WO2020024312A1 - Breathing signal extraction method, apparatus, processing device and system - Google Patents

Breathing signal extraction method, apparatus, processing device and system Download PDF

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Publication number
WO2020024312A1
WO2020024312A1 PCT/CN2018/099254 CN2018099254W WO2020024312A1 WO 2020024312 A1 WO2020024312 A1 WO 2020024312A1 CN 2018099254 W CN2018099254 W CN 2018099254W WO 2020024312 A1 WO2020024312 A1 WO 2020024312A1
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Prior art keywords
waveform
signal
time interval
breathing
same characteristic
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PCT/CN2018/099254
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French (fr)
Chinese (zh)
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叶飞
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深圳市大耳马科技有限公司
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Publication of WO2020024312A1 publication Critical patent/WO2020024312A1/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
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • 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
    • 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
    • A61B5/0803—Recording apparatus specially adapted therefor
    • 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
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/02—Stethoscopes
    • A61B7/04—Electric stethoscopes

Definitions

  • the invention belongs to the medical field, and particularly relates to a method, a device, a processing device and a system for extracting respiratory signals.
  • the sensor can sense and collect the vibration data signal of the body.
  • the original vibration signal collected by the sensor usually includes the body's heart beat signal, breathing signal, environmental micro-vibration signal, interference signal caused by body movement, and the circuit's own noise signal. If the breathing signal is obtained from the original vibration signal, the original vibration signal needs to be pre-processed (such as filtering, etc.) to capture the breathing waveform.
  • the pressure changes caused by the exhalation and inhalation of the body are related to the measurement position of the sensor.
  • the breathing waveforms that may be obtained at different positions are different, making it difficult to judge the breath from the breathing waveform. And inspiratory band.
  • the breathing waveform may be very weak or distorted under the influence of external low-frequency disturbances in some scenarios.
  • the purpose of the present invention is to provide a method, a device, a computer-readable storage medium, a processing device, and a system for extracting respiratory signals, which are intended to solve the difficulty in determining the actual exhalation and inhalation process in the prior art method for obtaining respiratory waveforms.
  • the breathing waveform in the scene may be very weak or distorted due to the influence of external low-frequency disturbances.
  • the present invention provides a method for extracting a breathing signal, the method comprising:
  • the present invention provides a device for extracting respiratory signals, the extraction device comprising:
  • An acquisition module for acquiring a waveform of a heart beat monitoring signal
  • the breathing waveform acquisition module is configured to obtain the time interval of the same characteristic event according to the waveform of the heart beat monitoring signal, and obtain the breathing waveform according to the time interval of the same characteristic event.
  • the present invention provides a computer-readable storage medium that stores a computer program that, when executed by a processor, implements the steps of the breathing signal extraction method described above.
  • the present invention provides a breathing signal extraction processing device, including:
  • One or more processors are One or more processors;
  • One or more computer programs wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, which are implemented when the processors execute the computer programs The steps of the method for extracting a breathing signal as described above.
  • the present invention provides a respiratory signal extraction system, the extraction system includes:
  • a generation module configured to generate a waveform of a heart beat monitoring signal
  • An extraction processing device connected to the generation module, such as the above-mentioned breathing signal.
  • the waveform of the heart beat monitoring signal is obtained, and then the time interval of the same characteristic event is obtained according to the waveform of the heart beat monitoring signal, and the breathing waveform is obtained according to the time interval of the same characteristic event. Therefore, it is possible to prevent the breathing signal from being affected or even distorted due to the weak breathing signal or the external low-frequency disturbance in some scenes.
  • the present invention can more accurately obtain the breathing signal, and the rise or fall of the breathing waveform obtained by the invention can directly determine the call In the process of inhalation or inhalation, it is more convenient to combine the respiratory signal with the parameters related to the heart for clinical analysis and calculation to meet more clinical needs.
  • FIG. 1 is a flowchart of a method for extracting a breathing signal according to a first embodiment of the present invention.
  • FIG. 2 is a schematic diagram of generating a time-domain waveform of a BCG signal based on an original vibration signal, wherein FIG. 2 (a) is a schematic diagram of an original vibration signal waveform, and FIG. 2 (b) is a schematic diagram of a time-domain waveform of the BCG signal.
  • FIG. 3 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the BCG signal, and obtaining the breathing waveform according to the change of the time interval of the same characteristic event with time.
  • FIG. 3 (a) is a diagram of the BCG signal. Schematic diagram of time domain waveforms
  • Fig. 3 (b) is a schematic diagram of time intervals of the same characteristic events
  • Fig. 3 (c) is a schematic diagram of respiratory waveforms.
  • Figure 4 shows the time-domain waveform of the BCG signal generated from the original vibration signal.
  • the waveform of the BCG signal obtained by the second-order differential transformation is a schematic diagram, where Figure 4 (a) is a schematic diagram of the original vibration signal waveform, and Figure 4 (b) is A schematic diagram of the time domain waveform of the BCG signal.
  • FIG. 4 (c) is a schematic diagram of the waveform of the BCG signal obtained through the second-order differential transformation.
  • FIG. 5 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peaks of the waveform of the second-order differential transformation of the BCG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event.
  • FIG. 5 ( a) is a waveform diagram of the BCG signal obtained by second-order differential transformation.
  • FIG. 5 (b) is a time interval diagram of the same characteristic event, and
  • FIG. 5 (c) is a breathing waveform diagram.
  • FIG. 6 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the ECG signal, and obtaining the breathing waveform according to the change of the time interval of the same characteristic event with time.
  • Figure 6 (b) shows the time interval of the same characteristic event
  • Figure 6 (c) shows the respiratory waveform.
  • FIG. 7 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the PPG signal, and obtaining the breathing waveform according to the change of the time interval of the same characteristic event with time, wherein FIG. 7 (a) is the waveform of the PPG signal A schematic diagram, FIG. 7 (b) is a time interval diagram of the same characteristic event, and FIG. 7 (c) is a breathing waveform diagram.
  • FIG. 8 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the SCG signal, and obtaining the breathing waveform according to the change of the time interval of the same characteristic event with time, wherein FIG. 8 (a) is the waveform of the SCG signal 8 (b) is a time interval diagram of the same characteristic event, and FIG. 8 (c) is a breathing waveform diagram.
  • FIG. 9 is a functional block diagram of a breathing signal extraction device provided by Embodiment 2 of the present invention.
  • FIG. 10 is a specific structural block diagram of a breathing signal extraction processing device provided in Embodiment 4 of the present invention.
  • FIG. 11 is a specific structural block diagram of a breathing signal extraction system provided by Embodiment 5 of the present invention.
  • the breathing signal extraction method provided by the first embodiment of the present invention includes the following steps: It should be noted that if there is substantially the same result, the breathing signal extraction method of the present invention is not as shown in FIG. Process sequence is limited.
  • the heartbeat monitoring signals may be Ballistocardiogram (BCG) signals, Electrocardiogram (ECG) signals, Phonocardiogram (PCG) signals, and Seismocardiogram (SCG) signals.
  • BCG Ballistocardiogram
  • ECG Electrocardiogram
  • PCG Phonocardiogram
  • SCG Seismocardiogram
  • Photoelectric volume pulse wave Photoplethysmograph
  • the heart beat monitoring signal is a BCG signal, a PCG signal or an SCG signal
  • the heart beat monitoring signal is obtained through a vibration sensor.
  • the heart beat monitoring signal is an ECG signal
  • the heart beat monitoring signal is obtained through an electrocardiograph
  • the heart beat monitoring signal is a PPG signal
  • the heart beat monitoring signal is obtained through a PPG signal collector.
  • the vibration sensor may be an acceleration sensor, a speed sensor, a displacement sensor, a pressure sensor, a strain sensor, or a sensor that converts the equivalent of a physical quantity equivalently based on acceleration, speed, pressure, or displacement (for example, Electrostatic charge sensitive sensors, inflatable micro-motion sensors, radar sensors, etc.).
  • the strain sensor may be an optical fiber strain sensor.
  • the vibration sensor can be placed on a contact surface behind a human lying on his back, a contact surface behind a human lying on his back within a predetermined range of inclination angles, a contact surface behind a human lying on a wheelchair or other reclining object, and the like.
  • the living body may be a living body performing vital sign signal monitoring.
  • a living body performing vital sign signal monitoring.
  • hospital patients such as elderly, imprisoned, etc.
  • caregivers such as elderly, imprisoned, etc.
  • the body needs to be measured in a quiet state.
  • the waveform of the heart beat monitoring signal represents a signal that is detected and recorded by a vibration sensor when the heart beat process causes the body to vibrate.
  • S101 may specifically be: filtering and scaling the original vibration signal obtained by the vibration sensor to generate a heartbeat monitoring signal waveform.
  • one or more of IIR filter, FIR filter, wavelet filter, zero-phase bidirectional filter, polynomial fitting smoothing filter, etc. can be used according to the requirements of the filtered signal characteristics. Combination to filter and denoise the original vibration signal.
  • Fig. 2 is a schematic diagram of generating a time-domain waveform of a BCG signal based on an original vibration signal.
  • Each waveform has obvious characteristics and good consistency, regular periodicity, clear outline, and stable baseline.
  • the “J” peak of the BCG signal can be extracted from the data characteristics.
  • the "J” peak has the following characteristics: a relatively narrow spike, and a sharper falling edge after the spike ends, gradually decreasing to the lowest point of the waveform of the heartbeat.
  • the "J” peak represents the maximum of one of acceleration, pressure, and displacement that the vibration effect caused by ejection is sensitive to by the vibration sensor.
  • the heartbeat is divided by the "J" peaks of the filtered BCG signal. This data includes a total of 30 "J” peaks from 1 to 30.
  • S102 may specifically be:
  • the waveform of the heart beat monitoring signal after transformation may be: the time interval of the heart beat monitoring signal waveform that does not affect the same characteristic events on its time domain signal through integral transformation, differential transformation (such as second-order differential transformation), etc.
  • the waveform of the transformation of the distribution characteristics may be: the time interval of the heart beat monitoring signal waveform that does not affect the same characteristic events on its time domain signal through integral transformation, differential transformation (such as second-order differential transformation), etc.
  • Obtaining a breathing waveform according to the change of the time interval of the same characteristic event over time may be: based on the change of the time interval of the same characteristic event over time, extracting the breathing waveform using linear interpolation, cubic spline fitting, polynomial fitting, and the like.
  • the time interval for obtaining the same characteristic event according to the same characteristic peak / valley of the waveform of the heart beat monitoring signal, or the waveform of the same characteristic peak / valley of the transformed waveform according to the waveform of the heart beat monitoring signal may specifically be:
  • each identical characteristic peak / valley is detected, and the time interval between each identical characteristic peak / valley and the same characteristic peak / valley of the adjacent preamble is calculated.
  • the time interval is taken as the time interval of the same characteristic event corresponding to the same characteristic peak / valley.
  • FIG. 3 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the BCG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event.
  • the “J” peak of each heartbeat can be easily detected based on the BCG signal. Affected by the cardiopulmonary coupling effect, the "J” peak also exhibits a "high and low” contour along with the exhalation and inspiration process.
  • the time interval between each "J” peak and the adjacent pre-order "J” peak is calculated, that is, the "JJ” time interval, that is, the same characteristic event. time interval.
  • the "J-J" time interval at the first "J” peak is the time interval with the adjacent pre-order "J” peak.
  • the time at which each "J" peak is located is taken as the abscissa, and the "J-J" time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted.
  • the respiratory waveform shown in Figure 3 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same.
  • the basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part For the exhalation process, you can do more calculations and analysis based on the signal during the exhalation / inhalation phase.
  • the “J” peak is used as the characteristic peak for heartbeat division.
  • the “I” valley and the “K” valley on the left and right sides of the “J” peak, the characteristic peak and valley groups (MC, AVO, etc.) corresponding to the systole, the peaks and valleys (AVC, MO, etc.) corresponding to the diastolic phase and other distinctive peaks and valleys are used for classification.
  • a time-domain waveform of a BCG signal is generated based on the original vibration signal, and a waveform obtained by subjecting the BCG signal to second-order differential transformation is illustrated.
  • the waveform obtained by the second-order differential transformation also has obvious characteristic peaks, which are long and narrow and are significantly higher than other peaks in a single heartbeat, such as the "J ⁇ " peak.
  • FIG. 5 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the BCG signal subjected to the second-order differential transformation, and obtaining the breathing waveform according to the time interval of the same characteristic event.
  • the time interval between each “J ⁇ ” peak and the adjacent pre-order “J ⁇ ” peak is calculated, that is, "J ⁇ -J ⁇ " time interval, that is, the time interval of the same characteristic event.
  • Each time interval has been identified in FIG. 5.
  • the "J ⁇ -J ⁇ " time interval at the first "J ⁇ " peak is the time interval with the adjacent pre-order "J ⁇ " peak.
  • the time at which each "J ⁇ " peak is located is taken as the abscissa, and the "J ⁇ -J ⁇ " time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted.
  • the breathing waveform shown in Figure 5 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same.
  • the basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part.
  • the “J ⁇ ” peak is used as a characteristic peak to perform heartbeat division, and the signal characteristics may also be used to perform division based on other peaks or valleys with obvious characteristics.
  • FIG. 6 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the ECG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event.
  • the most obvious characteristic peak is a narrow and tall "R” peak.
  • the “R” peak of each heartbeat can be easily detected based on the ECG signal. Affected by the cardiopulmonary coupling effect, the “R” peak also presents a “high and low” profile along with the exhalation and inspiration process.
  • the time interval between each "R” peak and the adjacent preamble "R” peak is calculated, that is, the "R-R” time interval, that is, the same characteristics The time interval of the event.
  • the "R-R” time interval at the first "R” peak is the time interval with the adjacent pre-order "R” peak.
  • the time at which each "R" peak is located is taken as the abscissa, and the "R-R” time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted.
  • the breathing waveform shown in Figure 6 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same.
  • the basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part. For the exhalation process, you can do more calculations and analysis based on the signal during the exhalation / inhalation phase.
  • the “R” peak is used as a characteristic peak to perform heartbeat division, and it may also be classified based on other peaks or valleys with obvious characteristics according to signal characteristics.
  • the ECG signal can also be transformed by integral transformation, differential transformation (such as second-order differential transformation), and other transformation methods that do not affect the distribution characteristics of the time interval of the same characteristic event on its time domain signal, and then based on the characteristics of the transformed ECG signal Peaks and valleys are divided into heartbeats.
  • the ECG signal shown in FIG. 6 is a lead II signal collected through five leads, and other lead signals can be analogized based on this embodiment.
  • FIG. 7 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the PPG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event.
  • the most obvious characteristic peak is the narrow and tall “P” peak. It is easy to detect the “P” peak of each heartbeat based on the PPG signal. Affected by the cardiopulmonary coupling effect, the "P” peak also presents a "high and low” contour along with the exhalation and inspiration process.
  • the time interval between each "P” peak and the adjacent previous "P” peak is calculated, that is, the "P-P” time interval, that is, the same characteristics The time interval of the event. Each time interval has been identified in Figure 7.
  • the "P-P" time interval at the first "P” peak is the time interval from the adjacent "P” peak.
  • the time at which each "P" peak is located is taken as the abscissa, and the "P-P" time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted.
  • the breathing waveform shown in Figure 7 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same.
  • the basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part. For the exhalation process, you can do more calculations and analysis based on the signal during the exhalation / inhalation phase.
  • the “P” peak is used as a characteristic peak to perform heartbeat division, and it can also be divided based on other peaks or valleys with obvious characteristics according to the signal characteristics.
  • the PPG signal can also be transformed by integral transformation, differential transformation (such as second-order differential transformation), and other transformation methods that do not affect the distribution characteristics of the time interval of the same characteristic event on its time domain signal, and then based on the characteristics of the transformed PPG signal Peaks and valleys are divided.
  • the PPG signal shown in FIG. 7 is a PPG signal collected by a finger, and signals of other parts can be analogized based on this embodiment.
  • FIG. 8 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the SCG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event.
  • the most obvious characteristic peak is the narrow and tall "S” peak. It is easy to detect the "S” peak of each heartbeat based on the SCG signal. Affected by the cardiopulmonary coupling effect, the "S” peak also presents a "high and low” contour along with the exhalation and inspiration process.
  • the time interval between each “S” peak and the adjacent “S” peak is calculated, that is, the “S-S” time interval, that is, the same characteristics The time interval of the event.
  • the "S-S” time interval at the first "S” peak is the time interval from the adjacent "S” peak.
  • the time at which each “S” peak is located is taken as the abscissa, and the “S-S” time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted.
  • the respiratory waveform shown in Figure 8 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same.
  • the basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part. For the exhalation process, you can do more calculations and analysis based on the signal during the exhalation / inhalation phase.
  • the “S” peak is used as a characteristic peak to perform heartbeat division, and it may also be classified based on other peaks or valleys with obvious characteristics according to signal characteristics.
  • the SCG signal can also be transformed by integral transformation, differential transformation (such as second-order differential transformation), and other transformation methods that do not affect the distribution characteristics of the time interval of the same characteristic event on its time domain signal, and then based on the characteristics of the transformed SCG signal Peaks and valleys are divided.
  • the SCG signal shown in FIG. 8 is an SCG signal collected through the apex of the chest when lying down, and the signals of other parts can be analogized based on this embodiment.
  • a breathing signal extraction device provided in Embodiment 2 of the present invention includes:
  • the respiratory waveform acquisition module 22 is configured to acquire the time interval of the same characteristic event according to the waveform of the heart beat monitoring signal, and acquire the respiratory waveform according to the time interval of the same characteristic event.
  • the breathing signal extraction device provided in the second embodiment of the present invention and the breathing signal extraction method provided in the first embodiment of the present invention belong to the same concept, and the specific implementation process thereof is detailed in the entire description, and will not be repeated here.
  • Embodiment 3 of the present invention provides a computer-readable storage medium.
  • the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the breathing signal extraction method provided by Embodiment 1 of the present invention is implemented. A step of.
  • Embodiment 4 is a diagrammatic representation of Embodiment 4:
  • Embodiment 4 of the present invention provides a breathing signal extraction processing device 100.
  • the breathing signal extraction processing device 100 includes: one or more processors 101, a memory 102, and one or more A computer program in which the processor 101 and the memory 102 are connected by a bus, the one or more computer programs are stored in the memory 102 and configured to be executed by the one or more processors 101 When the processor 101 executes the computer program, the steps of the method for extracting the breathing signal provided by the first embodiment of the present invention are implemented.
  • Embodiment 5 is a diagrammatic representation of Embodiment 5:
  • a breathing signal extraction system provided in Embodiment 5 of the present invention includes:
  • a generation module 11 configured to generate a waveform of a heart beat monitoring signal
  • the breathing signal extraction and processing device 100 which is connected to the generating module, is provided by the fourth embodiment of the present invention.
  • the generating module when the heartbeat monitoring signal is a BCG signal, a PCG signal or an SCG signal, the generating module is a vibration sensor; when the heartbeat monitoring signal is an ECG signal, the generating module is an electrocardiograph; when the heartbeat monitoring is When the signal is a PPG signal, the generating module is a PPG signal collector.
  • the waveform of the heart beat monitoring signal is obtained, and then the time interval of the same characteristic event is obtained according to the waveform of the heart beat monitoring signal, and the breathing waveform is obtained according to the time interval of the same characteristic event. Therefore, it is possible to prevent the breathing signal from being affected or even distorted due to the weak breathing signal or the external low-frequency disturbance in some scenes.
  • the present invention can more accurately obtain the breathing signal, and the rise or fall of the breathing waveform obtained by the invention can directly determine the call In the process of inhalation or inhalation, it is more convenient to combine the respiratory signal with the parameters related to the heart for clinical analysis and calculation to meet more clinical needs.
  • the program may be stored in a computer-readable storage medium.
  • the storage medium may include: Read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks, etc.

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  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)

Abstract

The present invention is applicable to the field of medicine and provides a breathing signal extraction method, apparatus, processing device and system. The method comprises: acquiring a waveform of a monitoring signal for cardiac impulses; acquiring a time interval between the same feature events according to the waveform of the monitoring signal for cardiac impulses, and acquiring a breathing waveform according to the change in the time interval between the same feature events over time. The present invention can prevent a breathing signal from being affected or even distorted due to a weak breathing signal or external low-frequency disturbance under certain scenarios, and thus can acquire the breathing signal more accurately. Also, an exhaling process and an inhaling process can be determined directly from the rise and fall of a breathing waveform acquired by means of the present invention, and a breathing signal can be more conveniently combined with cardiac parameters for clinical analysis and calculation, meeting more clinical requirements.

Description

一种呼吸信号的提取方法、装置、处理设备和***Extraction method, device, processing equipment and system for breathing signals 技术领域Technical field
本发明属于医学领域，尤其涉及一种呼吸信号的提取方法、装置、处理设备和***。The invention belongs to the medical field, and particularly relates to a method, a device, a processing device and a system for extracting respiratory signals.
背景技术Background technique
传感器可以感应并采集机体的振动数据信号，传感器所采集到的原始振动信号通常包含了机体心脏搏动信号、呼吸信号、环境微振动信号、机体体动引起的干扰信号、电路自身噪音信号等。如果从原始振动信号中获取呼吸信号，需要对原始振动信号进行预处理(如滤波等)来捕获呼吸波形。The sensor can sense and collect the vibration data signal of the body. The original vibration signal collected by the sensor usually includes the body's heart beat signal, breathing signal, environmental micro-vibration signal, interference signal caused by body movement, and the circuit's own noise signal. If the breathing signal is obtained from the original vibration signal, the original vibration signal needs to be pre-processed (such as filtering, etc.) to capture the breathing waveform.
由于传感器敏感的是振动位移变化引起的压力变化，机体呼气和吸气过程引起压力变化与传感器的测量位置相关，不同位置可能得到的呼吸波形具差异性，从而难以从呼吸波形中判断呼气和吸气的波段。另外，某些场景下呼吸波形可能非常微弱或者受外界低频扰动影响而发生畸变。Because the sensor is sensitive to pressure changes caused by changes in vibration displacement, the pressure changes caused by the exhalation and inhalation of the body are related to the measurement position of the sensor. The breathing waveforms that may be obtained at different positions are different, making it difficult to judge the breath from the breathing waveform. And inspiratory band. In addition, the breathing waveform may be very weak or distorted under the influence of external low-frequency disturbances in some scenarios.
因此，利用上述方式获取呼吸波形，一方面难以判断实际呼气和吸气过程，另一方面，在某些临床场景，例如需要计算呼气和吸气时间比，需要分析呼气和吸气时间的心冲击图特征，需要分析呼气和吸气时间的心脏收缩时间特征情况时，难以满足实际的分析计算需求。Therefore, it is difficult to determine the actual expiratory and inspiratory process by using the above methods to obtain the respiratory waveform. On the other hand, in some clinical scenarios, for example, the ratio of expiratory and inspiratory time needs to be calculated, and the expiratory and inspiratory time needs to be analyzed. When it is necessary to analyze the characteristics of the systolic time of exhalation and inspiratory time, it is difficult to meet the actual analysis and calculation requirements.
技术问题technical problem
本发明的目的在于提供一种呼吸信号的提取方法、装置、计算机可读存储介质、处理设备和***，旨在解决现有技术获取呼吸波形的方法难以判断实际呼气和吸气过程，某些场景下呼吸波形可能非常微弱或者受外界低频扰动影响而发生畸变的问题。The purpose of the present invention is to provide a method, a device, a computer-readable storage medium, a processing device, and a system for extracting respiratory signals, which are intended to solve the difficulty in determining the actual exhalation and inhalation process in the prior art method for obtaining respiratory waveforms. The breathing waveform in the scene may be very weak or distorted due to the influence of external low-frequency disturbances.
技术解决方案Technical solutions
第一方面，本发明提供了一种呼吸信号的提取方法，所述方法包括：In a first aspect, the present invention provides a method for extracting a breathing signal, the method comprising:
获取心脏搏动监测信号的波形；Obtain the waveform of the heart beat monitoring signal;
根据心脏搏动监测信号的波形获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。Obtain the time interval of the same characteristic event according to the waveform of the heart beat monitoring signal, and obtain the breathing waveform according to the time interval of the same characteristic event.
第二方面，本发明提供了一种呼吸信号的提取装置，所述提取装置包括：In a second aspect, the present invention provides a device for extracting respiratory signals, the extraction device comprising:
获取模块，用于获取心脏搏动监测信号的波形；和An acquisition module for acquiring a waveform of a heart beat monitoring signal; and
呼吸波形获取模块，用于根据心脏搏动监测信号的波形获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。The breathing waveform acquisition module is configured to obtain the time interval of the same characteristic event according to the waveform of the heart beat monitoring signal, and obtain the breathing waveform according to the time interval of the same characteristic event.
第三方面，本发明提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上述的呼吸信号的提取方法的步骤。In a third aspect, the present invention provides a computer-readable storage medium that stores a computer program that, when executed by a processor, implements the steps of the breathing signal extraction method described above.
第四方面，本发明提供了一种呼吸信号的提取处理设备，包括：In a fourth aspect, the present invention provides a breathing signal extraction processing device, including:
一个或多个处理器；One or more processors;
存储器；以及Memory; and
一个或多个计算机程序，其中所述一个或多个计算机程序被存储在所述存储器中，并且被配置成由所述一个或多个处理器执行，所述处理器执行所述计算机程序时实现如上述的呼吸信号的提取方法的步骤。One or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, which are implemented when the processors execute the computer programs The steps of the method for extracting a breathing signal as described above.
第五方面，本发明提供了一种呼吸信号的提取***，所述提取***包括：In a fifth aspect, the present invention provides a respiratory signal extraction system, the extraction system includes:
生成模块，被配置为用于生成心脏搏动监测信号的波形；和A generation module configured to generate a waveform of a heart beat monitoring signal; and
与生成模块连接的，如上述的呼吸信号的提取处理设备。An extraction processing device connected to the generation module, such as the above-mentioned breathing signal.
有益效果Beneficial effect
在本发明中，由于获取心脏搏动监测信号的波形，然后根据心脏搏动监测信号的波形获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。因此可以防止部分场景下呼吸信号微弱或外界低频扰动而引起呼吸信号受影响甚至发生畸变，本发明可以更准确的获取呼吸信号，而且通过本发明获取的呼吸波形的上升或下降能直接判断出呼气或吸气过程，且能更方便地将呼吸信号与心脏有关的参数结合进行临床分析计算，以满足更多的临床需求。In the present invention, the waveform of the heart beat monitoring signal is obtained, and then the time interval of the same characteristic event is obtained according to the waveform of the heart beat monitoring signal, and the breathing waveform is obtained according to the time interval of the same characteristic event. Therefore, it is possible to prevent the breathing signal from being affected or even distorted due to the weak breathing signal or the external low-frequency disturbance in some scenes. The present invention can more accurately obtain the breathing signal, and the rise or fall of the breathing waveform obtained by the invention can directly determine the call In the process of inhalation or inhalation, it is more convenient to combine the respiratory signal with the parameters related to the heart for clinical analysis and calculation to meet more clinical needs.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是本发明实施例一提供的呼吸信号的提取方法的流程图。FIG. 1 is a flowchart of a method for extracting a breathing signal according to a first embodiment of the present invention.
图2所示为根据原始振动信号生成BCG信号的时域波形的示意图，其中，图2(a)是原始振动信号波形示意图，图2(b)是BCG信号的时域波形示意图。FIG. 2 is a schematic diagram of generating a time-domain waveform of a BCG signal based on an original vibration signal, wherein FIG. 2 (a) is a schematic diagram of an original vibration signal waveform, and FIG. 2 (b) is a schematic diagram of a time-domain waveform of the BCG signal.
图3所示为根据BCG信号的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图，其中，图3(a)是BCG信号的时域波形示意图，图3(b)是相同特征事件的时间间隔示意图，图3(c)是呼吸波形示意图。FIG. 3 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the BCG signal, and obtaining the breathing waveform according to the change of the time interval of the same characteristic event with time. FIG. 3 (a) is a diagram of the BCG signal. Schematic diagram of time domain waveforms, Fig. 3 (b) is a schematic diagram of time intervals of the same characteristic events, and Fig. 3 (c) is a schematic diagram of respiratory waveforms.
图4所示为根据原始振动信号生成BCG信号的时域波形，BCG信号经二阶微分变换得到的波形的示意图，其中，图4(a)是原始振动信号波形示意图，图4(b)是BCG信号的时域波形示意图，图4(c)是BCG信号经二阶微分变换得到的波形示意图。Figure 4 shows the time-domain waveform of the BCG signal generated from the original vibration signal. The waveform of the BCG signal obtained by the second-order differential transformation is a schematic diagram, where Figure 4 (a) is a schematic diagram of the original vibration signal waveform, and Figure 4 (b) is A schematic diagram of the time domain waveform of the BCG signal. FIG. 4 (c) is a schematic diagram of the waveform of the BCG signal obtained through the second-order differential transformation.
图5所示为根据BCG信号经二阶微分变换后的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图，其中，图5(a)是BCG信号经二阶微分变换得到的波形示意图，图5(b) 是相同特征事件的时间间隔示意图，图5(c)是呼吸波形示意图。FIG. 5 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peaks of the waveform of the second-order differential transformation of the BCG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event. FIG. 5 ( a) is a waveform diagram of the BCG signal obtained by second-order differential transformation. FIG. 5 (b) is a time interval diagram of the same characteristic event, and FIG. 5 (c) is a breathing waveform diagram.
图6所示为根据ECG信号的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图，其中，图6(a)是ECG信号的波形示意图，图6(b)是相同特征事件的时间间隔示意图，图6(c)是呼吸波形示意图。FIG. 6 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the ECG signal, and obtaining the breathing waveform according to the change of the time interval of the same characteristic event with time. Figure 6 (b) shows the time interval of the same characteristic event, and Figure 6 (c) shows the respiratory waveform.
图7所示为根据PPG信号的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图，其中，图7(a)是PPG信号波形示意图，图7(b)是相同特征事件的时间间隔示意图，图7(c)是呼吸波形示意图。FIG. 7 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the PPG signal, and obtaining the breathing waveform according to the change of the time interval of the same characteristic event with time, wherein FIG. 7 (a) is the waveform of the PPG signal A schematic diagram, FIG. 7 (b) is a time interval diagram of the same characteristic event, and FIG. 7 (c) is a breathing waveform diagram.
图8所示为根据SCG信号的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图，其中，图8(a)是SCG信号波形示意图，图8(b)是相同特征事件的时间间隔示意图，图8(c)是呼吸波形示意图。FIG. 8 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the SCG signal, and obtaining the breathing waveform according to the change of the time interval of the same characteristic event with time, wherein FIG. 8 (a) is the waveform of the SCG signal 8 (b) is a time interval diagram of the same characteristic event, and FIG. 8 (c) is a breathing waveform diagram.
图9是本发明实施例二提供的呼吸信号的提取装置的功能模块框图。FIG. 9 is a functional block diagram of a breathing signal extraction device provided by Embodiment 2 of the present invention.
图10是本发明实施例四提供的呼吸信号的提取处理设备的具体结构框图。FIG. 10 is a specific structural block diagram of a breathing signal extraction processing device provided in Embodiment 4 of the present invention.
图11是本发明实施例五提供的呼吸信号的提取***的具体结构框图。FIG. 11 is a specific structural block diagram of a breathing signal extraction system provided by Embodiment 5 of the present invention.
本发明的最佳实施方式Best Mode of the Invention
为了使本发明的目的、技术方案及有益效果更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。In order to make the objectives, technical solutions, and beneficial effects of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.
为了说明本发明所述的技术方案，下面通过具体实施例来进行说明。In order to explain the technical solution of the present invention, the following description is made through specific embodiments.
实施例一：Embodiment one:
请参阅图1，本发明实施例一提供的呼吸信号的提取方法包括以下步骤：需注意的是，若有实质上相同的结果，本发明的呼吸信号的提取方法并不以图1所示的流程顺序为限。Please refer to FIG. 1. The breathing signal extraction method provided by the first embodiment of the present invention includes the following steps: It should be noted that if there is substantially the same result, the breathing signal extraction method of the present invention is not as shown in FIG. Process sequence is limited.
S101、获取心脏搏动监测信号的波形。S101. Obtain a waveform of a heart beat monitoring signal.
在本发明实施例一中，心脏搏动监测信号可以是心冲击图（Ballistocardiogram，BCG）信号、心电图（Electrocardiogram，ECG）信号、心音图（Phonocardiogram， PCG）信号、心震图（Seismocardiogram，SCG）信号、光电容积脉搏波（Photoplethysmograph，PPG）等。In the first embodiment of the present invention, the heartbeat monitoring signals may be Ballistocardiogram (BCG) signals, Electrocardiogram (ECG) signals, Phonocardiogram (PCG) signals, and Seismocardiogram (SCG) signals. Photoelectric volume pulse wave (Photoplethysmograph, PPG).
当心脏搏动监测信号为BCG信号、PCG信号或SCG信号时，所述心脏搏动监测信号通过振动传感器获得。当心脏搏动监测信号为ECG信号时，所述心脏搏动监测信号通过心电图机获得；当心脏搏动监测信号为PPG信号时，所述心脏搏动监测信号通过PPG信号采集器获得。When the heart beat monitoring signal is a BCG signal, a PCG signal or an SCG signal, the heart beat monitoring signal is obtained through a vibration sensor. When the heart beat monitoring signal is an ECG signal, the heart beat monitoring signal is obtained through an electrocardiograph; when the heart beat monitoring signal is a PPG signal, the heart beat monitoring signal is obtained through a PPG signal collector.
在本发明实施例一中，振动传感器可以是加速度传感器、速度传感器、位移传感器、压力传感器、应变传感器、或者是以加速度、速度、压力、或位移为基础将物理量等效性转换的传感器（例如静电荷敏感传感器、充气式微动传感器、雷达传感器等）中的一种或多种。其中，应变传感器可以是光纤应变传感器。In the first embodiment of the present invention, the vibration sensor may be an acceleration sensor, a speed sensor, a displacement sensor, a pressure sensor, a strain sensor, or a sensor that converts the equivalent of a physical quantity equivalently based on acceleration, speed, pressure, or displacement (for example, Electrostatic charge sensitive sensors, inflatable micro-motion sensors, radar sensors, etc.). The strain sensor may be an optical fiber strain sensor.
振动传感器可以放置于平躺仰卧的人体背后的接触面、在预定倾斜角范围仰卧的人体背后的接触面、轮椅或其它可倚靠物体的倚卧人体背后的接触面等。The vibration sensor can be placed on a contact surface behind a human lying on his back, a contact surface behind a human lying on his back within a predetermined range of inclination angles, a contact surface behind a human lying on a wheelchair or other reclining object, and the like.
机体可以是进行生命体征信号监测的生命体。例如医院患者、被看护人员（例如年老者、被监禁者等）等。一般地，为保证所采集原始振动信号的质量，所述机体需要在安静的状态下进行测量。The living body may be a living body performing vital sign signal monitoring. For example, hospital patients, caregivers (such as elderly, imprisoned, etc.). Generally, in order to ensure the quality of the collected original vibration signals, the body needs to be measured in a quiet state.
心脏搏动监测信号的波形表征心脏搏动过程引发机体振动作用而被振动传感器检测、记录的信号。The waveform of the heart beat monitoring signal represents a signal that is detected and recorded by a vibration sensor when the heart beat process causes the body to vibrate.
当所述心脏搏动监测信号是通过振动传感器获得时，S101具体可以为：对所述振动传感器获得的原始振动信号进行滤波和缩放以生成心脏搏动监测信号波形。When the heartbeat monitoring signal is obtained through a vibration sensor, S101 may specifically be: filtering and scaling the original vibration signal obtained by the vibration sensor to generate a heartbeat monitoring signal waveform.
对原始振动信号进行滤波时，可根据对滤波后信号特征的需求采用IIR滤波器、FIR滤波器、小波滤波器、零相位双向滤波器、多项式拟合平滑滤波器等中的一种或多种组合，对原始振动信号进行滤波去噪。When filtering the original vibration signal, one or more of IIR filter, FIR filter, wavelet filter, zero-phase bidirectional filter, polynomial fitting smoothing filter, etc. can be used according to the requirements of the filtered signal characteristics. Combination to filter and denoise the original vibration signal.
在对原始振动信号进行滤波时，还可以包括以下步骤：When filtering the original vibration signal, the following steps may also be included:
判断原始振动信号是否携带工频干扰信号，如果有，则通过工频陷波器滤除工频噪声。Determine whether the original vibration signal carries a power frequency interference signal, and if so, filter the power frequency noise through a power frequency notch.
如图2所示为根据原始振动信号生成BCG信号的时域波形的示意图。每个波形特征明显且一致性良好、周期规律、轮廓清晰、基线平稳。通过数据特征可以提炼出BCG信号的 “J”峰。所述“J”峰具备如下特性：较为狭长的尖峰，且在该尖峰结束后会有一个较为剧烈的下降沿，逐渐降至该心拍的波形最低点。该“J”峰表征射血引发的振动作用被振动传感器敏感到的加速度、压力、位移中其中一种的最大值。以滤波后的BCG信号的“J”峰进行心拍划分，此数据包含了1~30号共30个“J”峰。Fig. 2 is a schematic diagram of generating a time-domain waveform of a BCG signal based on an original vibration signal. Each waveform has obvious characteristics and good consistency, regular periodicity, clear outline, and stable baseline. The “J” peak of the BCG signal can be extracted from the data characteristics. The "J" peak has the following characteristics: a relatively narrow spike, and a sharper falling edge after the spike ends, gradually decreasing to the lowest point of the waveform of the heartbeat. The "J" peak represents the maximum of one of acceleration, pressure, and displacement that the vibration effect caused by ejection is sensitive to by the vibration sensor. The heartbeat is divided by the "J" peaks of the filtered BCG signal. This data includes a total of 30 "J" peaks from 1 to 30.
S102、根据心脏搏动监测信号的波形获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。S102. Obtain the time interval of the same characteristic event according to the waveform of the heart beat monitoring signal, and obtain the breathing waveform according to the time interval of the same characteristic event.
在本发明实施例一中，S102具体可以为：In the first embodiment of the present invention, S102 may specifically be:
根据心脏搏动监测信号的波形的相同特征峰/谷，或者根据心脏搏动监测信号的波形进行变换后的波形的相同特征峰/谷获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。Obtain the time interval of the same characteristic event according to the same characteristic peak / valley of the waveform of the heart beat monitoring signal or the waveform of the same characteristic peak / valley after the waveform of the heart beat monitoring signal is transformed, and according to the time interval of the same characteristic event, The change in time acquires the breathing waveform.
其中，心脏搏动监测信号的波形进行变换后的波形可以是：心脏搏动监测信号的波形经积分变换、微分变换（例如二阶微分变换）等不影响其时域信号上各相同特征事件的时间间隔的分布特征的变换方式的波形。The waveform of the heart beat monitoring signal after transformation may be: the time interval of the heart beat monitoring signal waveform that does not affect the same characteristic events on its time domain signal through integral transformation, differential transformation (such as second-order differential transformation), etc. The waveform of the transformation of the distribution characteristics.
根据相同特征事件的时间间隔随时间的变化获取呼吸波形可以是：基于相同特征事件的时间间隔随时间的变化，采用线性插值、三次样条拟合、多项式拟合等方式提取呼吸波形。Obtaining a breathing waveform according to the change of the time interval of the same characteristic event over time may be: based on the change of the time interval of the same characteristic event over time, extracting the breathing waveform using linear interpolation, cubic spline fitting, polynomial fitting, and the like.
根据心脏搏动监测信号的波形的相同特征峰/谷，或者根据心脏搏动监测信号的波形进行变换后的波形的相同特征峰/谷获取相同特征事件的时间间隔具体可以为：The time interval for obtaining the same characteristic event according to the same characteristic peak / valley of the waveform of the heart beat monitoring signal, or the waveform of the same characteristic peak / valley of the transformed waveform according to the waveform of the heart beat monitoring signal may specifically be:
基于心脏搏动监测信号的波形或者心脏搏动监测信号的波形进行变换后的波形检测出各个相同特征峰/谷，计算出各个相同特征峰/谷与相邻前序相同特征峰/谷的时间间隔，将所述时间间隔作为该相同特征峰/谷对应的相同特征事件的时间间隔。Based on the waveform of the heart beat monitoring signal or the waveform of the heart beat monitoring signal after transformation, each identical characteristic peak / valley is detected, and the time interval between each identical characteristic peak / valley and the same characteristic peak / valley of the adjacent preamble is calculated. The time interval is taken as the time interval of the same characteristic event corresponding to the same characteristic peak / valley.
如图3所示为根据BCG信号的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图。    FIG. 3 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the BCG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event. Zh
由图3可以看出，根据BCG信号容易检测得到各心拍的“J”峰。受心肺耦合作用的影响，“J”峰随着呼气和吸气过程也呈现“高低起伏”的轮廓。如图3所示，基于BCG信号检测出各个“J”峰之后，计算出各“J”峰与相邻前序“J”峰的时间间隔，即“J-J” 时间间隔，即相同特征事件的时间间隔。各时间间隔已在图3中标识，第1个“J”峰处的“J-J” 时间间隔为与相邻前序“J”峰的时间间隔。将各“J”峰所在时刻作为横坐标，“J-J”时间间隔作为纵坐标，绘制随时间变化的相同特征事件的时间间隔变化。图3所示的呼吸波形是基于三次样条拟合提取得到的。与原始振动信号的呼吸轮廓相比，所提取的呼吸波形的频率基本一致，可以计算基本参数呼吸频率，且根据心肺耦合影响，可以判断波形往低处走为吸气过程，波形往高处走为呼气过程，可以基于此做更多需要拆解呼/吸气阶段信号的计算分析。此处以“J”峰作为特征峰进行心拍划分，也可以根据“J”峰左右侧的“I”谷和“K”谷、对应于心脏收缩期的特征峰谷群（MC、AVO等）、对应于心脏舒张期的特征峰谷群（AVC、MO等）、以及其他特征明显的峰谷来进行划分。It can be seen from FIG. 3 that the “J” peak of each heartbeat can be easily detected based on the BCG signal. Affected by the cardiopulmonary coupling effect, the "J" peak also exhibits a "high and low" contour along with the exhalation and inspiration process. As shown in Figure 3, after detecting each "J" peak based on the BCG signal, the time interval between each "J" peak and the adjacent pre-order "J" peak is calculated, that is, the "JJ" time interval, that is, the same characteristic event. time interval. Each time interval has been identified in Figure 3. The "J-J" time interval at the first "J" peak is the time interval with the adjacent pre-order "J" peak. The time at which each "J" peak is located is taken as the abscissa, and the "J-J" time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted. The respiratory waveform shown in Figure 3 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same. The basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part For the exhalation process, you can do more calculations and analysis based on the signal during the exhalation / inhalation phase. Here, the “J” peak is used as the characteristic peak for heartbeat division. The “I” valley and the “K” valley on the left and right sides of the “J” peak, the characteristic peak and valley groups (MC, AVO, etc.) corresponding to the systole, The peaks and valleys (AVC, MO, etc.) corresponding to the diastolic phase and other distinctive peaks and valleys are used for classification.
如图4所示为根据原始振动信号生成BCG信号的时域波形，且将BCG信号经二阶微分变换得到的波形的示意图。经二阶微分变换得到的波形也具有明显的特征峰，狭长高耸且明显高于单个心拍内的其他峰，例如称之为“J^”峰。As shown in FIG. 4, a time-domain waveform of a BCG signal is generated based on the original vibration signal, and a waveform obtained by subjecting the BCG signal to second-order differential transformation is illustrated. The waveform obtained by the second-order differential transformation also has obvious characteristic peaks, which are long and narrow and are significantly higher than other peaks in a single heartbeat, such as the "J ^" peak.
如图5所示为根据BCG信号经二阶微分变换后的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图。如图5所示，基于BCG信号经二阶微分变换后的波形检测出各个“J^”峰之后，计算出各“J^”峰与相邻前序“J^”峰的时间间隔，即“J^- J^” 时间间隔，即相同特征事件的时间间隔。各时间间隔已在图5中标识，第1个“J^”峰处的“J^- J^” 时间间隔为与相邻前序“J^”峰的时间间隔。将各“J^”峰所在时刻作为横坐标，“J^- J^”时间间隔作为纵坐标，绘制随时间变化的相同特征事件的时间间隔变化。图5所示的呼吸波形是基于三次样条拟合提取得到的。与原始振动信号的呼吸轮廓相比，所提取的呼吸波形的频率基本一致，可以计算基本参数呼吸频率，且根据心肺耦合影响，可以判断波形往低处走为吸气过程，波形往高处走为呼气过程，可以基于此做更多需要拆解呼/吸气阶段信号的计算分析。此处以“J^”峰作为特征峰进行心拍划分，也可以根据信号特性基于特征明显的其他峰或谷来进行划分。FIG. 5 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the BCG signal subjected to the second-order differential transformation, and obtaining the breathing waveform according to the time interval of the same characteristic event. As shown in FIG. 5, after detecting each “J ^” peak based on the waveform of the second-order differential transformation of the BCG signal, the time interval between each “J ^” peak and the adjacent pre-order “J ^” peak is calculated, that is, "J ^-J ^" time interval, that is, the time interval of the same characteristic event. Each time interval has been identified in FIG. 5. The "J ^ -J ^" time interval at the first "J ^" peak is the time interval with the adjacent pre-order "J ^" peak. The time at which each "J ^" peak is located is taken as the abscissa, and the "J ^-J ^" time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted. The breathing waveform shown in Figure 5 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same. The basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part. For the exhalation process, you can do more calculations and analysis based on the signal during the exhalation / inhalation phase. Here, the “J ^” peak is used as a characteristic peak to perform heartbeat division, and the signal characteristics may also be used to perform division based on other peaks or valleys with obvious characteristics.
如图6所示为根据ECG信号的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图。    FIG. 6 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the ECG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event. Zh
由图6可以看出，最明显的特征峰为狭长高耸的“R”峰。根据ECG信号容易检测得到各心拍的“R”峰。受心肺耦合作用的影响，“R”峰随着呼气和吸气过程也呈现“高低起伏”的轮廓。如图6所示，基于ECG信号检测出各个“R”峰之后，计算出各“R”峰与相邻前序“R”峰的时间间隔，即“R - R”时间间隔，即相同特征事件的时间间隔。各时间间隔已在图6中标识，第1个“R”峰处的“R - R” 时间间隔为与相邻前序“R”峰的时间间隔。将各“R”峰所在时刻作为横坐标，“R - R”时间间隔作为纵坐标，绘制随时间变化的相同特征事件的时间间隔变化。图6所示的呼吸波形是基于三次样条拟合提取得到的。与原始振动信号的呼吸轮廓相比，所提取的呼吸波形的频率基本一致，可以计算基本参数呼吸频率，且根据心肺耦合影响，可以判断波形往低处走为吸气过程，波形往高处走为呼气过程，可以基于此做更多需要拆解呼/吸气阶段信号的计算分析。此处以“R”峰作为特征峰进行心拍划分，也可以根据信号特性基于特征明显的其他峰或谷来进行划分。也可以将ECG信号经积分变换、微分变换（例如二阶微分变换）等不影响其时域信号上各相同特征事件的时间间隔的分布特征的变换方式进行变换，然后基于变换后ECG信号的特征峰谷进行心拍划分。图6所示ECG信号为通过五导联采集的II导信号，其他导联信号可基于该实施例进行类推。It can be seen from Fig. 6 that the most obvious characteristic peak is a narrow and tall "R" peak. The “R” peak of each heartbeat can be easily detected based on the ECG signal. Affected by the cardiopulmonary coupling effect, the "R" peak also presents a "high and low" profile along with the exhalation and inspiration process. As shown in Figure 6, after detecting each "R" peak based on the ECG signal, the time interval between each "R" peak and the adjacent preamble "R" peak is calculated, that is, the "R-R" time interval, that is, the same characteristics The time interval of the event. Each time interval has been identified in Figure 6. The "R-R" time interval at the first "R" peak is the time interval with the adjacent pre-order "R" peak. The time at which each "R" peak is located is taken as the abscissa, and the "R-R" time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted. The breathing waveform shown in Figure 6 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same. The basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part. For the exhalation process, you can do more calculations and analysis based on the signal during the exhalation / inhalation phase. Here, the “R” peak is used as a characteristic peak to perform heartbeat division, and it may also be classified based on other peaks or valleys with obvious characteristics according to signal characteristics. The ECG signal can also be transformed by integral transformation, differential transformation (such as second-order differential transformation), and other transformation methods that do not affect the distribution characteristics of the time interval of the same characteristic event on its time domain signal, and then based on the characteristics of the transformed ECG signal Peaks and valleys are divided into heartbeats. The ECG signal shown in FIG. 6 is a lead II signal collected through five leads, and other lead signals can be analogized based on this embodiment.
如图7所示为根据PPG信号的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图。    FIG. 7 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the PPG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event. Zh
由图7可以看出，最明显的特征峰为狭长高耸的“P”峰。根据PPG信号容易检测得到各心拍的“P”峰。受心肺耦合作用的影响，“P”峰随着呼气和吸气过程也呈现“高低起伏”的轮廓。如图7所示，基于PPG信号检测出各个“P”峰之后，计算出各“P”峰与相邻前序“P”峰的时间间隔，即“P - P”时间间隔，即相同特征事件的时间间隔。各时间间隔已在图7中标识，第1个“P”峰处的“P - P” 时间间隔为与相邻前序“P”峰的时间间隔。将各“P”峰所在时刻作为横坐标，“P - P”时间间隔作为纵坐标，绘制随时间变化的相同特征事件的时间间隔变化。图7所示的呼吸波形是基于三次样条拟合提取得到的。与原始振动信号的呼吸轮廓相比，所提取的呼吸波形的频率基本一致，可以计算基本参数呼吸频率，且根据心肺耦合影响，可以判断波形往低处走为吸气过程，波形往高处走为呼气过程，可以基于此做更多需要拆解呼/吸气阶段信号的计算分析。此处以“P”峰作为特征峰进行心拍划分，也可以根据信号特性基于特征明显的其他峰或谷来进行划分。也可以将PPG信号经积分变换、微分变换（例如二阶微分变换）等不影响其时域信号上各相同特征事件的时间间隔的分布特征的变换方式进行变换，然后基于变换后PPG信号的特征峰谷进行划分。图7所示PPG信号为通过手指采集的PPG信号，其他部位信号可基于该实施例进行类推。It can be seen from FIG. 7 that the most obvious characteristic peak is the narrow and tall “P” peak. It is easy to detect the “P” peak of each heartbeat based on the PPG signal. Affected by the cardiopulmonary coupling effect, the "P" peak also presents a "high and low" contour along with the exhalation and inspiration process. As shown in Fig. 7, after detecting each "P" peak based on the PPG signal, the time interval between each "P" peak and the adjacent previous "P" peak is calculated, that is, the "P-P" time interval, that is, the same characteristics The time interval of the event. Each time interval has been identified in Figure 7. The "P-P" time interval at the first "P" peak is the time interval from the adjacent "P" peak. The time at which each "P" peak is located is taken as the abscissa, and the "P-P" time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted. The breathing waveform shown in Figure 7 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same. The basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part. For the exhalation process, you can do more calculations and analysis based on the signal during the exhalation / inhalation phase. Here, the “P” peak is used as a characteristic peak to perform heartbeat division, and it can also be divided based on other peaks or valleys with obvious characteristics according to the signal characteristics. The PPG signal can also be transformed by integral transformation, differential transformation (such as second-order differential transformation), and other transformation methods that do not affect the distribution characteristics of the time interval of the same characteristic event on its time domain signal, and then based on the characteristics of the transformed PPG signal Peaks and valleys are divided. The PPG signal shown in FIG. 7 is a PPG signal collected by a finger, and signals of other parts can be analogized based on this embodiment.
如图8所示为根据SCG信号的波形的特征峰获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形的示意图。    FIG. 8 is a schematic diagram of obtaining the time interval of the same characteristic event according to the characteristic peak of the waveform of the SCG signal, and obtaining the breathing waveform according to the time interval of the same characteristic event. Zh
由图8可以看出，最明显的特征峰为狭长高耸的“S”峰。根据SCG信号容易检测得到各心拍的“S”峰。受心肺耦合作用的影响，“S”峰随着呼气和吸气过程也呈现“高低起伏”的轮廓。如图8所示，基于SCG信号检测出各个“S”峰之后，计算出各“S”峰与相邻前序“S”峰的时间间隔，即“S - S”时间间隔，即相同特征事件的时间间隔。各时间间隔已在图8中标识，第1个“S”峰处的“S - S” 时间间隔为与相邻前序“S”峰的时间间隔。将各“S”峰所在时刻作为横坐标，“S - S”时间间隔作为纵坐标，绘制随时间变化的相同特征事件的时间间隔变化。图8所示的呼吸波形是基于三次样条拟合提取得到的。与原始振动信号的呼吸轮廓相比，所提取的呼吸波形的频率基本一致，可以计算基本参数呼吸频率，且根据心肺耦合影响，可以判断波形往低处走为吸气过程，波形往高处走为呼气过程，可以基于此做更多需要拆解呼/吸气阶段信号的计算分析。此处以“S”峰作为特征峰进行心拍划分，也可以根据信号特性基于特征明显的其他峰或谷来进行划分。也可以将SCG信号经积分变换、微分变换（例如二阶微分变换）等不影响其时域信号上各相同特征事件的时间间隔的分布特征的变换方式进行变换，然后基于变换后SCG信号的特征峰谷进行划分。图8所示SCG信号为通过平躺时胸前心尖采集的SCG信号，其他部位信号可基于该实施例进行类推。It can be seen from Fig. 8 that the most obvious characteristic peak is the narrow and tall "S" peak. It is easy to detect the "S" peak of each heartbeat based on the SCG signal. Affected by the cardiopulmonary coupling effect, the "S" peak also presents a "high and low" contour along with the exhalation and inspiration process. As shown in FIG. 8, after detecting each “S” peak based on the SCG signal, the time interval between each “S” peak and the adjacent “S” peak is calculated, that is, the “S-S” time interval, that is, the same characteristics The time interval of the event. Each time interval has been identified in Figure 8. The "S-S" time interval at the first "S" peak is the time interval from the adjacent "S" peak. The time at which each “S” peak is located is taken as the abscissa, and the “S-S” time interval is taken as the ordinate, and the time interval change of the same characteristic event that changes with time is plotted. The respiratory waveform shown in Figure 8 was extracted based on cubic spline fitting. Compared with the breathing profile of the original vibration signal, the frequency of the extracted breathing waveform is basically the same. The basic parameter breathing frequency can be calculated, and according to the influence of the cardiopulmonary coupling, it can be judged that the waveform goes to the lower part as the inhalation process, and the waveform goes to the higher part. For the exhalation process, you can do more calculations and analysis based on the signal during the exhalation / inhalation phase. Here, the “S” peak is used as a characteristic peak to perform heartbeat division, and it may also be classified based on other peaks or valleys with obvious characteristics according to signal characteristics. The SCG signal can also be transformed by integral transformation, differential transformation (such as second-order differential transformation), and other transformation methods that do not affect the distribution characteristics of the time interval of the same characteristic event on its time domain signal, and then based on the characteristics of the transformed SCG signal Peaks and valleys are divided. The SCG signal shown in FIG. 8 is an SCG signal collected through the apex of the chest when lying down, and the signals of other parts can be analogized based on this embodiment.
实施例二：Embodiment two:
请参阅图9，本发明实施例二提供的呼吸信号的提取装置包括：Referring to FIG. 9, a breathing signal extraction device provided in Embodiment 2 of the present invention includes:
获取模块21，用于获取心脏搏动监测信号的波形；An acquisition module 21 for acquiring a waveform of a heart beat monitoring signal;
呼吸波形获取模块22，用于根据心脏搏动监测信号的波形获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。The respiratory waveform acquisition module 22 is configured to acquire the time interval of the same characteristic event according to the waveform of the heart beat monitoring signal, and acquire the respiratory waveform according to the time interval of the same characteristic event.
本发明实施例二提供的呼吸信号的提取装置及本发明实施例一提供的呼吸信号的提取方法属于同一构思，其具体实现过程详见说明书全文，此处不再赘述。The breathing signal extraction device provided in the second embodiment of the present invention and the breathing signal extraction method provided in the first embodiment of the present invention belong to the same concept, and the specific implementation process thereof is detailed in the entire description, and will not be repeated here.
实施例三：Embodiment three:
本发明实施例三提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如本发明实施例一提供的呼吸信号的提取方法的步骤。Embodiment 3 of the present invention provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the breathing signal extraction method provided by Embodiment 1 of the present invention is implemented. A step of.
实施例四：Embodiment 4:
如图10所示，本发明实施例四提供了一种呼吸信号的提取处理设备100，所述呼吸信号的提取处理设备100包括：一个或多个处理器101、存储器102、以及一个或多个计算机程序，其中所述处理器101和所述存储器102通过总线连接，所述一个或多个计算机程序被存储在所述存储器102中，并且被配置成由所述一个或多个处理器101执行，所述处理器101执行所述计算机程序时实现如本发明实施例一提供的所述呼吸信号的提取方法的步骤。As shown in FIG. 10, Embodiment 4 of the present invention provides a breathing signal extraction processing device 100. The breathing signal extraction processing device 100 includes: one or more processors 101, a memory 102, and one or more A computer program in which the processor 101 and the memory 102 are connected by a bus, the one or more computer programs are stored in the memory 102 and configured to be executed by the one or more processors 101 When the processor 101 executes the computer program, the steps of the method for extracting the breathing signal provided by the first embodiment of the present invention are implemented.
实施例五：Embodiment 5:
请参阅图11，本发明实施例五提供的呼吸信号的提取***包括：Referring to FIG. 11, a breathing signal extraction system provided in Embodiment 5 of the present invention includes:
生成模块11，被配置为用于生成心脏搏动监测信号的波形；和A generation module 11 configured to generate a waveform of a heart beat monitoring signal; and
与生成模块连接的，如本发明实施例四提供的呼吸信号的提取处理设备100。The breathing signal extraction and processing device 100, which is connected to the generating module, is provided by the fourth embodiment of the present invention.
在本发明实施例五中，当心脏搏动监测信号为BCG信号、PCG信号或SCG信号时，生成模块是振动传感器；当心脏搏动监测信号为ECG信号时，生成模块是心电图机；当心脏搏动监测信号为PPG信号时，生成模块是PPG信号采集器。In the fifth embodiment of the present invention, when the heartbeat monitoring signal is a BCG signal, a PCG signal or an SCG signal, the generating module is a vibration sensor; when the heartbeat monitoring signal is an ECG signal, the generating module is an electrocardiograph; when the heartbeat monitoring is When the signal is a PPG signal, the generating module is a PPG signal collector.
在本发明中，由于获取心脏搏动监测信号的波形，然后根据心脏搏动监测信号的波形获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。因此可以防止部分场景下呼吸信号微弱或外界低频扰动而引起呼吸信号受影响甚至发生畸变，本发明可以更准确的获取呼吸信号，而且通过本发明获取的呼吸波形的上升或下降能直接判断出呼气或吸气过程，且能更方便地将呼吸信号与心脏有关的参数结合进行临床分析计算，以满足更多的临床需求。In the present invention, the waveform of the heart beat monitoring signal is obtained, and then the time interval of the same characteristic event is obtained according to the waveform of the heart beat monitoring signal, and the breathing waveform is obtained according to the time interval of the same characteristic event. Therefore, it is possible to prevent the breathing signal from being affected or even distorted due to the weak breathing signal or the external low-frequency disturbance in some scenes. The present invention can more accurately obtain the breathing signal, and the rise or fall of the breathing waveform obtained by the invention can directly determine the call In the process of inhalation or inhalation, it is more convenient to combine the respiratory signal with the parameters related to the heart for clinical analysis and calculation to meet more clinical needs.
本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质可以包括：只读存储器（ROM，Read Only Memory）、随机存取记忆体（RAM，Random Access Memory）、磁盘或光盘等。A person of ordinary skill in the art may understand that all or part of the steps in the various methods of the foregoing embodiments may be implemented by a program instructing related hardware. The program may be stored in a computer-readable storage medium. The storage medium may include: Read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks, etc.
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。The above description is only the preferred embodiments of the present invention, and is not intended to limit the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention shall be included in the protection of the present invention. Within range.

Claims (17)

  1. 一种呼吸信号的提取方法，其特征在于，所述方法包括：A method for extracting a breathing signal, wherein the method includes:
    获取心脏搏动监测信号的波形；Obtain the waveform of the heart beat monitoring signal;
    根据心脏搏动监测信号的波形获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。Obtain the time interval of the same characteristic event according to the waveform of the heart beat monitoring signal, and obtain the breathing waveform according to the time interval of the same characteristic event.
  2. 如权利要求1所述的方法，其特征在于，所述心脏搏动监测信号为ECG信号或PPG信号。The method according to claim 1, wherein the heartbeat monitoring signal is an ECG signal or a PPG signal.
  3. 如权利要求1所述的方法，其特征在于，所述心脏搏动监测信号为BCG信号、PCG信号或SCG信号。The method according to claim 1, wherein the heartbeat monitoring signal is a BCG signal, a PCG signal, or an SCG signal.
  4. 如权利要求3所述的方法，其特征在于，所述心脏搏动监测信号通过振动传感器获得，所述振动传感器是加速度传感器、速度传感器、位移传感器、压力传感器、应变传感器、或者是以加速度、速度、压力或位移为基础将物理量等效性转换的传感器中的一种或多种。The method according to claim 3, wherein the heartbeat monitoring signal is obtained through a vibration sensor, and the vibration sensor is an acceleration sensor, a speed sensor, a displacement sensor, a pressure sensor, a strain sensor, or an acceleration or a speed. One or more of the sensors that convert the equivalent of a physical quantity based on pressure, pressure, or displacement.
  5. 如权利要求4所述的方法，其特征在于，所述振动传感器放置于平躺仰卧的人体背后的接触面、在预定倾斜角范围仰卧的人体背后的接触面、或可倚靠物体的倚卧人体背后的接触面。The method according to claim 4, wherein the vibration sensor is placed on a contact surface behind a human body lying supine, a contact surface behind a human body lying supine within a predetermined tilt angle range, or a reclining human body that can lean on an object Contact surface behind.
  6. 如权利要求4所述的方法，其特征在于，所述获取心脏搏动监测信号的波形具体为：对所述振动传感器获得的原始振动信号进行滤波和缩放以生成心脏搏动监测信号波形。The method according to claim 4, wherein the acquiring the waveform of the heart beat monitoring signal is specifically: filtering and scaling the original vibration signal obtained by the vibration sensor to generate a heart beat monitoring signal waveform.
  7. 如权利要求6所述的方法，其特征在于，对原始振动信号进行滤波时，根据对滤波后信号特征的需求采用IIR滤波器、FIR滤波器、小波滤波器、零相位双向滤波器、多项式拟合平滑滤波器中的一种或多种组合，对原始振动信号进行滤波去噪。The method according to claim 6, characterized in that when filtering the original vibration signal, an IIR filter, a FIR filter, a wavelet filter, a zero-phase bidirectional filter, a polynomial simulation is used according to the requirements of the filtered signal characteristics. Combine one or more of the smoothing filters to filter and denoise the original vibration signal.
  8. 如权利要求6所述的方法，其特征在于，在对原始振动信号进行滤波时，还包括：The method according to claim 6, further comprising: when filtering the original vibration signal:
    判断原始振动信号是否携带工频干扰信号，如果有，则通过工频陷波器滤除工频噪声。Determine whether the original vibration signal carries a power frequency interference signal, and if so, filter the power frequency noise through a power frequency notch.
  9. 如权利要求1所述的方法，其特征在于，所述根据心脏搏动监测信号的波形获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形具体为：The method according to claim 1, wherein the obtaining the time interval of the same characteristic event according to the waveform of the heart beat monitoring signal, and obtaining the breathing waveform according to the change of the time interval of the same characteristic event with time are:
    根据心脏搏动监测信号的波形的相同特征峰/谷，或者根据心脏搏动监测信号的波形进行变换后的波形的相同特征峰/谷获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。Obtain the time interval of the same characteristic event according to the same characteristic peak / valley of the waveform of the heart beat monitoring signal or the waveform of the same characteristic peak / valley after the waveform of the heart beat monitoring signal is transformed, and according to the time interval of the same characteristic event, The change in time acquires the breathing waveform.
  10. 如权利要求9所述的方法，其特征在于，所述心脏搏动监测信号的波形进行变换后的波形是：所述心脏搏动监测信号的波形经不影响其时域信号上各相同特征事件的时间间隔的分布特征的变换方式的波形。The method according to claim 9, wherein the waveform of the waveform of the heart beat monitoring signal is transformed: the waveform of the heart beat monitoring signal does not affect the time of each of the same characteristic events on its time domain signal Waveform of interval distribution feature transformation.
  11. 如权利要求10所述的方法，其特征在于，所述不影响其时域信号上各相同特征事件的时间间隔的分布特征的变换方式是积分变换或微分变换。The method according to claim 10, wherein the transformation mode of the distribution characteristics that does not affect the time interval of each of the same characteristic events on the time domain signal is an integral transformation or a differential transformation.
  12. 如权利要求1所述的方法，其特征在于，所述根据相同特征事件的时间间隔随时间的变化获取呼吸波形是：基于相同特征事件的时间间隔随时间的变化，采用线性插值、三次样条拟合或多项式拟合方式提取的呼吸波形。The method according to claim 1, wherein the obtaining a breathing waveform according to a change in time interval of the same characteristic event is based on a change in time interval of the same characteristic event using time, using linear interpolation, cubic spline Respiratory waveform extracted by fitting or polynomial fitting.
  13. 如权利要求9所述的方法，其特征在于，所述根据心脏搏动监测信号的波形的相同特征峰/谷，或者根据心脏搏动监测信号的波形进行变换后的波形的相同特征峰/谷获取相同特征事件的时间间隔具体为：The method according to claim 9, characterized in that the same characteristic peak / valley of the waveform based on the waveform of the heart beat monitoring signal, or the same characteristic peak / valley of the waveform transformed according to the waveform of the heart beat monitoring signal obtains the same The time interval of the characteristic event is specifically:
    基于所述心脏搏动监测信号的波形或者心脏搏动监测信号的波形进行变换后的波形检测出各个相同特征峰/谷，计算出各个相同特征峰/谷与相邻前序相同特征峰/谷的时间间隔，将所述时间间隔作为该相同特征峰/谷对应的相同特征事件的时间间隔。Based on the waveform of the heart beat monitoring signal or the waveform of the heart beat monitoring signal after transformation, each identical characteristic peak / valley is detected, and the time of each identical characteristic peak / valley and the same characteristic peak / valley of adjacent preambles is calculated Interval, and the time interval is used as the time interval of the same characteristic event corresponding to the same characteristic peak / valley.
  14. 一种呼吸信号的提取装置，其特征在于，所述提取装置包括：A breathing signal extraction device, characterized in that the extraction device includes:
    获取模块，用于获取心脏搏动监测信号的波形；和An acquisition module for acquiring a waveform of a heart beat monitoring signal; and
    呼吸波形获取模块，用于根据心脏搏动监测信号的波形获取相同特征事件的时间间隔，并根据相同特征事件的时间间隔随时间的变化获取呼吸波形。The breathing waveform acquisition module is configured to obtain the time interval of the same characteristic event according to the waveform of the heart beat monitoring signal, and obtain the breathing waveform according to the time interval of the same characteristic event.
  15. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至13任一项所述的呼吸信号的提取方法的步骤。A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, extraction of the breathing signal according to any one of claims 1 to 13 is performed Method steps.
  16. 一种呼吸信号的提取处理设备，包括：A breathing signal extraction processing device includes:
    一个或多个处理器；One or more processors;
    存储器；以及Memory; and
    一个或多个计算机程序，其中所述一个或多个计算机程序被存储在所述存储器中，并且被配置成由所述一个或多个处理器执行，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至13任一项所述的呼吸信号的提取方法的步骤。One or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, characterized in that the processors execute the The computer program implements the steps of the breathing signal extraction method according to any one of claims 1 to 13.
  17. 一种呼吸信号的提取***，其特征在于，所述提取***包括：A respiratory signal extraction system, characterized in that the extraction system includes:
    生成模块，被配置为用于生成心脏搏动监测信号的波形；和A generation module configured to generate a waveform of a heart beat monitoring signal; and
    与所述生成模块连接的，如权利要求16所述的呼吸信号的提取处理设备。The breathing signal extraction and processing device according to claim 16 connected to the generating module.
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Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110881956B (en) * 2018-09-10 2022-06-14 深圳市大耳马科技有限公司 Heart physiological parameter measuring method, device, terminal and computer storage medium
CN110236527A (en) * 2019-07-05 2019-09-17 北京理工大学 A kind of method and device obtaining respiration information
CN111493869A (en) * 2020-04-10 2020-08-07 南京四十二科技有限公司 Ultra-bandwidth radar navigation imaging system and method based on respiratory signals
CN112363139A (en) * 2020-11-02 2021-02-12 深圳大学 Human body breathing time length detection method and device based on amplitude characteristics and storage medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102551727A (en) * 2012-01-11 2012-07-11 深圳市理邦精密仪器股份有限公司 Respiratory information detecting method and device
US20130123584A1 (en) * 2007-02-09 2013-05-16 Neuropace, Inc. Devices and methods for monitoring physiological information relating to sleep with an implantable device
CN106037743A (en) * 2016-05-19 2016-10-26 广东宝莱特医用科技股份有限公司 Method and device for extracting respiratory rate
CN108056769A (en) * 2017-11-14 2018-05-22 深圳市大耳马科技有限公司 A kind of vital sign parameter signals analysis and processing method, device and vital sign monitoring device

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150265184A1 (en) * 2014-03-20 2015-09-24 Capnia, Inc. Selection, segmentation and analysis of exhaled breath for airway disorders assessment

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130123584A1 (en) * 2007-02-09 2013-05-16 Neuropace, Inc. Devices and methods for monitoring physiological information relating to sleep with an implantable device
CN102551727A (en) * 2012-01-11 2012-07-11 深圳市理邦精密仪器股份有限公司 Respiratory information detecting method and device
CN106037743A (en) * 2016-05-19 2016-10-26 广东宝莱特医用科技股份有限公司 Method and device for extracting respiratory rate
CN108056769A (en) * 2017-11-14 2018-05-22 深圳市大耳马科技有限公司 A kind of vital sign parameter signals analysis and processing method, device and vital sign monitoring device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
WU, DAN: "Research on Non-invasive Respiratory Monitoring Method that can be Applied to Home", CHINA MASTER'S THESES FULL-TEXT DATABASE, 15 February 2011 (2011-02-15), pages 32 - 36 ; 46 ; 51, ISSN: 1674-0246 *

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