CN111345801B - Human body beat-by-beat heart rate measuring device and method based on particle filtering - Google Patents
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Abstract
The invention discloses a human body beat-by-beat heart rate measuring device based on particle filtering, which comprises a patch module and an electronic module, wherein the patch module is tightly contacted with the surface of the skin of a human body and acquires signals, the electronic module is positioned on one side of the patch module, which is far away from the surface of the skin of the human body, and the electronic module is used for processing the signals acquired by the patch module. The device has the advantages of multi-parameter integration, small size, light weight, low power consumption, suitability for long-term monitoring and the like, probability fusion is carried out on an observed value and a state value based on a particle filter algorithm, a reliable and stable human body beat-by-beat heart rate measuring mode is provided for a user, and the device can be used as a feasible means for family or community health monitoring. The invention further provides a human body beat-to-beat heart rate measuring method based on particle filtering.
Description
Technical Field
The invention relates to a human body beat-by-beat heart rate measuring device and method based on particle filtering, and belongs to the technical field of heart rate measurement.
Background
At present, the death rate of cardiovascular diseases is the first place, and the death rate of cardiovascular diseases accounts for nearly 30% of the total death rate in China, which is about 40%. According to the latest data of 'Chinese cardiovascular disease report 2018', the prevalence rate of cardiovascular diseases in China is in a continuously rising stage. At present, cardiovascular disease patients are estimated to be 2.9 hundred million. The aging phenomenon of the population in China is getting more and more serious, the proportion of the aged over 60 years old in the general population is increased year by year, and the aged over 60 years old and over 60 years old in China are 24949 ten thousands of people, accounting for 17.9 percent. The elderly are often the high-incidence people with cardiovascular diseases due to their decreased physical functions.
Respiration Rate (RESP), Heart Rate (HR), and Heart Rate Variability (HRV) are important physiological parameters of the human body, and can laterally reflect the health condition of the human body. Daily monitoring helps to detect cardiovascular and other diseases, thereby preventing early and more dangerous situations. The current method for measuring beat-to-beat heart rate is mainly based on Electrocardiogram (ECG) signals and Photoplethysmogram (PPG) signals. On one hand, however, a single-channel signal can only provide limited information related to human physiological parameter indexes, and once the quality of an original signal is poor, the information can be completely lost, so that a high error rate is caused for calculating the beat-to-beat heart rate; on the other hand, in a human body daily monitoring scene, large-amplitude fluctuation is inevitable, artifacts and noise are introduced into an original signal acquired by the wearable monitoring equipment, and signal quality is poor or even serious distortion is caused. The frequency spectrums of the artifacts and the noises are distributed in a wide frequency range, and the Finite Impulse Response (FIR) filter and the Infinite Impulse Response (IIR) filter which are designed in a fixed frequency range cannot effectively filter the artifacts and the noises. The above problems increase the risk of missed diagnosis, and may cause unnecessary false alarms, so that the use scene of the wearable device is limited, and the wearable device is not beneficial to large-scale popularization and popularization.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a human body beat-by-beat heart rate measuring device based on particle filtering, which can enhance the accuracy of human body beat-by-beat heart rate monitoring in motion and noise environments and can further accurately extract derived parameters related to beat-by-beat heart rate.
In order to achieve the purpose, the human body beat-by-beat heart rate measuring device based on particle filtering comprises a patch module and an electronic module, wherein the patch module is in close contact with the surface of the skin of a human body and collects signals, the electronic module is located on one side, away from the surface of the skin of the human body, of the patch module, and the electronic module is used for processing the signals collected by the patch module.
Furthermore, four electrocardio-electrode patches are arranged on one surface of the patch module, which is attached to the skin of a human body, and are positioned at four corners of the patch module, an optical probe is arranged at the center of the patch module and comprises an LED light source emitter and a photoelectric receiving sensor, and the optical probe is in data communication with the electronic module through a serial port.
Furthermore, the electronic module comprises a power supply module for supplying power, a storage module for storing signals and calculating parameters, a sensor module group, a microprocessor carrying a beat-by-beat heart rate calculation program based on particle filtering, and a wireless communication module.
Furthermore, the sensor module group comprises an electrocardio sensor, a three-axis acceleration sensor, a three-axis angular velocity sensor and a photoplethysmography signal acquisition sensor, an acquisition interface of the electrocardio sensor is exposed and attached to the surface of the skin of a human body through an electrocardio electrode patch to acquire electrocardio signals, the three-axis acceleration sensor and the three-axis angular velocity sensor are arranged inside the sensor module and record the electrocardiography signals on the surface of the thoracic cavity of the human body in real time, and the photoplethysmography signal acquisition sensor acquires the photoplethysmography signals of the human body through an optical probe.
Further, the X-axis, Y-axis and Z-axis of the three-axis acceleration sensor and the three-axis angular velocity sensor correspond to the left-right direction, the up-down direction and the front-back direction of the surface of the thoracic cavity, respectively.
The invention provides a human body beat-by-beat heart rate measuring method based on particle filtering, which comprises the following steps of:
s1: acquiring sensor data in real time, wherein the sensor data comprises electrocardiosignals, photoplethysmography signals and seismography signals;
s2: preprocessing a signal, processing the signal by utilizing wavelet decomposition, and then eliminating an error and improving the signal quality by using a morphological filtering method;
s3: extracting signal feature points based on a local optimal principle, and constructing an observation vector;
s4: modeling the beat-to-beat heart rate as a state estimation problem, establishing an equation for data analysis, performing probability fusion by using a particle filtering algorithm to obtain a stable beat-to-beat heart rate estimation value, and obtaining derived parameters including a respiration rate, a heart rate variability related frequency domain and a heart rate variability related time domain index.
Further, in step S2, the morphological filtering method includes erosion and dilation operations for one dimensionSignal f (n), the calculation formula of the corrosion operation is:
the calculation formula of the dilation operation is:
wherein B (m) is a structural element designed according to the morphological characteristics of the waveform.
Further, in step S3, the extracted signal feature points include an R point of the electrocardiographic signal, an AO point of the electrocardiographic signal, and a FOOT point of the photoplethysmographic signal.
Further, step S4 further includes:
s401: modeling the beat-to-beat heart rate as a state estimation problem, and establishing an observation equation and a transfer equation:
x k ＝f k (x k-1 ，v k-1 )
z k ＝h k (x k ，u k )
wherein x is k Is the state value at time k, z k Is the observation vector at time k, v k-1 And u k The state transition noise and the observation noise are respectively corresponding and independent to each other;
s402: the transfer equation for beat-to-beat heart rate estimation is: x is the number of k ＝f k (x k-1 ，v k-1 )＝x k-1 +v k-1 Definition of STD HR For heart rate standard deviation, instead of the mean of the multi-channel observed heart rates, v is k-1 Defined as white Gaussian noise v k-1 ～N(0，STD 2 HR )；
S403: from which a number N of particles are sampled,
if the sampling is the first sampling, the heart rate mean value HR is adopted mean The determined uniform distribution is taken as the prior distribution of the particles in the first step:
s404: modeling the mth observation as obeying a gaussian distribution:
wherein σ k To observe the noise standard deviation, heart rate observations from all M sensors
Its joint distribution is defined as:
s405: updating the weight of the ith particle and normalizing the weight value according to the joint distribution obtained in step S404:
s406: calculating the beat-to-beat heart rate of the k step:
s407: resampling the current N particles according to importance weight, namely increasing the particles with larger weight and decreasing the particles with smaller weight to solve the problem of particle degradation, obtaining N new particles, and resetting their weights to
S408: the above steps are performed recursively according to the defined observation equation and transfer equation until all data is completely processed.
The portable wearable beat-to-beat heart rate measuring device has the following advantages:
signals such as ECG, PPG, SCG and the like can be synchronously acquired, multi-parameter integration is realized, multi-dimensional information is provided for beat-to-beat heart rate estimation, and the problem of poor robustness of a single measurement means is avoided;
the monitoring system has small volume, light weight and low power consumption, does not influence the daily activities of users, is suitable for long-term monitoring, and can be used as a feasible means for home or community health monitoring;
on the basis of the acquired original signals, beat-to-beat heart rate estimation is modeled into a state estimation problem, probability fusion is carried out on the observed value and the state value by utilizing a particle filter algorithm, the influence of artifacts and noise caused by body motion on beat-to-beat heart rate measurement results is effectively inhibited, and a reliable and stable human beat-to-beat heart rate measurement mode is provided for users.
Drawings
The present invention will be further described and illustrated with reference to the following drawings.
FIG. 1 is a side view of a portable wearable beat-to-beat heart rate measurement device in accordance with a preferred embodiment of the present invention;
FIG. 2 is a top view of a portable wearable beat-to-beat heart rate measurement device in accordance with a preferred embodiment of the present invention;
FIG. 3 is a bottom view of a portable wearable beat-to-beat heart rate measurement device in accordance with a preferred embodiment of the present invention;
FIG. 4 is a schematic diagram of a portable wearable beat-to-beat heart rate measurement device according to a preferred embodiment of the present invention attached to the surface of the chest of a human body;
FIG. 5 is a flow chart of a portable wearable beat-to-beat heart rate measurement method in accordance with a preferred embodiment of the present invention;
fig. 6 is a schematic diagram of extracting point locations of signal feature points in a portable wearable beat-to-beat heart rate measurement method according to a preferred embodiment of the present invention.
Reference numerals: 101. an electronic module; 102. a patch module; 103. a power supply module; 104. a storage module; 105. a group of sensor modules; 106. a microprocessor; 107. a wireless communication module; 108. an LED light source emitter; 109. a photoelectric receiving sensor; 110. an electrocardio-electrode paster.
Detailed Description
The technical solution of the present invention will be more clearly and completely explained by the description of the preferred embodiments of the present invention with reference to the accompanying drawings.
As shown in fig. 1 and 4, a preferred embodiment of the present invention relates to a human body beat-to-beat heart rate measuring device based on particle filtering, which includes a patch module 102 and an electronic module 101, wherein one side of the patch module 102 can be in close contact with a skin surface of a human body, and the other side is connected to the electronic module 101. The whole chest surface position department of attaching to above the human heart of this device of accessible magic area.
As shown in fig. 3, four electrocardiographic electrode patches 110 are disposed on one surface of the patch module 102 attached to the skin of the human body, the four electrocardiographic electrode patches 110 are located at four corners of the patch module 102, an optical probe is disposed at the center of the patch module 102, the optical probe includes an LED light source emitter 108 and a photoelectric receiving sensor 109, and the optical probe performs data communication with the electronic module 101 through a serial port.
As shown in fig. 2, the electronic module 101 includes a power module 103 for supplying power, a storage module 104 for storing signals and calculation parameters, a sensor module group 105, a microprocessor 106 carrying a beat-to-beat heart rate calculation program based on particle filtering, and a wireless communication module 107.
The power module 103 adopts a rechargeable lithium battery with the capacity of 300mAh, and provides reliable power for other modules of the system through a 3V/2.1V linear voltage stabilizing circuit.
The storage module 104 may store the acquired raw signals and various calculated parameters, such as respiration rate, heart rate variability index, and the like, in real time.
The sensor module group 105 comprises an electrocardio sensor, a three-axis acceleration sensor, a three-axis angular velocity sensor and a photoelectric volume pulse wave signal acquisition sensor,
the acquisition interface of the electrocardio sensor is exposed and attached to the surface of the skin of a human body through an electrocardio electrode patch 110 to acquire electrocardio signals, in the embodiment, an ultra-low power consumption integrated type analog front end AFE4900 of TI company is adopted, and an AFE4900 device supports synchronous acquisition of 1 path of ECG signals and 3 paths of PPG signals.
As shown in fig. 4, the three-axis acceleration sensor and the three-axis angular velocity sensor are disposed inside the sensor module and record the seismograph Signal (SCG) of the surface of the thoracic cavity of the human body in real time, and the X-axis, the Y-axis, and the Z-axis of the three-axis acceleration sensor and the three-axis angular velocity sensor correspond to the left-right direction, the up-down direction, and the front-back direction of the surface of the thoracic cavity, respectively. In the embodiment, an MPU9250 device of Invensense company is adopted to acquire three-axis acceleration and three-axis angular velocity motion signals, the MPU9250 has three axial 16-bits acceleration AD outputs and three axial 16-bits angular velocity AD outputs, and the realization of precise slow and fast motion tracking functions is guaranteed. The three sets of acceleration and angular velocity values obtained by the acquisition record the weak acceleration and angular velocity changes of the chest wall surface caused by ejection of blood and myocardial contraction, and reflect the relaxation and contraction of the left ventricle, the opening and closing of the aortic valve and other heart-related mechanical activities. Actual test results show that X, Y and Z axes can provide useful information for calculating beat-to-beat heart rate, and the signal amplitude in the Z axis direction is the strongest and the signal-to-noise ratio is the highest.
The photoelectric volume pulse wave signal acquisition sensor acquires a human body photoelectric volume pulse wave signal through an optical probe. In this embodiment, an AFE4900 analog front end is used to collect photoplethysmography signals, an optical probe of the sensor is exposed and includes an LED light source emitter 108 and a photoelectric receiving sensor 109, when light emitted by a light source irradiates human tissues, the absorption of muscles, skin and the like to light is a relatively stable value, and due to the relaxation and contraction of the heart, the filling degree of subcutaneous capillaries of a human body changes, the change of blood flow causes the absorption of light in blood to also show periodic changes, the photoelectric sensor can convert the light intensity into an electrical signal for output, and a PPG signal can be obtained after further processing by a program.
In this embodiment, the microprocessor 106 employs a CC2640R2F chip of TI corporation, and is loaded with a beat-to-beat heart rate calculation program based on a particle filter, which is a bayesian filter that can approximate arbitrarily distributed noise by generating a plurality of particles as compared with a kalman filter, and thus is applied to a nonlinear model having non-gaussian noise.
The wireless communication module 107 sends the measurement result to a client with calculation and storage functions, such as a smart phone, a tablet, a computer, and the like, by using wireless communication technologies such as bluetooth low energy, WIFI, ZigBee, and the like. The 2.4GHz radio frequency portion of this embodiment has been integrated into the microprocessor 106CC2640R2F device. The radio frequency part conforms to a Bluetooth low energy consumption (BLE) protocol, and can be further developed based on a low energy consumption Bluetooth software protocol stack of a company TI, so that data transmission is realized.
As shown in fig. 5, a method for measuring a beat-to-beat heart rate of a human body based on particle filtering according to a preferred embodiment of the present invention includes the following steps:
s1: the method comprises the steps of collecting sensor data in real time, wherein the sensor data comprise 1-channel electrocardiosignals, 1-channel photoplethysmography signals, 3-channel acceleration signals and 3-channel angular velocity signals, all the signals are collected synchronously, and the sampling frequency is 250 Hz.
S2: the signal is pre-processed and the sym6 wavelet basis is chosen to decompose the original signal into signal components with different resolutions at different scales, obtaining a wavelet decomposition tree consisting of approximation and detail coefficients at each level, the number of decomposition layers chosen in this example being 6. To suppress the noise component, a "Soft threshold" (Soft threshold) function is selected to determine and discard detail coefficients with small amplitudes for the purpose of removing baseline offsets and high frequency noise components.
And then, performing Erosion (Erosis) and expansion (decomposition) operations on the signal by using a morphological filtering method, thereby further improving the signal quality. Morphological filtering is a nonlinear filter which can effectively avoid the nonlinear phase problem, i.e. it can deal with the "convex" and "concave" structure of the signal in terms of morphology on the basis of maintaining the original signal phase. The morphological filtering algorithm has the advantages of stability, simplicity and low calculation complexity, and for a one-dimensional signal f (n), the calculation formula of the corrosion operation is as follows:
expansion ofThe calculation formula of the operation is as follows:
wherein, b (m) is a Structural element (Structural element) designed according to waveform morphological characteristics, which is beneficial to obtaining an optimal filtering effect.
S3: signal feature points are extracted based on the local optimum principle, as shown in fig. 6, the extracted signal feature points include R points of the electrocardiographic signal, AO points of the electrocardiographic signal, and FOOT points of the photoplethysmographic signal, and corresponding heart rate observation values can be calculated by using the feature points, and an observation vector is formed.
S4: modeling the beat-to-beat heart rate as a state estimation problem, establishing an equation for data analysis, performing probability fusion by using a particle filtering algorithm to obtain a stable beat-to-beat heart rate estimation value, and obtaining derived parameters including a respiration rate, a heart rate variability related frequency domain and a heart rate variability related time domain index.
The method specifically comprises the following steps:
s401: modeling the beat-to-beat heart rate as a state estimation problem, and establishing an observation equation and a transfer equation:
x k ＝f k (x k-1 ，v k-1 )
z k ＝h k (x k ，u k )
wherein x is k Is the state value at time k, z k Is the observation vector at time k, v k-1 And u k The state transition noise and the observation noise are respectively corresponding and independent to each other;
s402: the transfer equation for beat-to-beat heart rate estimation is: x is the number of k ＝f k (x k-1 ，v k-1 )＝x k-1 +v k-1 Definition of STD HR As heart rate standard deviation, instead of the mean of the multi-channel observed heart rates, v is k-1 Defined as white Gaussian noise v k-1 ～N(0，STD 2 HR )；
S403: sampling to obtain N particles, taking N as 500 in the embodiment,
since the first step (k is 0) of the process cannot obtain the prior distribution of the particle states, in order to make the particle filtering algorithm converge faster, if the first sampling is performed, the heart rate mean HR is used mean The determined uniform distribution is taken as the prior distribution of the particles in the first step:
s404: modeling the mth observation as obeying a gaussian distribution:
wherein σ k To observe the noise standard deviation, heart rate observations from all M sensors
Its joint distribution is defined as:
s405: updating the weight of the ith particle and normalizing the weight value according to the joint distribution obtained in step S404:
s406: calculating the beat-to-beat heart rate of the kth step:
s407: resampling the current N particles according to importance weight, namely increasing the particles with larger weight and decreasing the particles with smaller weight to solve the problem of particle degradation and obtain N new particlesThereafter, their weights are reset to
S408: the above steps are performed recursively according to the defined observation equation and transfer equation until all data is completely processed.
The human body beat-by-beat heart rate measuring device based on the particle filtering has the advantages of multi-parameter integration, small volume, light weight, low power consumption, suitability for long-term monitoring and the like, probability fusion is carried out on observed values and state values based on the particle filtering algorithm, a reliable and stable human body beat-by-beat heart rate measuring mode is provided for users, and the device can be used as a feasible means for family or community health monitoring.
The above detailed description merely describes preferred embodiments of the present invention and does not limit the scope of the invention. Without departing from the spirit and scope of the present invention, it should be understood that various changes, substitutions and alterations can be made herein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents. The scope of the invention is defined by the claims.
Claims (2)
1. A human body beat-by-beat heart rate measuring method based on particle filtering is characterized by comprising the following steps:
s1: acquiring sensor data in real time, wherein the sensor data comprises electrocardiosignals, photoplethysmography signals and seismography signals;
s2: preprocessing a signal, processing the signal by utilizing wavelet decomposition, and then eliminating errors and improving the signal quality by using a morphological filtering method, wherein the morphological filtering method comprises corrosion and expansion operations, and for a one-dimensional signal f (n), the calculation formula of the corrosion operation is as follows:
the calculation formula of the dilation operation is:
wherein, B (m) is a structural element designed according to the morphological characteristics of the waveform;
s3: extracting signal characteristic points based on a local optimal principle, and constructing an observation vector, wherein the extracted signal characteristic points comprise an R point of an electrocardiosignal, an AO point of a seismograph signal and an FOOT point of a photoplethysmography signal;
s4: modeling beat-to-beat heart rate as a state estimation problem, establishing an equation for data analysis, performing probability fusion by using a particle filtering algorithm to obtain a stable beat-to-beat heart rate estimation value and obtain derived parameters including respiration rate, a heart rate variability related frequency domain and a heart rate variability related time domain index, and specifically comprising the following steps of:
s401: modeling the beat-to-beat heart rate as a state estimation problem, and establishing an observation equation and a transfer equation:
x k ＝f k (x k-1 ，v k-1 )
z k ＝h k (x k ，u k )
wherein x is k Is the state value at time k, z k Is the observation vector at time k, v k-1 And u k The state transition noise and the observation noise are respectively corresponding and independent to each other;
s402: the transfer equation for beat-to-beat heart rate estimation is: x is the number of k ＝f k (x k-1 ，v k-1 )＝x k-1 +v k-1 Definition of STD HR As heart rate standard deviation, instead of the mean of the multi-channel observed heart rates, v is k-1 Defined as white Gaussian noise v k-1 ～N(0，STD 2 HR )；
S403: from which a number N of particles are sampled,
if the sampling is performed for the first time, the heart rate mean value HR is adopted mean The determined uniform distribution is taken as the prior distribution of the particles in the first step:
s404: modeling the mth observation as obeying a gaussian distribution:
wherein σ k To observe the noise standard deviation, heart rate observations from all M sensors
Its joint distribution is defined as:
s405: updating the weight of the ith particle and normalizing the weight value according to the joint distribution obtained in step S404:
s406: calculating the beat-to-beat heart rate of the k step:
s407: resampling the current N particles according to importance weight, namely increasing the particles with larger weight and decreasing the particles with smaller weight to solve the problem of particle degradation, obtaining N new particles, and resetting their weights to
S408: the above steps are performed recursively according to the defined observation equation and transfer equation until all data is completely processed.
2. The device for measuring the human body beat-to-beat heart rate based on the particle filtering as claimed in claim 1, which comprises a patch module and an electronic module, wherein the patch module is in close contact with the skin surface of the human body and collects signals, the electronic module is arranged at the side of the patch module, which is far away from the skin surface of the human body, and the electronic module is used for processing the signals collected by the patch module;
the surface of the patch module, which is attached to the skin of a human body, is provided with four electrocardio-electrode patches, the four electrocardio-electrode patches are positioned at four corners of the patch module, the center of the patch module is provided with an optical probe, the optical probe comprises an LED light source transmitter and a photoelectric receiving sensor, and the optical probe is in data communication with the electronic module through a serial port;
the electronic module comprises a power supply module for supplying power, a storage module for storing signals and calculating parameters, a sensor module group, a microprocessor carrying a beat-by-beat heart rate calculation program based on particle filtering and a wireless communication module;
sensor module group is including electrocardio sensor, triaxial acceleration sensor, triaxial angular velocity sensor and photoplethysmography pulse wave signal acquisition sensor, electrocardio sensor acquisition interface exposes and carries out electrocardio signal collection through electrocardio electrode paster attached on human skin surface, triaxial acceleration sensor and triaxial angular velocity sensor set up inside the sensor module and record the human thorax surperficial seismograph signal in real time, photoplethysmography pulse wave signal acquisition sensor passes through optical probe and gathers human photoplethysmography pulse wave signal, triaxial acceleration sensor and triaxial angular velocity sensor's X axle, Y axle and Z axle correspond left right direction, upper and lower direction and fore-and-aft direction on thorax surface respectively.
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