CN112914554A - Noninvasive breathing frequency monitoring method and system for breathing machine - Google Patents

Noninvasive breathing frequency monitoring method and system for breathing machine Download PDF

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CN112914554A
CN112914554A CN202110314599.6A CN202110314599A CN112914554A CN 112914554 A CN112914554 A CN 112914554A CN 202110314599 A CN202110314599 A CN 202110314599A CN 112914554 A CN112914554 A CN 112914554A
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heartbeat
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刘哲
郑其昌
周磊
唐聪能
吴超民
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Hunan Ventmed Medical Technology Co Ltd
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Abstract

The invention provides a noninvasive breathing frequency monitoring method and a noninvasive breathing frequency monitoring system for a breathing machine, wherein the method comprises the following steps: the detector sensing module monitors and collects a monitored state signal of a source signal including a heartbeat signal and a respiration signal at the time t, transmits the state signal to the data preprocessing module for preprocessing, transmits the state signal to the high-order low-pass filtering module again, constructs an autocorrelation calculation model for the heartbeat signal, obtains the heartbeat signal with accurate fundamental frequency as a carrier signal, executes self amplitude demodulation, separates the heartbeat signal and the respiration signal, and sequentially transmits the heartbeat signal and the respiration signal to the main control module and the big data cloud platform storage module. After the periodicity of the heartbeat signal is accurately deduced by constructing an autocorrelation calculation model, the heartbeat signal with accurate fundamental frequency is obtained and is used as a carrier signal, and then the state signal of the monitored person is multiplied by itself to execute amplitude demodulation, so that the heartbeat signal and the respiratory signal are accurately separated.

Description

Noninvasive breathing frequency monitoring method and system for breathing machine
Technical Field
The invention belongs to the technical field of respiratory frequency detection, and particularly relates to a noninvasive respiratory frequency monitoring method and system for a respirator.
Background
Ventilators are used in a wide range of clinical departments in hospitals as first aid devices that can provide respiratory support to critically ill patients with respiratory disorders or to patients that are anesthetized during surgery. Is often applied to mobile first-aid places such as respiratory care units (RICU), emergency departments, emergency Intensive Care Units (ICU), cardiac medicine care units (CCU), postoperative anesthesia resuscitation rooms, ambulances and the like. Whether the parameters of the breathing machine are accurate or not is directly related to the life safety of the patient.
With the continuous research on the breathing mechanism and the mechanical ventilation technology and the application of a large number of new technologies in the field of biomedical engineering, the development of the breathing machine is very rapid in recent 20 years, the clinical application is becoming more extensive, a lot of experience is accumulated, the influence of the breathing machine on the human body is more understood, meanwhile, some problems to be solved are found, new requirements are provided clinically, and the development of the breathing machine is promoted by the problems and the requirements.
Due to the rapid development of computer technology in recent years, such control type ventilators are becoming mature. The control precision of the breathing machine is high, the functions are multiple, and more breathing machines adopt the method. At present, the performance of the breathing machine can be modified and the function of the breathing machine can be developed only by changing the software part of the control system without changing hardware and structural components of the breathing machine. Therefore, the utilization of a microcomputer as a control part of a ventilator is a general trend of development and update of the ventilator. Therefore, there is an urgent need for a noninvasive respiratory rate monitoring method and system for a ventilator, which can effectively and accurately detect respiratory rate signals of a ventilator user, and further facilitate accurate and rapid nursing or treatment response of subsequent processing software.
Disclosure of Invention
Aiming at the defects, the invention provides the noninvasive breathing frequency monitoring method and the noninvasive breathing frequency monitoring system of the breathing machine, which only need to recover respective frequency components and do not need amplitude and phase information values of signals when separating the breathing signals and the heartbeat signals, simplify the calculated amount required by an algorithm, and reduce the calculated amount and the required time for obtaining accurate breathing signals by calculation and separation.
The invention provides the following technical scheme: a noninvasive respiratory rate monitoring method of a respirator comprises the following steps:
s1: the detector sensing module monitors and collects the state signals s (t) of the monitored person at the time t, wherein the state signals of the monitored person comprise heartbeat signals
Figure RE-GDA0003025936190000021
And respiratory signal sr(t), wherein the heartbeat signal
Figure RE-GDA0003025936190000022
As a carrier signal, the respiration signal sr(t) as an information signal, j being the jth harmonic of the heartbeat signal, j being 1,2 …, N;
s2: the detector sensing module amplifies and integrates the state signal of the detected person through an amplifying circuit module and an A/D conversion circuit module, and then transmits the amplified and integrated state signal to the data preprocessing module;
s3: the data preprocessing module preprocesses the state signal s (t) of the detected person, and constructs a calculation model of the state signal s (t) of the detected person and the heartbeat signal
Figure RE-GDA0003025936190000023
A calculation model, the respiratory signal sr(t) calculating a model;
s4: the preprocessing module transmits the preprocessed detected state signal s (t) to the high-order low-pass filtering module;
s5: the high-order low-pass filtering module receives the preprocessed detected state signal s (t) and carries out the heartbeat signal
Figure RE-GDA0003025936190000024
An autocorrelation calculation model is constructed, the influence of the sampling time point and the time delay brought in the signal transmission and receiving process on the frequency of the heartbeat signal is reduced, and the heartbeat signal with accurate fundamental frequency is obtained
Figure RE-GDA0003025936190000031
As a carrier signal;
s6: then the high-order low-pass filtering module multiplies the preprocessed detected state signal s (t) by itself to construct a self amplitude demodulation model to execute self amplitude demodulation, and further, the heartbeat signal with accurate fundamental frequency is subjected to the heartbeat demodulation
Figure RE-GDA0003025936190000032
And the respiratory signal sr(t) carrying out a separation;
s7: separating the S6 step to obtain the heartbeat signal with accurate fundamental frequency
Figure RE-GDA0003025936190000033
And respiratory signal srAnd (t) transmitting the data to a main control module, and wirelessly transmitting the data to the big data cloud platform storage module by the main control module.
Further, the calculation model of the monitored object status signal S (t) constructed in the step S3 is:
Figure RE-GDA0003025936190000034
further, the heartbeat signal constructed in the step of S3
Figure RE-GDA0003025936190000035
The calculation model is as follows:
Sr(t)＝ar×cos(2πfrt+θj)；
a is arCalculating parameters for the heartbeat signal, said frFor heart beat frequency, thetajA heartbeat frequency amplitude angle that is a jth harmonic of the heartbeat signal.
Further, the respiration signal S constructed in the step S3r(t) the calculation model is:
Figure RE-GDA0003025936190000036
the above-mentioned
Figure RE-GDA0003025936190000037
Calculating a parameter for the respiratory signal, said
Figure RE-GDA0003025936190000038
A respiratory frequency amplitude angle brought to the jth harmonic of the heartbeat signal; to be provided with
Figure RE-GDA0003025936190000039
The fundamental frequency of the heartbeat signal.
Further, in the step S6, the self amplitude demodulation model constructed by multiplying the preprocessed detected state signal S (t) by itself is:
Figure RE-GDA0003025936190000041
wherein, a'jSeparation parameters of self amplitude demodulation algorithm to respiratory frequency at jth harmonic of heartbeat signal
Figure RE-GDA0003025936190000042
Separate parameters of the heartbeat frequency, a ', for a self amplitude demodulation algorithm'h-jIs a low frequency separation parameter whose respiratory frequency is influenced by the heartbeat frequency at the jth harmonic of the heartbeat signal, a'h+jA high frequency separation parameter, which is the respiratory frequency affected by the heartbeat frequency at the jth harmonic of the heartbeat signal; a'1×cos(4πfrt) is the lowest frequency component.
Further, the step S5 is to compare the heartbeat signal
Figure RE-GDA0003025936190000043
Constructing an autocorrelation calculation model, comprising the steps of:
s51: to what is neededThe heart beat signal
Figure RE-GDA0003025936190000044
The time series signal x (t) of the signal power at time t, the autocorrelation model is constructed:
Figure RE-GDA0003025936190000045
wherein l is the sampling time point and the time delay brought by the signal transmission and receiving process, l is more than or equal to 0 and less than n, and n is the number of the sampling time points;
s52: the heartbeat signal is processed by the step S51
Figure RE-GDA0003025936190000046
To 0 to generate a power signal proportional to the instantaneous power of the detector sensing module when the heartbeat signal is present
Figure RE-GDA0003025936190000051
When the average value of the signal power of (1) is shifted to 0, the heartbeat signal
Figure RE-GDA0003025936190000052
The power signal of (a) is aligned with itself, and the autocorrelation reaches a maximum value;
s53: deducing the periodicity of the heartbeat by detecting the peak in the autocorrelation model, and further obtaining the heartbeat signal with accurate fundamental frequency
Figure RE-GDA0003025936190000053
And acts as a carrier signal.
Further, in the step S51
Figure RE-GDA0003025936190000054
The calculation formula of (2) is as follows:
Figure RE-GDA0003025936190000055
wherein, the
Figure RE-GDA0003025936190000056
Is the average number of samples, said xt+lTime series signal values at time t + l, said xtIs the time series signal value at time t.
Further, the time series signal value average
Figure RE-GDA0003025936190000057
The calculation formula of (2) is as follows:
Figure RE-GDA0003025936190000058
the invention also provides a noninvasive respiratory rate monitoring system of the respirator by adopting the method, which comprises a wave detector sensing module, an amplifying circuit module, an A/D conversion circuit module, a high-order low-pass filtering module, a main control module and a big data cloud platform storage module.
The system further comprises a power supply module for supplying stable current required by the system to work, a wireless transceiver module for wirelessly transmitting and sharing data obtained by separating the main control module to the big data cloud platform storage module, and an interactive function module for interactively transmitting data monitored and collected by the detector sensing module and the amplifying circuit module, the A/D conversion circuit module and the high-order low-pass filtering module in sequence.
The invention has the beneficial effects that:
1. the method and the system for monitoring the noninvasive breathing frequency of the respirator provided by the invention are used for monitoring the heartbeat signal
Figure RE-GDA0003025936190000061
Constructing an autocorrelation calculation model to enable the heartbeat signal comprising a fundamental frequency and a plurality of higher frequency components to be capable of being subjected to signal caused by power signal dislocation caused by time delay of signal transmission according to time sequence signal valuesCorrecting the error, and moving the average value to 0 to generate a power signal proportional to the instantaneous power of the sensor module when the heartbeat signal is detected
Figure RE-GDA0003025936190000062
When the average value of the signal power of (1) is shifted to 0, the heartbeat signal
Figure RE-GDA0003025936190000063
Is aligned with itself, the autocorrelation reaches a maximum, the first signal remains unchanged when the time delay starts to increase, while the second signal, formed by the time delay due to the signal propagation, moves to the right. The mismatch between the two signals causes the autocorrelation value of the heartbeat signal collected during sampling to be reduced, and the periodicity of the heartbeat can be deduced by detecting the peak in the autocorrelation calculation model result.
2. The method for monitoring the noninvasive respiratory rate of the respirator provided by the invention can be used for accurately deducing the periodicity of the heartbeat signal by constructing an autocorrelation calculation model to obtain the heartbeat signal with accurate fundamental frequency as a carrier signal, and then accurately deducing the heartbeat signal with the accurate fundamental frequency
Figure RE-GDA0003025936190000064
The monitored state signal s (t) is multiplied by itself to execute amplitude demodulation, and then accurate separation of the heartbeat signal and the respiration signal which are also lower than the detection threshold value of the detector detection module by 8.4Hz is realized.
3. By constructing the self-correlation calculation model of the heartbeat signal, the signal error caused by signal delay caused by signal transmission is reduced, and the heartbeat signal with accurate fundamental frequency is obtained
Figure RE-GDA0003025936190000065
As a carrier signal, in turn, it is fundamentally different from the conventional radio frequency modulation amplitude signal, which is a radio frequency signal with a known carrier frequency as the carrier signal, which results in no need to worry about the information signal frequency again when performing the envelope analysisThe high-order low-pass filtering module reuses the obtained heartbeat signal with accurate fundamental frequency
Figure RE-GDA0003025936190000071
As carrier signal, multiplying the preprocessed detected state signal s (t) with itself to construct its own amplitude demodulation model, executing its own amplitude demodulation, and further processing the heartbeat signal with accurate fundamental frequency
Figure RE-GDA0003025936190000072
And the respiratory signal srAnd (t) separation is carried out, so that the phenomenon that separation information is lost or the respiratory signal obtained by separation is inaccurate due to inaccurate fundamental frequency of the carrier signal in the separation process is avoided.
4. When the respiratory signal and the heartbeat signal are separated, the noninvasive respiratory frequency monitoring method of the respirator only needs to recover respective frequency components without amplitude and phase information values of the signals, simplifies the calculated amount required by an algorithm, and reduces the calculated amount and the required time for obtaining accurate respiratory signals by calculation and separation.
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The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
FIG. 1 is a schematic flow chart of a noninvasive ventilator frequency monitoring method according to the present invention;
fig. 2 is a schematic structural diagram of a noninvasive ventilator frequency monitoring system provided by the present invention.
Detailed description of the preferred embodiments
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides a method for noninvasive respiratory rate monitoring of a ventilator, including the following steps:
s1: the detector sensing module monitors and collects the state signals of the monitored person at the time t, wherein the state signals of the monitored person comprise heartbeat signals
Figure RE-GDA0003025936190000081
And respiratory signal sr(t), wherein the heartbeat signal
Figure RE-GDA0003025936190000082
As a carrier signal, the respiration signal sr(t) as an information signal, j being the jth harmonic of the heartbeat signal, j being 1,2 …, N;
s2: the detector sensing module amplifies and integrates the state signal of the detected person through an amplifying circuit module and an A/D conversion circuit module, and then transmits the amplified and integrated state signal to the data preprocessing module;
s3: the data preprocessing module preprocesses the state signal s (t) of the detected person, and constructs a calculation model of the state signal s (t) of the detected person and the heartbeat signal
Figure RE-GDA0003025936190000083
A calculation model, the respiratory signal sr(t) calculation model:
Figure RE-GDA0003025936190000084
sr(t)＝ar×cos(2πfrt+θj)；
Figure RE-GDA0003025936190000085
wherein, the arCalculating parameters for the heartbeat signal, said frFor heart beat frequency, thetajIs the first of the heartbeat signalThe amplitude angle of the heartbeat frequency of the j harmonic; the above-mentioned
Figure RE-GDA0003025936190000086
Calculating a parameter for the respiratory signal, said
Figure RE-GDA0003025936190000087
A respiratory frequency amplitude angle brought to the jth harmonic of the heartbeat signal; to be provided with
Figure RE-GDA0003025936190000088
Is the fundamental frequency of the heartbeat signal;
s4: the preprocessing module transmits the preprocessed detected state signal s (t) to the high-order low-pass filtering module;
s5: the high-order low-pass filtering module receives the preprocessed detected state signal s (t) and carries out the heartbeat signal
Figure RE-GDA0003025936190000089
An autocorrelation calculation model is constructed, the influence of the sampling time point and the time delay brought in the signal transmission and receiving process on the frequency of the heartbeat signal is reduced, and the heartbeat signal with accurate fundamental frequency is obtained
Figure RE-GDA0003025936190000091
As a carrier signal;
s6: then the high-order low-pass filtering module multiplies the preprocessed detected state signal s (t) by itself to construct a self amplitude demodulation model to execute self amplitude demodulation, and further, the heartbeat signal with accurate fundamental frequency is subjected to the heartbeat demodulation
Figure RE-GDA0003025936190000092
And the respiratory signal sr(t) separating, wherein the self amplitude demodulation model is constructed as follows:
Figure RE-GDA0003025936190000093
wherein, a'jSeparation parameters of self amplitude demodulation algorithm to respiratory frequency at jth harmonic of heartbeat signal
Figure RE-GDA0003025936190000094
Separate parameters of the heartbeat frequency, a ', for a self amplitude demodulation algorithm'h-jIs a low frequency separation parameter whose respiratory frequency is influenced by the heartbeat frequency at the jth harmonic of the heartbeat signal, a'h+jA high frequency separation parameter, which is the respiratory frequency affected by the heartbeat frequency at the jth harmonic of the heartbeat signal; a'1×cos(4πfrt) is the lowest frequency component;
s7: separating the S6 step to obtain the heartbeat signal with accurate fundamental frequency
Figure RE-GDA0003025936190000095
And respiratory signal srAnd (t) transmitting the data to a main control module, and wirelessly transmitting the data to the big data cloud platform storage module by the main control module.
Step S5 for the heartbeat signal
Figure RE-GDA0003025936190000101
Constructing an autocorrelation calculation model, comprising the steps of:
s51: for the heartbeat signal
Figure RE-GDA0003025936190000102
The time series signal x (t) of the signal power at time t, the autocorrelation model is constructed:
Figure RE-GDA0003025936190000103
Figure RE-GDA0003025936190000104
wherein, the l is a sampling time point and signal transmissionAnd time delay brought in the receiving process, l is more than or equal to 0 and less than n, and n is the number of sampling time points; the above-mentioned
Figure RE-GDA0003025936190000105
Is the mean of the time series signal values, said xt+lTime series signal values at time t + l, said xtTime series signal values at time t;
wherein the time series signal value average
Figure RE-GDA0003025936190000106
The calculation formula of (2) is as follows:
Figure RE-GDA0003025936190000107
s52: the heartbeat signal is processed by the step S51
Figure RE-GDA0003025936190000108
To 0 to generate a power signal proportional to the instantaneous power of the detector sensing module when the heartbeat signal is present
Figure RE-GDA0003025936190000109
When the average value of the signal power of (1) is shifted to 0, the heartbeat signal
Figure RE-GDA00030259361900001010
The power signal of (a) is aligned with itself, and the autocorrelation reaches a maximum value;
s53: deducing the periodicity of the heartbeat by detecting the peak in the autocorrelation model, and further obtaining the heartbeat signal with accurate fundamental frequency
Figure RE-GDA00030259361900001011
And acts as a carrier signal.
Example 2
The embodiment provides a noninvasive respiratory frequency monitoring system of a ventilator using the method provided in embodiment 1, which includes a detector sensing module, an amplifying circuit module, an a/D conversion circuit module, a high-order low-pass filtering module, a main control module, a big data cloud platform storage module, a power supply module for supplying stable current required by the system to work, a wireless transceiver module for wirelessly transmitting and sharing data obtained by separating the main control module to the big data cloud platform storage module, and an interactive function module for interactively transmitting data acquired by the detector sensing module and the amplifying circuit module, the a/D conversion circuit module, and the high-order low-pass filtering module in sequence.
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any way as long as there is no structural conflict. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.

Claims (10)

1. A noninvasive respiratory rate monitoring method of a respirator is characterized by comprising the following steps:
s1: the detector sensing module monitors and collects the state signals s (t) of the monitored person at the time t, wherein the state signals of the monitored person comprise heartbeat signals
Figure FDA0002990632640000011
And respiratory signal sr(t), wherein the heartbeat signal
Figure FDA0002990632640000012
As a carrier signal, the respiration signal sr(t) as an information signal, j being the jth harmonic of the heartbeat signal, j being 1,2 …, N;
s2: the detector sensing module amplifies and integrates the state signal of the detected person through an amplifying circuit module and an A/D conversion circuit module, and then transmits the amplified and integrated state signal to the data preprocessing module;
s3: the data preprocessing module preprocesses the state signal s (t) of the detected person, and constructs a calculation model of the state signal s (t) of the detected person and the heartbeat signal
Figure FDA0002990632640000013
A calculation model, the respiratory signal sr(t) calculating a model;
s4: the preprocessing module transmits the preprocessed detected state signal s (t) to the high-order low-pass filtering module;
s5: the high-order low-pass filtering module receives the preprocessed detected state signal s (t) and carries out the heartbeat signal
Figure FDA0002990632640000014
An autocorrelation calculation model is constructed, the influence of the sampling time point and the time delay brought in the signal transmission and receiving process on the frequency of the heartbeat signal is reduced, and the heartbeat signal with accurate fundamental frequency is obtained
Figure FDA0002990632640000015
As a carrier signal;
s6: then the high-order low-pass filtering module multiplies the preprocessed detected state signal s (t) by itself to construct a self amplitude demodulation model to execute self amplitude demodulation, and further, the heartbeat signal with accurate fundamental frequency is subjected to the heartbeat demodulation
Figure FDA0002990632640000016
And the respiratory signal sr(t) carrying out a separation;
s7: separating the S6 step to obtain the heartbeat signal with accurate fundamental frequency
Figure FDA0002990632640000017
And respiratory signal srAnd (t) transmitting the data to a main control module, and wirelessly transmitting the data to the big data cloud platform storage module by the main control module.
2. The method of claim 1, wherein the calculation model of the monitored condition signal S (t) constructed in the step S3 is:
Figure FDA0002990632640000021
3. the method of claim 1, wherein the heartbeat signal generated in step S3 is used to monitor the respiration rate of the patient
Figure FDA0002990632640000022
The calculation model is as follows:
sr(t)＝ar×cos(2πfrt+θj)；
a is arCalculating parameters for the heartbeat signal, said frFor heart beat frequency, thetajA heartbeat frequency amplitude being a jth harmonic of the heartbeat signalAnd (4) an angle.
4. The method of claim 1, wherein the respiration signal S constructed in the step S3 is used as the respiration signal Sr(t) the calculation model is:
Figure FDA0002990632640000023
the above-mentioned
Figure FDA0002990632640000024
Calculating a parameter for the respiratory signal, said
Figure FDA0002990632640000025
A respiratory frequency amplitude angle brought to the jth harmonic of the heartbeat signal; to be provided with
Figure FDA0002990632640000026
The fundamental frequency of the heartbeat signal.
5. The method of claim 1, wherein the step S6 of multiplying the preprocessed detected status signal S (t) by itself to construct a self amplitude demodulation model is as follows:
Figure FDA0002990632640000031
wherein, a'jSeparation parameters of self amplitude demodulation algorithm to respiratory frequency at jth harmonic of heartbeat signal
Figure FDA0002990632640000032
Separate parameters of the heartbeat frequency, a ', for a self amplitude demodulation algorithm'h-jFor breathing frequency by heart at the jth harmonic of the heartbeat signalA 'is a low-frequency separation parameter influenced by hop frequency'h+jA high frequency separation parameter, which is the respiratory frequency affected by the heartbeat frequency at the jth harmonic of the heartbeat signal; a'1×cos(4πfrt) is the lowest frequency component.
6. The method of claim 1, wherein the step of S5 is performed on the heartbeat signal
Figure FDA0002990632640000033
Constructing an autocorrelation calculation model, comprising the steps of:
s51: for the heartbeat signal
Figure FDA0002990632640000034
The time series signal x (t) of the signal power at time t, the autocorrelation model is constructed:
Figure FDA0002990632640000035
wherein l is the sampling time point and the time delay brought by the signal transmission and receiving process, l is more than or equal to 0 and less than n, and n is the number of the sampling time points;
s52: the heartbeat signal is processed by the step S51
Figure FDA0002990632640000041
To 0 to generate a power signal proportional to the instantaneous power of the detector sensing module when the heartbeat signal is present
Figure FDA0002990632640000042
When the average value of the signal power of (1) is shifted to 0, the heartbeat signal
Figure FDA0002990632640000043
Power signal and selfSelf-alignment, the autocorrelation reaches a maximum;
s53: deducing the periodicity of the heartbeat by detecting the peak in the autocorrelation model, and further obtaining the heartbeat signal with accurate fundamental frequency
Figure FDA0002990632640000044
And acts as a carrier signal.
7. The method for noninvasive respiratory rate monitoring of claim 6, wherein in step S51
Figure FDA0002990632640000045
The calculation formula of (2) is as follows:
Figure FDA0002990632640000046
wherein, the
Figure FDA0002990632640000047
Is the average number of samples, said xt+lTime series signal values at time t + l, said xtIs the time series signal value at time t.
8. The method of claim 6, wherein the average of the time series signal values is used to monitor the non-invasive respiratory rate of the ventilator
Figure FDA0002990632640000048
The calculation formula of (2) is as follows:
Figure FDA0002990632640000049
9. the system for noninvasive respiratory rate monitoring of a ventilator according to any one of claims 1-8, comprising a detector sensing module, an amplifying circuit module, an A/D conversion circuit module, a high-order low-pass filtering module, a main control module, and a big data cloud platform storage module.
10. The system of claim 9, further comprising a power supply module for supplying a stable current required for operation of the system, a wireless transceiver module for wirelessly transmitting and sharing data obtained by separation of the main control module to the big data cloud platform storage module, and an interaction function module for sequentially interactively transmitting data collected by the detector sensing module with the amplifying circuit module, the a/D conversion circuit module, and the high-order low-pass filtering module.
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