CN116369888B - Non-contact heart rate variability data acquisition method and device - Google Patents
Non-contact heart rate variability data acquisition method and device Download PDFInfo
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Abstract
The invention discloses a non-contact heart rate variability data acquisition method and a non-contact heart rate variability data acquisition device. The non-contact heart rate variability data acquisition device can acquire the heart rate variability information of the user in real time in daily work and life, can work in a very strong noise environment, and has very good robustness; the HRV information is acquired in a non-contact mode, and compared with a data acquisition mode of attaching electrode plates on human skin, the method has better user experience.
Description
Technical Field
The invention relates to the technical field of vital sign monitoring, in particular to a non-contact heart rate variability data acquisition method and device.
Background
Along with the development of social and economic technologies and the improvement of living standard, people have higher and higher attention to self health and have higher and higher requirements for daily health monitoring. Heart rate is the most basic vital sign of human body, and the pathological sign of human body often reflects from abnormal heart rate, so that the realization of daily real-time monitoring of heart rate has important significance for human health assessment and disease prevention.
Heart rate variability (Heart Rate Variability, HRV) is often used to assess health as an indicator of the health of the human body. Whereas for heart rate variability monitoring, the current main means is the Electrocardiograph (ECG) technique. However, the ECG technology needs to attach an electrode pad to the skin of the human body in the monitoring process, so that the health state of the monitored person can be monitored and mastered in real time through long-time heartbeat monitoring, but most of the traditional monitoring systems use contact equipment, so that the user experience is poor, the daily life of the monitored person is interfered during monitoring, the accuracy of the monitoring effect is limited, and the ECG monitoring system is difficult to be applied to the scene of the daily life of people.
In recent years, non-contact monitoring systems have evolved rapidly. The sensing system based on the optical fiber sensing principle has the characteristics of high sensitivity, good comfort and electromagnetic interference resistance, but the signal to noise ratio of the signals acquired by the optical fiber sensing at present is low, and various physiological signals and noise are aliased and limited by a plurality of factors such as a sensor technology, a sensing mode, a use environment and the like, so that the original signals of a Ballistocardiogram (BCG) acquired by the optical fiber sensing are inevitably polluted by artifact noise, the heart rate signals are difficult to be completely separated by adopting a traditional filtering mode, and the extraction difficulty of the HRV in the heart rate signals is increased.
Disclosure of Invention
In view of the above, embodiments of the present invention are presented to provide a non-contact heart rate variability data acquisition method and a corresponding non-contact heart rate variability data acquisition device that overcome or at least partially solve the above-mentioned problems.
In order to solve the above problems, in one aspect, an embodiment of the present invention discloses a non-contact heart rate variability data acquisition method, wherein the method acquires a signal through a vibration sensor, the vibration sensor is built in a daily product, the daily product comprises a cushion, a mattress, an insole or a pillow, and the method comprises:
acquiring a body vibration pressure signal, and converting the pressure signal into a digital electric signal;
acquiring heart rate variability data from the digital electrical signal;
transmitting the heart rate variability data to a corresponding terminal;
wherein the acquiring heart rate variability data from the digital electrical signal comprises:
filtering the digital electrical signal and separating a ballistocardiogram signal from the digital electrical signal, wherein the filtering comprises removing motion artifacts, respiratory waves and a signal baseline in the digital electrical signal;
Carrying out smoothing treatment and cyclic spectrum estimation on the ballistocardiogram signal through a preset sliding window, judging the signal quality of the ballistocardiogram signal through a signal quality coefficient, removing noise components contained in the ballistocardiogram signal, generating a corresponding heartbeat waveform, and determining the fundamental frequency of the cyclic frequency in the ballistocardiogram signal, wherein the fundamental frequency is an estimated value of the current instantaneous heart rate;
and determining heart rate variability parameters of the ballistocardiogram signals according to the heartbeat waveform and the fundamental frequency, and calculating heart rate variability data in the digital electric signals.
Optionally, the smoothing and cyclic spectrum estimation of the ballistocardiogram signal through a preset sliding window includes:
a sliding window of preset duration is used for carrying out sectional sliding window on the ballistocardiogram signals in preset time, a time-frequency distribution diagram corresponding to the ballistocardiogram signals in the preset time is obtained, and an instantaneous heart rate corresponding to the preset time is obtained according to the time-frequency distribution diagram;
estimating a cyclic spectrum of the ballistocardiogram signal according to the time-frequency distribution diagram and the instantaneous heart rate; cyclic spectrum estimation utilizes cyclostationary characteristics of a heartbeat signal, a random signal x (t) of a non-stationary ballistocardiogram continuous in a given time, and a cyclostationary autocorrelation function corresponding to the random signal x (t) is For->Fourier transform can be performed to obtain spectrum correlation functionThe calculation formula of the spectrum correlation function is as follows:
where f is the spectral frequency, τ is the time shift, α is the cyclic frequency, e is the natural constant, and j is the imaginary symbol.
Optionally, the determining the signal quality of the ballistocardiogram signal according to the signal quality coefficient, and removing the noise component included in the ballistocardiogram signal includes:
calculating a signal quality coefficient of the ballistocardiogram signal within a preset time;
judging whether the ballistocardiogram signal contains a noise component according to the signal quality coefficient, and eliminating the noise component in the ballistocardiogram;
the specific calculation formula of the signal quality coefficient is as follows:
wherein,η x for cyclic spectral density, eta x And (alpha) is obtained by integrating the spectrum frequency f of the spectrum correlation function, wherein xi is the fundamental frequency of the circulating frequency, alpha is the circulating frequency, and mu is the circulating frequency range.
Optionally, the determining the heart rate variability parameter of the ballistocardiogram signal according to the heartbeat waveform and the fundamental frequency includes:
determining an instantaneous heart rate value in preset time according to the heartbeat waveform and the fundamental frequency;
estimating duration of a current heartbeat and a subsequent heartbeat according to the instantaneous heart rate value;
According to the duration time of the current heartbeat and the subsequent heartbeat, determining the time delay of the current heartbeat and the subsequent heartbeat, the positions of the current heartbeat and the subsequent heartbeat and the highest peak J peak through cross-correlation calculation;
and calculating the time interval of adjacent J peaks, and acquiring heart rate variability data of the ballistocardiogram signals.
On the other hand, the embodiment of the invention discloses a non-contact heart rate variability data acquisition device, which acquires signals through a vibration sensor, wherein the vibration sensor is arranged in daily necessities, the daily necessities comprise a cushion, a mattress, an insole or a pillow, and the device comprises:
the signal acquisition module comprises a vibration sensor, wherein the vibration sensor is used for acquiring a body vibration pressure signal and converting the pressure signal into a digital electric signal;
a heart rate variability data acquisition module for acquiring heart rate variability data from the digital electrical signal;
the storage module is used for storing heart rate variability data and sending the heart rate variability data to a corresponding terminal;
the communication module is used for connecting the heart rate variability data acquisition module and the storage module;
Wherein, the heart rate variability data acquisition module includes:
the signal processing sub-module is used for carrying out filtering processing on the digital electric signal and separating a ballistocardiogram signal from the digital electric signal, and the filtering processing comprises removing motion artifact, respiratory wave and signal baseline in the digital electric signal;
the first data processing sub-module is used for carrying out smoothing processing and cyclic spectrum estimation on the ballistocardiogram signal through a preset sliding window, judging the signal quality of the ballistocardiogram signal through a signal quality coefficient, removing noise components contained in the ballistocardiogram signal, generating corresponding heartbeat waveforms, and determining the fundamental frequency of the cyclic frequency in the ballistocardiogram signal, wherein the fundamental frequency is an estimated value of the current instantaneous heart rate;
and the second data processing sub-module is used for determining heart rate variability parameters of the ballistocardiogram signals according to the heartbeat waveform and the fundamental frequency and calculating heart rate variability data in the digital electric signals.
Optionally, the first data processing submodule includes:
The first data processing unit is used for carrying out sectional sliding window on the ballistocardiogram signals in the preset time through a sliding window with preset time length to obtain a time-frequency distribution diagram corresponding to the ballistocardiogram signals in the preset time, and acquiring an instantaneous heart rate corresponding to the preset time according to the time-frequency distribution diagram;
a second data processing unit for estimating a cyclic spectrum of the ballistocardiogram signal from the time-frequency distribution map and the instantaneous heart rate;
cyclic spectrum estimation utilizes cyclostationary characteristics of a heartbeat signal, a random signal x (t) of a non-stationary ballistocardiogram continuous in a given time, and a cyclostationary autocorrelation function corresponding to the random signal x (t) isFor->Fourier transform can be performed to obtain a spectral correlation function +.>The calculation formula of the spectrum correlation function is as follows:
where f is the spectral frequency, τ is the time shift, and α is the cyclic frequency.
Optionally, the first data processing submodule includes:
the third data processing unit is used for calculating the signal quality coefficient of the ballistocardiogram signal in the preset time;
the fourth data processing unit is used for judging whether the ballistocardiogram signal contains a noise component according to the signal quality coefficient and eliminating the noise component in the ballistocardiogram;
The specific calculation formula of the signal quality coefficient is as follows:
wherein,η x for cyclic spectral density, eta x And (alpha) is obtained by integrating the spectrum frequency f of the spectrum correlation function, wherein xi is the fundamental frequency of the circulating frequency, alpha is the circulating frequency, and mu is the circulating frequency range.
Optionally, the second data processing submodule includes:
a fifth data processing unit, configured to determine an instantaneous heart rate value within a preset time according to the heartbeat waveform and the fundamental frequency;
a sixth data processing unit for estimating the duration of the current and subsequent heart beats from the instantaneous heart rate value;
the seventh data processing unit is used for determining the time delay of the current heartbeat and the subsequent heartbeat, the positions of the current heartbeat and the subsequent heartbeat and the highest peak J peak through cross-correlation calculation according to the duration of the current heartbeat and the subsequent heartbeat;
and the eighth data processing unit is used for calculating the time interval of the adjacent J peaks and acquiring the heart rate variability parameter of the ballistocardiogram signal.
In another aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes a processor, a memory, and a computer program stored on the memory and capable of running on the processor, where the computer program when executed by the processor implements the steps of the non-contact heart rate variability data acquisition method.
In another aspect, embodiments of the present invention also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the non-contact heart rate variability data acquisition method.
The embodiment of the invention discloses a non-contact heart rate variability data acquisition method and a non-contact heart rate variability data acquisition device. By adopting the technical scheme, the signal acquisition module and the heart rate variability data acquisition module in the non-contact heart rate variability data acquisition device can be arranged in daily necessities such as a cushion, a mattress or a pillow, so that HRV information of a user can be acquired in real time in daily work and life, long-term continuous monitoring can be realized, and a personal health file is established for the user; the invention collects BCG signals based on the high-sensitivity vibration sensor, acquires real-time heart rate through the cycle frequency by utilizing the cycle stability characteristic of the heart beat activity, so that the invention can work in a very strong noise environment, has very good robustness, and can improve the accuracy and reliability of the collected signals; the HRV information is acquired in a non-contact mode, and compared with a data acquisition mode of attaching electrode plates on human skin, the method has better user experience.
Drawings
Fig. 1 is a flowchart of steps of a non-contact heart rate variability data acquisition method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of time-frequency distribution in a process of separating a ballistocardiogram signal according to an embodiment of the present invention;
fig. 3 is a block diagram of a non-contact heart rate variability data acquisition device according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a non-contact method for obtaining HRV data by using signals of normal heart rate lying down and calculation results according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of HRV data obtained by a non-contact method for obtaining signals of normal heart rate lying on one side and calculating results according to an embodiment of the present invention;
fig. 6 and fig. 7 are schematic diagrams illustrating a comparison between HRV data obtained by a non-contact heart rate variability data obtaining method and HRV data obtained by an ECG technique according to an embodiment of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Heart rate variability (Heart Rate Variability, HRV) is often used to assess health as an indicator of the health of the human body. Whereas for heart rate variability monitoring, the current main means is the Electrocardiograph (ECG) technique. Heart rate variability refers to the phenomenon in which the instantaneous heart rate or instantaneous cardiac cycle varies slightly, i.e. the successive heart beat R-R intervals fluctuate continuously. Information about the nervous system for cardiac rhythm control can be obtained by analysis of heart rate variability, HRV analysis is a contact measurement and a non-contact measurement from the measurement method, and there are time domain analysis, frequency domain analysis and non-linear analysis from the analysis method. In the prior art, the RRI sequence is derived by using a contact type HRV analysis method, an electrocardiograph is used for measuring electrocardiosignals from the chest by a lead electrode, and then electrocardiosignal period changes are recorded; whereas derivation of RRI sequences using the method of contactless HRV analysis is based mainly on HRV analysis of the acquired dynamic heart rate signals. Researchers explore a plurality of techniques for realizing a method for monitoring heart rate variability, and Ballistocardiogram (BCG) technology is a technology with very wide application potential because of the characteristics of non-contact sensing mode, suitability for daily life scenes and the like. The sensing system based on the optical fiber sensing principle has the characteristics of high sensitivity, good comfort and electromagnetic interference resistance, can be built in daily necessities such as a cushion, a mattress, an insole or a pillow, and is convenient for acquiring BCG signals in daily life scenes.
However, BCG signals are much more complex than ECG signals in terms of signal waveforms, and extraction of BCG signals appears more difficult. Meanwhile, the method is limited by a plurality of factors such as sensor technology, a sensing mode, a use environment and the like, so that the BCG original signal is inevitably polluted by artifact noise, and the extraction difficulty is further increased. For this reason, many methods for extracting BCG have been proposed by the scholars, but it is still difficult to solve the stability and robustness problems of the extraction method. The method based on frequency domain analysis can only be used for estimating the average heart rate, and has low accuracy; the method based on template matching has higher requirements on signal quality; the machine learning-based method requires a large number of calibrated signal libraries for training and cannot adapt to variable BCG signals. Therefore, there is a need for a stable, accurate, robust method of extracting heart rate variability from BCG.
Fig. 1 is a flowchart of steps of a non-contact heart rate variability data acquisition method according to an embodiment of the present invention, where the method acquires signals through a vibration sensor, and the vibration sensor may be built in an article of daily use, where the article of daily use includes a cushion, a mattress, an insole, or a pillow, and the method includes:
Step 101, acquiring a body vibration pressure signal, and converting the pressure signal into a digital electric signal;
in this embodiment, the sensing system based on the optical fiber sensing principle has the characteristics of high sensitivity, good comfort and electromagnetic interference resistance, and can embed the vibration sensor with high sensitivity into daily necessities such as a cushion, a mattress, an insole or a pillow, so as to be convenient for collecting BCG signals of users in daily life scenes. When the cushion is specifically used, the optical fiber sensor is built in the cushion, after a human body contacts the cushion, the optical fiber sensor built in the cushion is pressed to acquire a body vibration pressure signal, and the pressure signal is converted into a digital electric signal.
Note that the types of vibration sensors include, but are not limited to, piezoelectric, fiber-optic type vibration sensors, and the number includes, but is not limited to, 1 sensor. Because the vibration sensor and the user collect the pressure signal in a non-contact mode, the acquisition of the body vibration pressure signal of the human body can be realized only by the contact between the human body and the cushion, and the problem that the existing wearable product acquisition signal needs to directly contact a test object and restrict the action of the test object to cause inconvenience is solved. For example, the vibration sensor can adopt an optical fiber bending loss type vibration sensor, the signal sampling rate is 256Hz, the sensor is embedded into the cushion and laid below the normal cushion, and vital sign signals in the sleeping process of a human body are monitored.
102, acquiring heart rate variability data from the digital electric signals;
specifically, acquiring heart rate variability data from digital electrical signals includes: filtering the digital electric signal, separating a ballistocardiogram signal from the digital electric signal, carrying out smoothing treatment and cyclic spectrum estimation on the ballistocardiogram signal through a preset sliding window, judging the signal quality of the ballistocardiogram signal through a signal quality coefficient, removing noise components contained in the ballistocardiogram signal, generating corresponding heartbeat waveforms, and determining the fundamental frequency of the cyclic frequency in the ballistocardiogram signal, wherein the fundamental frequency is an estimated value of the current instantaneous heart rate; and determining heart rate variability parameters of the ballistocardiogram signals according to the heartbeat waveform and the fundamental frequency, and counting heart rate variability data in the digital electric signals.
The filtering process includes removing motion artifacts, respiratory waves, and signal baselines from the digital electrical signal. The digital electric signals are signals processed by the sensor signal acquisition circuit, the digital electric signals comprise baseline drift, respiratory wave and body movement artifact, the digital electric signals need to be preprocessed, and the ballistocardiogram signals are separated from the digital electric signals. Illustratively, the preprocessing may employ a hard threshold to remove the circuit saturation signal and scale to body motion, with a band pass filter having a cutoff frequency of 0.5-15Hz for the denoising operation. The preprocessing may also be performed by preprocessing the aliased signal using a second order IIR low-pass filtering process, and the BCG signal may be selected to have a low-pass cut-off frequency of 1Hz, and the respiratory signal may be selected to have a low-pass cut-off frequency of 0.3Hz, thereby separating the BCG signal from the aliased signal consisting of BCG, respiratory, body motion, and noise. The preprocessing can collect BCG signals of the aliasing strong interference in the initial state, and perform baseline removal processing by adopting differential filtering, namely deleting the combined baseline interference in the BCG signals by adopting a 0.02Hz high-pass filter, so as to complete the preprocessing of the BCG signals.
The smoothing and cyclic spectrum estimation of the ballistocardiogram signal through a preset sliding window comprises the following steps: a sliding window of preset duration is used for carrying out sectional sliding window on the ballistocardiogram signals in preset time, a time-frequency distribution diagram corresponding to the ballistocardiogram signals in the preset time is obtained, and an instantaneous heart rate corresponding to the preset time is obtained according to the time-frequency distribution diagram; estimating a cyclic spectrum of the ballistocardiogram signal according to the time-frequency distribution diagram and the instantaneous heart rate; the cyclic spectrum estimation uses the cyclostationary characteristic of the heartbeat signal, given a time-continuous non-stationary ballistocardiogram random signal x (t), the cyclostationary autocorrelation function of which isFor->Fourier transform can be performed to obtain a spectral correlation function +.>The specific calculation formula for calculating the spectrum correlation function is as follows:
where f is the spectral frequency, τ is the time shift, and α is the cyclic frequency.
It should be noted that, a person skilled in the art may set the preset time of the ballistocardiogram signal and the time window step of the sliding window according to actual needs, and the embodiment of the present application does not limit the preset time. Illustratively, the segmented sliding window is a sliding time window of 5-10 seconds long for the data processing window, with a time window step size of between 0.1-1 second. For example, the continuous signal duration of the ballistocardiogram signal may be 5 seconds, and the corresponding time window step length may be 0.2 seconds as a sliding window, so as to obtain a time-frequency distribution with higher resolution and obtain a more accurate instantaneous heart rate. The cyclic spectrum estimation is to calculate the cyclic spectrum of the BCG signal by using the cyclostationary characteristic of the heartbeat signal, and since the harmonic component contained in the BCG signal is very complex, it is difficult to accurately identify the fundamental frequency of the heartbeat frequency from the conventional spectrum frequency. From the above formula, it can be seen that the spectrum estimation in the method of the application does not depend on the spectrum frequency of the signal, but estimates the cyclic frequency, which has extremely high robustness to the signal noise. Because only the signal is cyclostationary and the noise does not have cyclostationary properties, the noise has a very difficult impact on the estimation of the heart beat frequency. This enables the method to handle BCG signals with low signal-to-noise ratios. Meanwhile, the waveform of the BCG signal shows variable characteristics due to various factors such as the collected environment, common lying posture and different people, but the periodic characteristic of the heart beat of the BCG signal is not changed all the time, so that the cyclic spectrum estimation algorithm provided by the application has good robustness to the variability of the waveform of the BCG signal, and the universality of the cyclic spectrum estimation algorithm for processing different BCG signals is ensured.
Judging the signal quality of the ballistocardiogram signal through the signal quality coefficient, and removing noise components contained in the ballistocardiogram signal comprises the following steps: calculating a signal quality coefficient of the ballistocardiogram signal within a preset time; judging whether the ballistocardiogram signal contains a noise component according to the signal quality coefficient, and eliminating the noise component in the ballistocardiogram;
the specific calculation formula of the signal quality coefficient is as follows:
wherein eta x For cyclic spectral density, eta x And (alpha) is obtained by integrating the spectrum frequency f of the spectrum correlation function, wherein xi is the fundamental frequency of the circulating frequency, alpha is the circulating frequency, and mu is the circulating frequency range.
It should be noted that, the filtering process generally can only filter most of the noise, and some small body motion artifacts may still exist in the BCG signal and cannot be removed by the common filtering process. Thus, small body movement artifacts need to be identified and removed before detecting the specific location of the heart beat.
Determining heart rate variability parameters of the ballistocardiogram signal according to the heartbeat waveform and the fundamental frequency comprises: determining an instantaneous heart rate value in a preset time according to the heartbeat waveform and the fundamental frequency; estimating duration of the current heartbeat and the subsequent heartbeat according to the instantaneous heart rate value; according to the duration time of the current heartbeat and the subsequent heartbeat, determining the time delay of the current heartbeat and the subsequent heartbeat, the positions of the current heartbeat and the subsequent heartbeat and the highest peak J peak through cross-correlation calculation; and calculating the time interval of adjacent J peaks, and acquiring heart rate variability parameters of the ballistocardiogram signals.
Fig. 2 is a schematic diagram of time-frequency distribution in a process of separating a ballistocardiogram signal according to an embodiment of the present invention, where (a) is a schematic diagram of a digital electric signal, (b) is a schematic diagram of a digital electric signal after filtering, and (c) is a schematic diagram of a digital electric signal after filtering and identifying a signal quality coefficient, and one clear ridge line is located near 1Hz, which indicates that the heart beat frequency of the subject is about 1Hz, that is, 60 times/min. The ridge line is formed by the fundamental frequency of the circulating frequency, so that the ridge line is found out from the graph and a specific numerical value is calculated, namely the current instantaneous heart rate. (c) And (c) providing a mode of identifying the small artifact noise for the signal quality coefficient, wherein a noise signal threshold can be set according to actual requirements by a person skilled in the art, and when the signal quality coefficient is smaller than a preset threshold, the noise is determined and the removal processing is performed. It can be seen that weak artifacts, which are difficult to identify by other methods, can be easily identified by using the signal quality coefficient. The quality coefficient provided by the invention is based on cyclic spectrum estimation, is not influenced by low signal-to-noise ratio of the signal, and has good robustness to noise, so that the heartbeat signal is ensured not to be mistakenly identified as a body movement artifact.
According to the instantaneous heart rate estimated value, estimating the duration of the current and subsequent heart beats, intercepting two signal segments of which the heart beats are estimated, taking the highest peak in the two signal segments as the J peak of one heart beat because the J peak is the highest peak in the BCG heart cycle, searching the time delay between the two heart beats through cross-correlation calculation, and determining the position of the other J peak, thereby determining the specific positions of the front heart beat and the rear heart beat. The above-mentioned J peak is the highest peak in the BCG signal of a single cardiac cycle, represents the process of systole pumping blood, and is the most characteristic peak in a single BCG cycle.
The embodiment of the invention discloses a non-contact heart rate variability data acquisition method, which comprises the steps of acquiring a body vibration pressure signal, converting the pressure signal into a digital electric signal, filtering the digital electric signal, separating a heart shock image signal from the digital electric signal, carrying out smoothing treatment and cyclic spectrum estimation on the heart shock image signal through a preset sliding window, judging the signal quality of the heart shock image signal through a signal quality coefficient, removing noise components contained in the heart shock image signal, generating a corresponding heartbeat waveform, determining the fundamental frequency of the cyclic frequency in the heart shock image signal, determining the heart rate variability parameter of the heart shock image signal according to the heartbeat waveform and the fundamental frequency, counting the heart rate variability data in the digital electric signal, and sending the heart rate variability data to a corresponding terminal. By adopting the technical scheme, the signal acquisition module and the heart rate variability data acquisition module in the non-contact heart rate variability data acquisition device can be arranged in daily necessities such as a cushion, a mattress or a pillow, so that HRV information of a user can be acquired in real time in daily work and life, long-term continuous monitoring can be realized, and a personal health file is established for the user; the invention collects BCG signals based on the high-sensitivity vibration sensor, acquires real-time heart rate through the cycle frequency by utilizing the cycle stability characteristic of the heart beat activity, so that the invention can work in a very strong noise environment, has very good robustness, and can improve the accuracy and reliability of the collected signals; the HRV information is acquired in a non-contact mode, and compared with a data acquisition mode of attaching electrode plates on human skin, the method has better user experience.
It should be noted that, for simplicity of description, the method embodiments are shown as a series of acts, but it should be understood by those skilled in the art that the embodiments are not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the invention.
In order to implement the above-mentioned non-contact heart rate variability data acquisition method, fig. 3 is a block diagram of a non-contact heart rate variability data acquisition device according to an embodiment of the present invention, where the device acquires signals through a vibration sensor, the vibration sensor is built in an article of daily use, and the article of daily use includes a cushion, a mattress, an insole, or a pillow, and the device includes:
the signal acquisition module 301, the signal acquisition module 301 includes a vibration sensor, the vibration sensor is used for acquiring a body vibration pressure signal and converting the pressure signal into a digital electric signal;
a heart rate variability data acquisition module 302, wherein the heart rate variability data acquisition module 302 is configured to acquire heart rate variability data from the digital electrical signal;
The storage module 303 is used for storing heart rate variability data and sending the heart rate variability data to a corresponding terminal;
a communication module 304, configured to connect the heart rate variability data acquisition module 302 and the storage module 303;
wherein the heart rate variability data acquisition module 302 may comprise:
the signal processing sub-module is used for carrying out filtering processing on the digital electric signal and separating a ballistocardiogram signal from the digital electric signal, and the filtering processing comprises removing motion artifact, respiratory wave and signal baseline in the digital electric signal;
the first data processing sub-module is used for carrying out smoothing processing and cyclic spectrum estimation on the ballistocardiogram signal through a preset sliding window, judging the signal quality of the ballistocardiogram signal through a signal quality coefficient, removing noise components contained in the ballistocardiogram signal, generating corresponding heartbeat waveforms, and determining the fundamental frequency of the cyclic frequency in the ballistocardiogram signal, wherein the fundamental frequency is an estimated value of the current instantaneous heart rate;
and the second data processing sub-module is used for determining heart rate variability parameters of the ballistocardiogram signals according to the heartbeat waveform and the fundamental frequency and calculating heart rate variability data in the digital electric signals.
It should be noted that, the communication module 304 may be used to connect the heart rate variability data acquisition module 302 and the storage module 303, and the storage module 303 may be connected to a cloud platform for data storage and processing, and send HRV data information to an end user through the cloud platform. The connection mode of the communication module 304 and the cloud platform includes, but is not limited to, a module adopting a wired communication mode and a wireless communication mode. The terminal is an electronic device for receiving and viewing the HRV data information of the user, and can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart sound box or a smart watch.
In an alternative embodiment, the first data processing sub-module may include:
the first data processing unit is used for carrying out sectional sliding window on the ballistocardiogram signals in the preset time through a sliding window with preset time length to obtain a time-frequency distribution diagram corresponding to the ballistocardiogram signals in the preset time, and acquiring an instantaneous heart rate corresponding to the preset time according to the time-frequency distribution diagram;
a second data processing unit for estimating a cyclic spectrum of the ballistocardiogram signal from the time-frequency distribution map and the instantaneous heart rate;
The cyclic spectrum estimation uses the cyclostationary characteristic of the heartbeat signal, given a time-continuous non-stationary ballistocardiogram random signal x (t), the cyclostationary autocorrelation function of which isFor->Fourier transform can be performed to obtain spectrum correlation functionThe specific calculation formula for calculating the spectrum correlation function is as follows:
where f is the spectral frequency, τ is the time shift, and α is the cyclic frequency.
In an alternative embodiment, the first data processing sub-module may include:
the third data processing unit is used for calculating the signal quality coefficient of the ballistocardiogram signal in the preset time;
the fourth data processing unit is used for judging whether the ballistocardiogram signal contains a noise component according to the signal quality coefficient and eliminating the noise component in the ballistocardiogram;
the specific calculation formula of the signal quality coefficient is as follows:
wherein eta x For cyclic spectral density, eta x And (alpha) is obtained by integrating the spectrum frequency f of the spectrum correlation function, wherein xi is the fundamental frequency of the circulating frequency, alpha is the circulating frequency, and mu is the circulating frequency range.
In an alternative embodiment, the second data processing sub-module may include:
a fifth data processing unit, configured to determine an instantaneous heart rate value within a preset time according to the heartbeat waveform and the fundamental frequency;
A sixth data processing unit for estimating the duration of the current and subsequent heart beats from the instantaneous heart rate value;
the seventh data processing unit is used for determining the time delay of the current heartbeat and the subsequent heartbeat, the positions of the current heartbeat and the subsequent heartbeat and the highest peak J peak through cross-correlation calculation according to the duration of the current heartbeat and the subsequent heartbeat;
and the eighth data processing unit is used for calculating the time interval of the adjacent J peaks and acquiring the heart rate variability parameter of the ballistocardiogram signal.
The embodiment of the invention discloses a non-contact heart rate variability data acquisition device, which is used for acquiring a body vibration pressure signal, converting the pressure signal into a digital electric signal, filtering the digital electric signal, separating a heart attack graph signal from the digital electric signal, carrying out smoothing processing and cyclic spectrum estimation on the heart attack graph signal through a preset sliding window, judging the signal quality of the heart attack graph signal through a signal quality coefficient, eliminating noise components contained in the heart attack graph signal, generating a corresponding heartbeat waveform, determining the fundamental frequency of the cyclic frequency in the heart attack graph signal, determining the heart rate variability parameter of the heart attack graph signal according to the heartbeat waveform and the fundamental frequency, counting the heart rate variability data in the digital electric signal, and sending the heart rate variability data to a corresponding terminal. By adopting the technical scheme, the signal acquisition module and the heart rate variability data acquisition module in the non-contact heart rate variability data acquisition device can be arranged in daily necessities such as a cushion, a mattress or a pillow, so that HRV information of a user can be acquired in real time in daily work and life, long-term continuous monitoring can be realized, and a personal health file is established for the user; the invention collects BCG signals based on the high-sensitivity vibration sensor, acquires real-time heart rate through the cycle frequency by utilizing the cycle stability characteristic of the heart beat activity, so that the invention can work in a very strong noise environment, has very good robustness, and can improve the accuracy and reliability of the collected signals; the HRV information is acquired in a non-contact mode, and compared with a data acquisition mode of attaching electrode plates on human skin, the method has better user experience.
In some embodiments, fig. 4 and fig. 5 are schematic diagrams of HRV data acquired by a non-contact heart rate variability data acquisition method according to an embodiment of the present invention. Wherein, the calculation results of a section of example data are shown in fig. 4 and fig. 5, and it can be seen that the instantaneous heart rate estimated by the method provided by the invention (shown by solid line) is highly consistent with the final calculation result (shown by black triangle in fig. 4b and fig. 5 b), and all J peaks can be accurately found (shown by the careful dots in fig. 4a and fig. 5 a). Fig. 4 shows the signals and calculation results of a normal heart rate lying flat, and fig. 5 shows the signals and calculation results of a lying on the side. It can be seen that the lateral BCG signal has obvious change, the waveform is more complex, and the influence of noise is obvious, but the calculation result shows that the method provided by the invention can still accurately find the J peak, which also proves that the method has good robustness.
In some embodiments, fig. 6 and fig. 7 are schematic diagrams of a comparison between HRV data obtained by a non-contact heart rate variability data obtaining method and HRV data obtained by an ECG technique according to an embodiment of the present invention. Fig. 6a-b show the calculation result of the method and the synchronous ECG monitoring signal, and it can be seen that the R peak of the ECG signal and the J peak of the BCG signal of each heartbeat can both correspond well, which indicates that the BCG signal acquisition accuracy is very high. Fig. 7a-b show a comparison of the calculated results, JJI is HRV data acquired by BCG, RRI is HRV data acquired by ECG, and example results show that the output of HRV for 5 minutes of the present invention is highly consistent with ECG monitoring results. Fig. 7c also shows the error of JJI and RRI, which shows that the maximum absolute error remains within 16 ms and the average absolute error is 4.4 ms, further illustrating the high accuracy and reliability of the present invention.
In another aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes a processor, a memory, and a computer program stored on the memory and capable of running on the processor, where the computer program when executed by the processor implements the steps of the non-contact heart rate variability data acquisition method.
In another aspect, embodiments of the present invention also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the non-contact heart rate variability data acquisition method.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal 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 terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The above description of a method for acquiring non-contact heart rate variability data and a device for acquiring non-contact heart rate variability data provided by the present invention are provided in detail, and specific examples are used herein to illustrate the principles and embodiments of the present invention, and those skilled in the art, based on the spirit and scope of the present invention, will be able to modify the detailed description of the invention without limiting the scope of the invention.
Claims (9)
1. A method of non-contact heart rate variability data acquisition, the method comprising acquiring a signal via a vibration sensor built into a commodity, the commodity comprising a cushion, mattress, insole or pillow, the method comprising:
acquiring a body vibration pressure signal, and converting the pressure signal into a digital electric signal;
acquiring heart rate variability data from the digital electrical signal;
transmitting the heart rate variability data to a corresponding terminal;
wherein the acquiring heart rate variability data from the digital electrical signal comprises:
filtering the digital electrical signal and separating a ballistocardiogram signal from the digital electrical signal, wherein the filtering comprises removing motion artifacts, respiratory waves and a signal baseline in the digital electrical signal;
Carrying out smoothing treatment and cyclic spectrum estimation on the ballistocardiogram signal through a preset sliding window, judging the signal quality of the ballistocardiogram signal through a signal quality coefficient, removing noise components contained in the ballistocardiogram signal, generating a corresponding heartbeat waveform, and determining the fundamental frequency of the cyclic frequency in the ballistocardiogram signal, wherein the fundamental frequency is an estimated value of the current instantaneous heart rate;
determining heart rate variability parameters of the ballistocardiogram signals according to the heartbeat waveform and the fundamental frequency, and calculating heart rate variability data in the digital electric signals;
the determining heart rate variability parameters of the ballistocardiogram signal according to the heartbeat waveform and the fundamental frequency comprises:
determining an instantaneous heart rate value in preset time according to the heartbeat waveform and the fundamental frequency;
estimating duration of a current heartbeat and a subsequent heartbeat according to the instantaneous heart rate value;
according to the duration time of the current heartbeat and the subsequent heartbeat, determining the time delay of the current heartbeat and the subsequent heartbeat, the positions of the current heartbeat and the subsequent heartbeat and the highest peak J peak through cross-correlation calculation;
and calculating the time interval of adjacent J peaks, and acquiring heart rate variability parameters of the ballistocardiogram signals.
2. The method of claim 1, wherein smoothing and cyclic spectrum estimation of the ballistocardiogram signal through a preset sliding window comprises:
a sliding window of preset duration is used for carrying out sectional sliding window on the ballistocardiogram signals in preset time, a time-frequency distribution diagram corresponding to the ballistocardiogram signals in the preset time is obtained, and an instantaneous heart rate corresponding to the preset time is obtained according to the time-frequency distribution diagram;
estimating a cyclic spectrum of the ballistocardiogram signal according to the time-frequency distribution diagram and the instantaneous heart rate; cyclic spectrum estimation utilizes cyclostationary characteristics of a heartbeat signal, a random signal x (t) of a non-stationary ballistocardiogram continuous in a given time, and a cyclostationary autocorrelation function corresponding to the random signal x (t) isFor->Fourier transform can be performed to obtain spectrum correlation functionThe calculation formula of the spectrum correlation function is as follows:
where f is the spectral frequency, τ is the time shift, α is the cyclic frequency, e is the natural constant, and j is the imaginary symbol.
3. The method according to claim 2, wherein the determining the signal quality of the ballistocardiogram signal by the signal quality coefficient, and removing the noise component included in the ballistocardiogram signal includes:
Calculating a signal quality coefficient of the ballistocardiogram signal within a preset time;
judging whether the ballistocardiogram signal contains a noise component according to the signal quality coefficient, and eliminating the noise component in the ballistocardiogram;
the specific calculation formula of the signal quality coefficient Q (ζ) is as follows:
wherein,η x (alpha) is the cyclic spectral density, eta x And (alpha) is obtained by integrating the spectrum frequency f of the spectrum correlation function, wherein xi is the fundamental frequency of the circulating frequency, alpha is the circulating frequency, and mu is the circulating frequency range.
4. A non-contact heart rate variability data acquisition device, characterized in that the device acquires signals through a vibration sensor, the vibration sensor being built into a commodity, the commodity comprising a cushion, a mattress, an insole or a pillow, the device comprising:
the signal acquisition module comprises a vibration sensor, wherein the vibration sensor is used for acquiring a body vibration pressure signal and converting the pressure signal into a digital electric signal;
a heart rate variability data acquisition module for acquiring heart rate variability data from the digital electrical signal;
the storage module is used for storing heart rate variability data and sending the heart rate variability data to a corresponding terminal;
The communication module is used for connecting the heart rate variability data acquisition module and the storage module;
wherein, the heart rate variability data acquisition module includes:
the signal processing sub-module is used for carrying out filtering processing on the digital electric signal and separating a ballistocardiogram signal from the digital electric signal, and the filtering processing comprises removing motion artifact, respiratory wave and signal baseline in the digital electric signal;
the first data processing sub-module is used for carrying out smoothing processing and cyclic spectrum estimation on the ballistocardiogram signal through a preset sliding window, judging the signal quality of the ballistocardiogram signal through a signal quality coefficient, removing noise components contained in the ballistocardiogram signal, generating corresponding heartbeat waveforms, and determining the fundamental frequency of the cyclic frequency in the ballistocardiogram signal, wherein the fundamental frequency is an estimated value of the current instantaneous heart rate;
a second data processing sub-module for determining a heart rate variability parameter of the ballistocardiogram signal from the heartbeat waveform and the fundamental frequency and calculating heart rate variability data in the digital electrical signal; the determining heart rate variability parameters of the ballistocardiogram signal according to the heartbeat waveform and the fundamental frequency comprises:
Determining an instantaneous heart rate value in preset time according to the heartbeat waveform and the fundamental frequency;
estimating duration of a current heartbeat and a subsequent heartbeat according to the instantaneous heart rate value;
according to the duration time of the current heartbeat and the subsequent heartbeat, determining the time delay of the current heartbeat and the subsequent heartbeat, the positions of the current heartbeat and the subsequent heartbeat and the highest peak J peak through cross-correlation calculation;
and calculating the time interval of adjacent J peaks, and acquiring heart rate variability parameters of the ballistocardiogram signals.
5. The apparatus of claim 4, wherein the first data processing sub-module comprises:
the first data processing unit is used for carrying out sectional sliding window on the ballistocardiogram signals in the preset time through a sliding window with preset time length to obtain a time-frequency distribution diagram corresponding to the ballistocardiogram signals in the preset time, and acquiring an instantaneous heart rate corresponding to the preset time according to the time-frequency distribution diagram;
a second data processing unit for estimating a cyclic spectrum of the ballistocardiogram signal from the time-frequency distribution map and the instantaneous heart rate;
cyclic spectrum estimation utilizes cyclostationary characteristics of a heartbeat signal, a random signal x (t) of a non-stationary ballistocardiogram continuous in a given time, and a cyclostationary autocorrelation function corresponding to the random signal x (t) is For->Fourier transform can be performed to obtain a spectral correlation function +.>The calculation formula of the spectrum correlation function is as follows:
where f is the spectral frequency, τ is the time shift, α is the cyclic frequency, e is the natural constant, and j is the imaginary symbol.
6. The apparatus of claim 5, wherein the first data processing sub-module comprises:
the third data processing unit is used for calculating the signal quality coefficient of the ballistocardiogram signal in the preset time;
the fourth data processing unit is used for judging whether the ballistocardiogram signal contains a noise component according to the signal quality coefficient and eliminating the noise component in the ballistocardiogram;
the specific calculation formula of the signal quality coefficient is as follows:
wherein,η x (alpha) is the cyclic spectral density, eta x And (alpha) is obtained by integrating the spectrum frequency f of the spectrum correlation function, wherein xi is the fundamental frequency of the circulating frequency, alpha is the circulating frequency, and mu is the circulating frequency range.
7. The apparatus of claim 4, wherein the second data processing submodule comprises:
a fifth data processing unit, configured to determine an instantaneous heart rate value within a preset time according to the heartbeat waveform and the fundamental frequency;
a sixth data processing unit for estimating the duration of the current and subsequent heart beats from the instantaneous heart rate value;
The seventh data processing unit is used for determining the time delay of the current heartbeat and the subsequent heartbeat, the positions of the current heartbeat and the subsequent heartbeat and the highest peak J peak through cross-correlation calculation according to the duration of the current heartbeat and the subsequent heartbeat;
and the eighth data processing unit is used for calculating the time interval of the adjacent J peaks and acquiring the heart rate variability parameter of the ballistocardiogram signal.
8. An electronic device, comprising: a processor, a memory and a computer program stored on the memory and capable of running on the processor, which when executed by the processor performs the steps of the method according to any of claims 1-3.
9. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of the method according to any of claims 1-3.
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