WO2020024312A1 - Procédé d'extraction de signal de respiration, appareil, dispositif de traitement et système - Google Patents

Procédé d'extraction de signal de respiration, appareil, dispositif de traitement et système 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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English (en)
Chinese (zh)
Inventor
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/fr

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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.

Abstract

La présente invention se rapporte au domaine de la médecine et fournit un système d'extraction de signal de respiration, un appareil, un dispositif de traitement et un système. Le procédé comprend les étapes suivantes : acquisition d'une forme d'onde d'un signal de surveillance pour des impulsions cardiaques ; acquisition d'un intervalle de temps entre les mêmes événements de caractéristique en fonction de la forme d'onde du signal de surveillance pour des impulsions cardiaques, et acquisition d'une forme d'onde de respiration en fonction du changement de l'intervalle de temps entre les mêmes événements de caractéristique dans le temps. La présente invention peut empêcher qu'un signal de respiration ne soit affecté ou même déformé en raison d'un signal de respiration faible ou d'une perturbation basse fréquence externe dans certains scénarios, et peut ainsi acquérir plus précisément le signal de respiration. En outre, un processus d'expiration et un processus d'inspiration peuvent être déterminés directement à partir de l'élévation et de la chute d'une forme d'onde de respiration acquise au moyen de la présente invention, et un signal de respiration peut être combiné de manière plus commode avec des paramètres cardiaques pour une analyse et un calcul cliniques, répondant à des exigences plus cliniques.
PCT/CN2018/099254 2018-08-03 2018-08-07 Procédé d'extraction de signal de respiration, appareil, dispositif de traitement et système WO2020024312A1 (fr)

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