WO2020024311A1

WO2020024311A1 - Method, apparatus, processing device and system for extracting respiratory signal

Info

Publication number: WO2020024311A1
Application number: PCT/CN2018/099248
Authority: WO
Inventors: 叶飞
Original assignee: 深圳市大耳马科技有限公司
Priority date: 2018-08-03
Filing date: 2018-08-07
Publication date: 2020-02-06
Also published as: CN109222928A; CN109222928B

Abstract

A method, apparatus, processing device and system for extracting a respiratory signal. The method comprises: acquiring a waveform of a heart beat monitoring signal (S101); and according to the waveform of the heart beat monitoring signal, acquiring a time interval between two different feature events in the same cardiac cycle, and acquiring a respiratory waveform according to a change in the time interval between the two different feature events over time (S102). By means of the method, impacts on a respiratory signal and even distortion thereof caused by a weak respiratory signal or outside low frequency disturbance in some scenarios can be prevented, the respiratory signal can be acquired more accurately, in addition, an expiration or inspiration process can be directly determined by means of rise or fall of a respiratory waveform acquired by the method, and the respiratory signal can be more conveniently combined with parameters related to the heart to carry out clinical analysis and calculation so as to meet more clinical requirements.

Description

Method, device, processing equipment and system for extracting respiratory signals

Technical field

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.

Background technique

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. 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.

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 The characteristics of the cardiac shock map, which need to analyze the characteristics of the systolic time of exhalation and inspiratory time, are difficult to meet the actual analysis and calculation requirements.

technical problem

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.

Technical solutions

In a first aspect, the present invention provides a method for extracting a breathing signal, the method comprising: acquiring a waveform of a heart beat monitoring signal; and acquiring the time of two different characteristic events in the same cardiac cycle according to the waveform of the heart beat monitoring signal. Interval, and obtain the breathing waveform based on the time interval of two different characteristic events.

In a second aspect, the present invention provides a breathing signal extraction device, the extraction device includes: an acquisition module for acquiring a waveform of a heart beat monitoring signal; and a respiratory waveform acquisition module for obtaining a waveform of a heart beat monitoring signal. Obtain the time interval of two different characteristic events, and obtain the breathing waveform according to the time interval of the two different characteristic events.

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.

In a fourth aspect, the present invention provides a respiratory signal extraction processing device, including: one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the The memory is configured to be executed by the one or more processors, and the processor executes the computer program to implement the steps of the breathing signal extraction method as described above.

In a fifth aspect, the present invention provides a breathing signal extraction system, the extraction system includes: a generating module configured to generate a waveform of a heart beat monitoring signal; and a breathing signal connected to the generating module as described above Extraction processing equipment.

Beneficial effect

In the present invention, the waveform of the heart beat monitoring signal is obtained, and then the time interval of two different characteristic events in the same cardiac cycle is obtained according to the waveform of the heart beat monitoring signal, and the time interval of the two different characteristic events varies with time according to the time Change to get the breathing waveform. 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.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a method for extracting a breathing signal according to a first embodiment of the present invention.

Figure 2 shows a schematic diagram of the original vibration signal.

FIG. 3 is a schematic diagram showing a time-domain waveform of a BCG signal generated from an original vibration signal.

FIG. 4 is a schematic diagram of obtaining time intervals of different characteristic events according to the characteristic peaks of the waveform of the BCG signal, wherein FIG. 4 (a) is a schematic diagram of the original vibration signal waveform, and FIG. 4 (b) is a schematic diagram of the time domain waveform of the BCG signal .

FIG. 5 is a schematic diagram of obtaining the time intervals of different characteristic events according to the characteristic peaks of the waveform of the BCG signal, and obtaining the breathing waveform according to the change of the time intervals of different characteristic events with time, wherein FIG. 5 (a) is the original vibration signal Figure 5 (b) is a time-domain waveform diagram of two BCG signals, Figure 5 (c) is a time interval diagram of two different characteristic events, and Figure 5 (d) is a breathing waveform diagram.

FIG. 6 shows a time-domain waveform of a BCG signal generated from the original vibration signal, and a waveform of the BCG signal obtained through second-order differential transformation.

FIG. 7 is a schematic diagram of a breathing waveform obtained based on cubic spline fitting extraction, in which FIG. 7 (a) is a schematic diagram of a waveform of an original vibration signal, a BCG signal, and a second-order differential of the BCG signal, and FIG. 7 ( b) is a time interval diagram of two different characteristic events, and FIG. 7 (c) is a schematic diagram of a breathing waveform.

FIG. 8 is a schematic diagram of generating a time-domain waveform of a BCG signal based on two original vibration signals, wherein FIG. 8 (a) is a schematic diagram of an original vibration signal waveform, and FIG. 8 (b) is a schematic of a time-domain waveform of two BCG signals. .

FIG. 9 is a schematic diagram of obtaining time intervals of different characteristic events according to the characteristic peaks of the waveforms of two BCG signals, and obtaining a breathing waveform according to the change of the time intervals of different characteristic events with time. Among them, FIG. 9 (a) is the original Figure 9 (b) is a schematic diagram of the time domain waveforms of two BCG signals, Figure 9 (c) is a schematic diagram of the time interval between two different characteristic events, and Figure 9 (d) is a schematic diagram of the respiratory waveform.

FIG. 10 is a functional block diagram of a breathing signal extraction device provided by Embodiment 2 of the present invention.

FIG. 11 is a specific structural block diagram of a breathing signal extraction processing device provided in Embodiment 4 of the present invention.

FIG. 12 is a specific structural block diagram of a breathing signal extraction system provided by Embodiment 5 of the present invention.

Best Mode of the Invention

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. In order to explain the technical solution of the present invention, the following description is made through specific embodiments.

Referring to FIG. 1, a method for extracting a breathing signal provided by Embodiment 1 of the present invention includes the following steps:

S101. Obtain a waveform of a heart beat monitoring signal.

Among them, the heartbeat monitoring signals can be Ballistocardiogram (BCG) signals, Electrocardiogram (ECG) signals, Phonocardiogram (PCG) signals, Seismocardiogram (SCG) signals, photoelectric volume pulse waves ( Photoplethysmograph (PPG), etc. 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.

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. 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. 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.

When the heartbeat monitoring signal is obtained through a vibration sensor, S101 may specifically be: filtering and scaling the original vibration signal (as shown in FIG. 2) obtained by the vibration sensor to generate a heartbeat monitoring signal waveform (for example, FIG. The time-domain waveform of the BCG signal shown in Figure 3).

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. When filtering the original vibration signal, the method may further include the following steps: determining whether the original vibration signal carries a power frequency interference signal, and if so, filtering the power frequency noise through a power frequency notch.

S102. Obtain the time interval of two different characteristic events in the same cardiac cycle according to the waveform of the heart beat monitoring signal, and obtain the breathing waveform according to the time interval of the two different characteristic events.

The following uses the heartbeat monitoring signal as the BCG signal as an example to further explain S102. When the heartbeat monitoring signal is an ECG signal, a PCG signal, an SCG signal, or a PPG signal, the time interval between two different characteristic events in the same cardiac cycle is obtained according to the waveform of the heartbeat monitoring signal, and according to the two different characteristic events The change of the time interval with time to obtain the breathing waveform is based on a similar principle to the BCG signal, and the signal waveform is the same, and will not be repeated here.

The waveform of the BCG signal contains a wealth of information about the characteristics of the cardiac cycle, including the most representative "J" peak, the "I" valley and the "K" valley to the left and right of the "J" peak, and the characteristics corresponding to the systole Peak-valley groups (MC, AVO, etc.), characteristic peak-valley groups (AVC, MO, etc.) corresponding to diastole, and other characteristic peak-valleys.

From the cardiopulmonary coupling mechanism, it is known that the intrinsic coordination mechanism between the cardiovascular circulatory system and the respiratory system interacts. Cardiopulmonary coupling analysis can reflect the relationship between the cardiopulmonary system and the strength of the coupling. Therefore, due to the effects of cardiopulmonary coupling, BCG signals contain breathing-related signals. The body breathing signals can be obtained through BCG signals, which can better meet the actual clinical needs and have more important clinical analysis significance.

In the first embodiment of the present invention, feature points such as “J” peak, “I” valley, “K” valley, MC, AVO, AVC, and MO are determined from the waveform of the BCG signal, and then the time interval of different characteristic events is selected. To obtain the breathing waveform, for example, the time interval of the JK feature, the time interval of the JI feature, the time interval of the AVO-J feature, the time interval of the AVO-K feature, the time interval of the AVO-I feature, and the time interval of the AVO-MC feature , The time interval of the AVO-AVC feature, the time interval of the AVO-MO feature, and the like. Wherein, the time interval of any combination of the “J” peak, “I” valley, “K” valley, MC, AVO, AVC, MO and other characteristics determined on the waveform of the BCG signal can be inferred to obtain a breathing waveform.

In the first embodiment of the present invention, S102 may specifically be:

Based on a heartbeat monitoring signal, obtaining the time interval of two different characteristic events in the same cardiac cycle according to the waveform of the heartbeat monitoring signal, and obtaining the respiratory waveform according to the time interval of the two different characteristic events; or,

Based on the two heart beat monitoring signals, and based on the synchronized waveforms of the two heart beat monitoring signals, the time interval between two different characteristic events in the same heartbeat cycle of the synchronized waveforms of the two heart beat monitoring signals, respectively, and according to the two different Obtain breathing waveforms over time for characteristic events; or

Based on the multi-channel heart beat monitoring signals, two high-quality heart beat monitoring signals are selected for synchronization. Based on the high-quality two heart beat monitoring signals, the two heart beat monitoring signals are acquired based on the synchronized waveforms of the two heart beat monitoring signals. The synchronous waveform of the signal is the time interval of two different characteristic events in the same cardiac cycle, and the breathing waveform is obtained according to the time interval of the two different characteristic events.

When based on the two-way heart beat monitoring signals, the two-way heart beat monitoring signals may include a left shoulder-based heart beat monitoring signal and a right shoulder-based heart beat monitoring signal. When based on the multiple heart beat monitoring signals, the multiple heart beat monitoring signals may include a left shoulder-based heart beat monitoring signal, a right shoulder-based heart beat monitoring signal, and heart beat monitoring signals based on other parts of the body.

In the first embodiment of the present invention, the time interval of two different characteristic events in the same cardiac cycle is obtained based on the waveform of the heart beat monitoring signal based on a heartbeat monitoring signal, and according to the time interval of the two different characteristic events, Obtaining the breathing waveform as a function of time can be:

Based on a heartbeat monitoring signal, according to any two different characteristic peaks / troughs of the waveform of the heartbeat monitoring signal in the same cardiac cycle, or any two waveforms transformed in the same heartbeat cycle according to the waveform of the heartbeat monitoring signal Different types of characteristic peaks / valleys are used to obtain the time interval of any two different characteristic events, and a breathing waveform is obtained according to the time interval of the any two different characteristic events.

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 different 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.

Obtaining a breathing waveform according to the change in time interval of any two different characteristic events in the same cardiac cycle may be based on the change in time interval of any two different characteristic events with time, using linear interpolation, cubic samples Extract the breathing waveform by bar fitting, polynomial fitting and other methods.

Acquired according to any two different characteristic peaks / valleys of the waveform of the heart beat monitoring signal in the same cardiac cycle, or any two different characteristic peaks / valleys of the same waveform during the transformed waveform according to the waveform of the heart beat monitoring signal. The time interval of any two different characteristic events may specifically be:

Based on the waveform of the heart beat monitoring signal or the transformed waveform of the heart beat monitoring signal, all selected two different characteristic peaks / valleys of the waveform in the same cardiac cycle are detected, and the adjacent selected ones are calculated. The time interval between any two different characteristic peaks / valleys is used as the time interval between different characteristic events corresponding to the selected any two different characteristic peaks / valleys.

In the first embodiment of the present invention, the time interval of two different characteristic events in the same cardiac cycle obtained from the synchronous waveforms of the two cardiac beat monitoring signals respectively in the same cardiac cycle is obtained according to the synchronous waveforms of the two cardiac beat monitoring signals. Obtaining the respiratory waveform for the time interval of different characteristic events can be specifically:

Any two different characteristic peaks / valleys based on the synchronized waveforms of the two heart beat monitoring signals in the same cardiac cycle, or any two of the same transformed waveforms based on the synchronized waveforms of the two heart beat monitoring signals in the same cardiac cycle For different characteristic peaks / valleys, obtain the time interval of the two different characteristic events in the same cardiac cycle with the synchronous waveforms of the two heart beat monitoring signals respectively, and according to the time interval of the two different characteristic events, The change in time acquires the breathing waveform.

Any two different characteristic peaks / valleys in the same cardiac cycle according to the synchronous waveforms of the two cardiac beat monitoring signals, or any waveform in the same cardiac cycle that is transformed according to the synchronous waveforms of the two cardiac beat monitoring signals in the same cardiac cycle The time interval between the two different characteristic peaks / valleys to obtain the synchronous waveforms of the two cardiac beat monitoring signals respectively in the same cardiac cycle can be:

Based on the two waveforms of the heartbeat monitoring signals or the waveforms of the heartbeat monitoring signals after conversion, all selected two different characteristic peaks / valleys of the waveform in the same cardiac cycle are detected, and the two are located in two And the time interval between the selected any two different characteristic peaks / valleys adjacent to each other in the synchronous waveform of the heart beat monitoring signal, and the time interval is used as the selected any two different characteristic peaks / valves. The time interval of different characteristic events corresponding to the valley.

As shown in Fig. 4, based on the waveform of the BCG signal, each "J" peak and the "K" valley following it are detected, and the time interval between each adjacent "J" peak and "K" valley is calculated, that is, " "JK" time interval, that is, the time interval between two different characteristic events. At this time, it was found that as the process of expiration and inhalation changes, the "J-K" time interval also changes accordingly. The characteristic events “J” peak and “K” valley selected at this time are characteristic events in the same cardiac cycle. The same cardiac cycle here does not specifically refer to the cardiac cycle in the chronological sequence of physiological events from contraction to relaxation in the physiological sense, but generally refers to two peaks / valleys with the same characteristic that can divide the interval between heartbeats. Between cardiac cycles. For example, a cardiac cycle between two adjacent "J" peaks and a cardiac cycle between two adjacent "K" valleys. The "J" peaks and "K" valleys selected here not only correspond to the events in the cardiac cycle corresponding to the broadness of the heartbeat interval, but also actually belong to the events in the cardiac cycle in the physiological sense of heart beat.

As shown in FIG. 5, the time at which each "J" peak is located is taken as the abscissa, and the "J-K" time interval is taken as the ordinate, and the "J-K" time interval changes with time are plotted. Affected by the cardiopulmonary coupling effect, the "J-K" time interval also presents a "high and low" contour along with the exhalation and inspiration process. Based on the time-series waveform, methods such as linear interpolation, cubic spline fitting, polynomial fitting, etc. can be provided to extract the breathing waveform. Figure 7 shows the respiration waveforms 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. Based on the influence of the cardiopulmonary coupling, it can be judged that the waveform goes high / low and the process of exhalation and inhalation. Correspondence relationship can be based on this to do more calculation and analysis of signals during the exhalation and inhalation phase. The time interval of the two different characteristic events selected here is the "J-K" time interval. According to a large amount of experimental data, it is shown that the waveform going to the lower part is an inhalation process, and the waveform going to the high part is an exhalation process.

As shown in Figure 6, from top to bottom, the first path is the waveform of the original vibration signal containing the breathing envelope, the second path is the extracted BCG signal waveform, and the third path is the signal obtained by second-order differentiation of the BCG signal. Waveform. The starting point of the characteristic event is switched from the “J” peak of the BCG signal waveform to the “AVO” peak of the second-order differential signal waveform, that is, the position of the maximum peak point of the second-order differential signal waveform before the “J” peak of the BCG signal waveform. The width between the "AVO" peak and the "K" valley is the "AVO-K" time interval. Similarly, as the exhalation and inspiration processes change, the "AVO-K" time interval will change accordingly.

As shown in FIG. 7, the time at which each “AVO” peak is located is taken as the abscissa, and the “AVO-K” time interval is taken as the ordinate, and the change of the “AVO-K” time interval that changes with time is plotted. Affected by the effects of cardiopulmonary coupling, the "AVO-K" time interval also exhibits a "high and low" contour along with the exhalation and inspiration process. Based on the time-series waveform, methods such as linear interpolation, cubic spline fitting, polynomial fitting, etc. can be provided to extract the breathing waveform. Figure 7 shows the respiration waveforms extracted based on cubic spline fitting. Similarly, 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 cardiopulmonary coupling effect, the waveform can be judged to go high / low and exhale and inhale. The corresponding relationship of the gas process can be based on this to do more calculation and analysis of the signals during the exhalation and inhalation phases. The time interval of the two different characteristic events selected here is the "AVO-K" time interval. According to a large amount of experimental data, it is shown that the waveform going to the lower part is an inhalation process, and the waveform going to the high part is an exhalation process.

As shown in Figure 8, the first path is the waveform of the original vibration signal containing the breathing envelope (the solid line is the left shoulder signal and the dotted line is the right shoulder signal), and the second path is the signal waveform obtained by second-order differentiation of the BCG signal (real The line is the left shoulder signal and the dotted line is the right shoulder signal). The starting point of the characteristic event is still the left shoulder second-order differential signal waveform "AVO" peak, and the end point is switched from the "K" valley of the left shoulder BCG signal to the right shoulder second-order differential signal waveform and the peak associated with "AVC" (may also be defined here as " AVC "peak). At this time, the time interval between each" AVO "peak and the" AVC "peak is calculated, that is, the" AVO-AVC "time interval. Similarly, as the exhalation and inspiration processes change, the "AVO-AVC" time interval will change accordingly. Using the time of each "AVO" peak as the abscissa and the "AVO-AVC" time interval as the ordinate, plot the "AVO-AVC" time interval change over time. Affected by the effects of cardiopulmonary coupling, the "AVO-AVC" time interval also showed a "high and low" contour with the exhalation and inspiration process. Based on the time-series waveform, methods such as linear interpolation, cubic spline fitting, polynomial fitting, etc. can be provided to extract the breathing waveform. Figure 9 shows the respiration waveform obtained based on cubic spline fitting. Similarly, 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 cardiopulmonary coupling effect, the waveform can be judged to go high / low and exhale and inhale. The corresponding relationship of the gas process can be based on this to do more calculation and analysis of the signals during the exhalation and inhalation phases. The time interval of the two different characteristic events selected here is the "AVO-AVC" time interval. According to a large number of experimental data, it is shown that the waveform going to the lower part is an inhalation process, and the waveform going to the high part is an exhalation process. In addition to the "AVO" peak and the "AVC" peak selected in this embodiment, suitable characteristic event peaks and valleys of two-way BCG signals can be selected according to the actual signal characteristics.

Referring to FIG. 10, a breathing signal extraction device provided in a second embodiment of the present invention includes: an acquisition module 21 for acquiring a waveform of a heart beat monitoring signal; and a respiratory waveform acquisition module 22 for obtaining a waveform according to the waveform of a heart beat monitoring signal. The time interval of two different characteristic events, and the breathing waveform is obtained according to the time interval of the two different characteristic events. 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.

As shown in FIG. 11, the fourth embodiment 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.

Referring to FIG. 12, a breathing signal extraction system provided in Embodiment 5 of the present invention includes: a generating module 11 configured to generate a waveform of a heart beat monitoring signal; and a module connected to the generating module, as provided in Embodiment 4 of the present invention. Of respiratory signal extraction processing device 100.

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.

In the present invention, the waveform of the heart beat monitoring signal is obtained, and then the time interval of two different characteristic events is obtained according to the waveform of the heart beat monitoring signal, and the breathing waveform is obtained according to the time change of the two different characteristic events. 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.

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. 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

A method for extracting a breathing signal, wherein the method includes:

Obtain the waveform of the heart beat monitoring signal;

Obtain the time interval of two different characteristic events in the same cardiac cycle according to the waveform of the heart beat monitoring signal, and obtain the breathing waveform according to the time interval of the two different characteristic events.
The method according to claim 1, wherein the heartbeat monitoring signal is an ECG signal or a PPG signal.
The method according to claim 1, wherein the heartbeat monitoring signal is a BCG signal, a PCG signal, or an SCG signal.
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.
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.
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.
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.
The method according to claim 6, further comprising: when filtering the original vibration signal:

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.
The method according to claim 1, wherein the time interval of two different characteristic events in the same cardiac cycle is obtained according to the waveform of the heart beat monitoring signal, and the time interval of the two different characteristic events varies with time according to the The changes to obtain the breathing waveform are:

Based on a heartbeat monitoring signal, obtaining the time interval of two different characteristic events in the same cardiac cycle according to the waveform of the heartbeat monitoring signal, and obtaining the respiratory waveform according to the time interval of the two different characteristic events; or,

Based on the two heart beat monitoring signals, and based on the synchronized waveforms of the two heart beat monitoring signals, the time interval between two different characteristic events in the same heartbeat cycle of the synchronized waveforms of the two heart beat monitoring signals, respectively, and according to the two different Obtain breathing waveforms over time for characteristic events; or

Based on the multi-channel heart beat monitoring signals, two high-quality heart beat monitoring signals are selected for synchronization. Based on the high-quality two heart beat monitoring signals, the two heart beat monitoring signals are acquired based on the synchronized waveforms of the two heart beat monitoring signals. The synchronous waveform of the signal is the time interval of two different characteristic events in the same cardiac cycle, and the breathing waveform is obtained according to the time interval of the two different characteristic events.
The method according to claim 9, wherein the time interval of two different characteristic events in the same cardiac cycle is obtained based on the waveform of the heart beat monitoring signal based on a heart beat monitoring signal, and according to two different The change of the time interval of the characteristic event with time to obtain the breathing waveform is as follows:

Based on a heartbeat monitoring signal, according to any two different characteristic peaks / troughs of the waveform of the heartbeat monitoring signal in the same cardiac cycle, or any two waveforms transformed in the same heartbeat cycle according to the waveform of the heartbeat monitoring signal Different types of characteristic peaks / valleys are used to obtain the time interval of any two different characteristic events, and a breathing waveform is obtained according to the time interval of the any two different characteristic events.
The method according to claim 10, wherein the waveform according to any two different characteristic peaks / valleys in the same cardiac cycle according to the waveform of the heart beat monitoring signal, or a waveform transformed according to the waveform of the heart beat monitoring signal The time interval for acquiring any two different characteristic events in any two different characteristic peaks / valleys in the same cardiac cycle is specifically:

Based on the waveform of the heart beat monitoring signal or the transformed waveform of the heart beat monitoring signal, all selected two different characteristic peaks / valleys of the waveform in the same cardiac cycle are detected, and the adjacent selected ones are calculated. The time interval between any two different characteristic peaks / valleys is used as the time interval between different characteristic events corresponding to the selected any two different characteristic peaks / valleys.
The method according to claim 9, characterized in that the time of two different characteristic events in the same cardiac cycle obtained from the synchronous waveforms of the two cardiac beat monitoring signals respectively in the same cardiac cycle is obtained according to the synchronous waveforms of the two cardiac beat monitoring signals. The interval and the change of the respiratory waveform based on the time interval of two different characteristic events are as follows:

Any two different characteristic peaks / valleys based on the synchronized waveforms of the two heart beat monitoring signals in the same cardiac cycle, or any two of the same transformed waveforms based on the synchronized waveforms of the two heart beat monitoring signals in the same cardiac cycle For different characteristic peaks / valleys, obtain the time interval of the two different characteristic events in the same cardiac cycle with the synchronous waveforms of the two heart beat monitoring signals respectively, and according to the time interval of the two different characteristic events, The change in time acquires the breathing waveform.
The method according to claim 12, characterized in that the waveforms according to any two different characteristic peaks / valleys in the same cardiac cycle according to the synchronization waveforms of the two heart beat monitoring signals, or according to the synchronization of the two heart beat monitoring signals After the waveform is transformed, any two different characteristic peaks / valleys of the waveform in the same cardiac cycle are obtained, and the time interval of the two different characteristic events in the same cardiac cycle of the synchronized waveforms of the two cardiac beat monitoring signals are obtained. Specifically:

Based on the synchronized waveforms of the two heartbeat monitoring signals or the waveforms of the heartbeat monitoring signal transformed, all selected two different characteristic peaks / valleys of the waveform in the same cardiac cycle are detected, and the two channels are calculated respectively. The time interval between the synchronous waveforms of the heartbeat monitoring signals between the adjacent two selected arbitrary two different characteristic peaks / valleys in the same cardiac cycle, and the time interval is taken as the selected any two different Time interval of different characteristic events corresponding to characteristic peaks / valleys.
The method according to claim 10 or 12, 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 different characteristic events on its time domain signal. The waveform of the time-interval distribution characteristics is transformed.
The method according to claim 14, wherein the transformation mode of the distribution feature that does not affect the time interval of each different feature event on its time domain signal is an integral transformation or a differential transformation.
The method according to claim 1, wherein the obtaining a breathing waveform according to a change in time interval of two different characteristic events is: based on a change of time interval of two different characteristic events with time, using linear interpolation Respiration waveform extracted by cubic spline fitting or polynomial fitting.
A breathing signal extraction device, characterized in that the extraction device includes:

An acquisition module for acquiring a waveform of a heart beat monitoring signal; and

The breathing waveform acquisition module is used to obtain the time interval of two different characteristic events in the same cardiac cycle according to the waveform of the heart beat monitoring signal, and to obtain the respiratory waveform according to the time interval of the two different characteristic events.
A computer-readable storage medium storing a computer program, wherein the computer program executes extraction of a breathing signal according to any one of claims 1 to 16 when executed by a processor. Method steps.
A breathing signal extraction processing device includes:

One or more processors;

Memory; and

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 method for extracting a breathing signal according to any one of claims 1 to 16.
A respiratory signal extraction system, characterized in that the extraction system includes:

A generation module configured to generate a waveform of a heart beat monitoring signal; and

The breathing signal extraction and processing device according to claim 19, connected to the generating module.