device may be in the form of a device for specific use, for example of the "DSP" type (for "Digital Signal Process"), connected to the aforementioned sound recording means (stethoscopes in the example described ).
- the structure of the central unit, in the example described above, of the analysis device according to the invention is susceptible of variants. However, it is preferable to keep storage means (random access memory and / or read only memory) of the sampled intensities and of the transformed intensities, which are addressable as a function of the sampling instants and of the stored frequency sets f k , chosen.
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Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
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JP2000606127A JP2002538921A (ja) | 1999-03-24 | 2000-03-23 | 聴診音、特に呼吸音の分析装置 |
CA002366686A CA2366686A1 (fr) | 1999-03-24 | 2000-03-23 | Dispositif d'analyse de bruits auscultatoires, en particulier de bruits respiratoires |
EP00914243A EP1202669A1 (fr) | 1999-03-24 | 2000-03-23 | Dispositif d'analyse de bruits auscultatoires, en particulier de bruits respiratoires |
US09/937,424 US6699204B1 (en) | 1999-03-24 | 2000-03-23 | Device for analyzing auscultation sounds, in particular respiratory sounds |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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FR99/03682 | 1999-03-24 | ||
FR9903682A FR2791248B1 (fr) | 1999-03-24 | 1999-03-24 | Dispositif d'analyse de bruits auscultatoires, en particulier de bruits respiratoires |
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WO2000056218A1 true WO2000056218A1 (fr) | 2000-09-28 |
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Application Number | Title | Priority Date | Filing Date |
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PCT/FR2000/000736 WO2000056218A1 (fr) | 1999-03-24 | 2000-03-23 | Dispositif d'analyse de bruits auscultatoires, en particulier de bruits respiratoires |
Country Status (6)
Country | Link |
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US (1) | US6699204B1 (fr) |
EP (1) | EP1202669A1 (fr) |
JP (1) | JP2002538921A (fr) |
CA (1) | CA2366686A1 (fr) |
FR (1) | FR2791248B1 (fr) |
WO (1) | WO2000056218A1 (fr) |
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EP1531719A1 (fr) * | 2002-02-21 | 2005-05-25 | MURPHY, Raymond, L.,H. | Procede et appareil pour l'analyse de sons corporels |
JP2014050672A (ja) * | 2012-08-09 | 2014-03-20 | Jvc Kenwood Corp | 呼吸音分析装置、呼吸音分析方法および呼吸音分析プログラム |
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CA3010835C (fr) | 2018-03-30 | 2024-05-14 | The Board Of Trustees Of Western Michigan University | Appareil stethographique |
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US6139505A (en) * | 1998-10-14 | 2000-10-31 | Murphy; Raymond L. H. | Method and apparatus for displaying lung sounds and performing diagnosis based on lung sound analysis |
US6368283B1 (en) * | 2000-09-08 | 2002-04-09 | Institut De Recherches Cliniques De Montreal | Method and apparatus for estimating systolic and mean pulmonary artery pressures of a patient |
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- 1999-03-24 FR FR9903682A patent/FR2791248B1/fr not_active Expired - Fee Related
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2000
- 2000-03-23 JP JP2000606127A patent/JP2002538921A/ja active Pending
- 2000-03-23 EP EP00914243A patent/EP1202669A1/fr not_active Withdrawn
- 2000-03-23 CA CA002366686A patent/CA2366686A1/fr not_active Abandoned
- 2000-03-23 US US09/937,424 patent/US6699204B1/en not_active Expired - Fee Related
- 2000-03-23 WO PCT/FR2000/000736 patent/WO2000056218A1/fr not_active Application Discontinuation
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Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7520861B2 (en) | 1998-10-14 | 2009-04-21 | Murphy Raymond L H | Method and apparatus for displaying body sounds and performing diagnosis based on body sound analysis |
JP2005512753A (ja) * | 2002-01-10 | 2005-05-12 | デイープブリーズ・リミテツド | 気道音響の解析及び画像形成用システム |
EP1531719A1 (fr) * | 2002-02-21 | 2005-05-25 | MURPHY, Raymond, L.,H. | Procede et appareil pour l'analyse de sons corporels |
EP1531719A4 (fr) * | 2002-02-21 | 2006-12-27 | Raymond L H Murphy | Procede et appareil pour l'analyse de sons corporels |
JP2014050672A (ja) * | 2012-08-09 | 2014-03-20 | Jvc Kenwood Corp | 呼吸音分析装置、呼吸音分析方法および呼吸音分析プログラム |
Also Published As
Publication number | Publication date |
---|---|
EP1202669A1 (fr) | 2002-05-08 |
FR2791248A1 (fr) | 2000-09-29 |
US6699204B1 (en) | 2004-03-02 |
JP2002538921A (ja) | 2002-11-19 |
FR2791248B1 (fr) | 2001-08-24 |
CA2366686A1 (fr) | 2000-09-28 |
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