CN105816165B - real-time dynamic heart rate monitoring device and monitoring method - Google Patents
real-time dynamic heart rate monitoring device and monitoring method Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
Abstract
The invention discloses a real-time dynamic heart rate monitoring device and a monitoring method, wherein the device comprises a baseline drift elimination module, a band-pass filtering module, a frequency domain analysis module and a heart rate frequency point selection module; the baseline wander elimination module is used for eliminating interference signals causing wander of a baseline line of the pulse wave signals; the band-pass filtering module is used for acquiring frequency signal components belonging to a heart rate frequency band and eliminating noise signals outside the heart rate frequency band; the frequency domain analysis module is used for acquiring a signal frequency spectrum of the pulse wave signal in a preset specific frequency band interval; the heart rate frequency point selection module is used for acquiring a frequency point corresponding to the heart rate and outputting the frequency point. The invention eliminates the influence of human body movement and muscle activity on heart rate analysis, reduces hardware cost, can flexibly select the concerned frequency band interval, improves the frequency domain precision of signals only in the concerned frequency band interval, and avoids causing excessive calculation and storage expenses.
Description
Technical Field
the invention relates to the field of electronics, in particular to a real-time dynamic heart rate monitoring device and a monitoring method.
background
Along with the application and the development of mobile internet technology in the medical health field, a large amount of forms such as intelligent wrist-watch, intelligent bracelet, intelligent wrist strap have appeared in the market and have been varied, possess the wearable removal healthy product of medical physiological parameters such as measurement rhythm of the heart, blood pressure, blood oxygen concentration, respiratory frequency. The heart rate is defined as the number of heart beats per minute of a human body, and is an important medical routine physiological parameter for evaluating the health state of the human body. The heart rate monitoring has very important significance for disease risk early warning, disease diagnosis and annual routine physical examination. In particular, exercise modes such as fitness activities and outdoor running have wide application requirements on real-time dynamic heart rate monitoring.
At present, the heart rate monitoring technology adopted by most mobile health products is based on a photoelectric transmission measurement method. In the hardware design of the product, a sensor in contact with the skin of a person emits a beam of light that impinges on the skin while measuring the light reflected or transmitted through the skin. Because blood has absorption effect on light with specific wavelength, the heart blood pumping process directly influences the change of the light signal intensity measured by the sensor, and the hardware records the signal intensity change according to the set sampling rate to acquire the original data, namely the pulse wave signal. And the data analysis software unit runs a heart rate monitoring algorithm to process the pulse wave signals and output a heart rate value. The heart rate monitoring algorithm is a key core technology of heart rate measurement products, and determines the accuracy and reliability of heart rate measurement values. In practical applications, heart rate monitoring includes static heart rate monitoring and real-time dynamic heart rate monitoring, and the latter has a wider application space and also provides a great challenge to the prior art.
Actual tests show that the current technical situation of the dynamic heart rate monitoring algorithm of most of the current mobile health products is that the following defects are exposed in the application scene of real-time monitoring of the heart rate of a human body in a motion state by combining traditional signal time domain waveform analysis or signal frequency domain analysis with accelerometer reading for auxiliary judgment. Firstly, due to noise interference, characteristic points on a signal time domain waveform are not obvious, so that an algorithm cannot acquire complete input information; secondly, the rule of signal time domain waveform matching is set too much, and the specific numerical value setting of the algorithm parameter is difficult; thirdly, for the embedded module, the calculation complexity of the waveform matching algorithm is large; fourthly, the traditional signal frequency domain analysis method can increase calculation and data storage expenses when improving the frequency spectrum precision; fifth, accelerometers increase hardware costs, while also increasing resource overhead in terms of computation, storage, and energy consumption.
accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
in view of the defects of the prior art, the invention aims to provide a real-time dynamic heart rate monitoring device and a monitoring method, and aims to overcome the defects that when the dynamic heart rate monitoring device monitors the heart rate, the algorithm is high in calculation complexity, the hardware cost is high, and the resource overhead in the aspects of calculation, storage and energy is large.
the technical scheme of the invention is as follows:
a real-time dynamic heart rate monitoring device comprises a baseline drift elimination module, a band-pass filtering module, a frequency domain analysis module and a heart rate frequency point selection module;
The baseline wander elimination module is used for eliminating interference signals causing wander of a baseline line of the pulse wave signals;
the band-pass filtering module is used for acquiring frequency signal components belonging to a heart rate frequency band and eliminating noise signals outside the heart rate frequency band;
the frequency domain analysis module is used for acquiring a signal frequency spectrum of the pulse wave signal in a preset specific frequency band interval;
the heart rate frequency point selection module is used for acquiring a frequency point corresponding to a heart rate and outputting the frequency point;
the baseline drift elimination module is connected with the band-pass filtering module, the band-pass filtering module is connected with the frequency domain analysis module, and the frequency domain analysis module is further connected with the heart rate frequency point selection module.
The real-time dynamic heart rate monitoring device comprises a baseline drift elimination module, a baseline drift trend term signal extraction unit and a signal linear superposition unit, wherein the baseline drift elimination module comprises a baseline drift trend term signal extraction unit and a signal linear superposition unit;
the baseline drift trend item signal extraction unit is used for acquiring a baseline drift trend item signal which is as long as the original pulse wave signal;
the signal linear superposition unit is used for subtracting the baseline drift trend term signal from the original pulse wave signal to obtain a pulse wave signal without the baseline drift;
The baseline drift trend term signal extraction unit is connected with the signal linear superposition unit.
the real-time dynamic heart rate monitoring device is characterized in that the band-pass filtering module specifically comprises a filtering parameter setting unit and a filtering unit,
The filter parameter setting unit is used for setting the lower limit and the upper limit frequency of a pass band, the lower limit and the upper limit frequency of a stop band, the attenuation coefficient in the pass band and the attenuation coefficient in the stop band;
The filtering unit is used for filtering the pulse wave signals of which the base lines are eliminated by the base line drift elimination module after acquiring the order and the coefficient of the filtering module, and acquiring the pulse wave signals of which the noise is eliminated;
And the filtering parameter setting unit is connected with the filtering unit.
the real-time dynamic heart rate monitoring device comprises a frequency domain analysis module, a frequency point power calculation module and a signal spectrum acquisition module, wherein the frequency domain analysis module specifically comprises a specific frequency band interval setting unit, a frequency point power calculation unit and a signal spectrum acquisition unit;
the specific frequency band interval setting unit is used for setting a starting frequency point, an ending frequency point and a frequency point subdivision number of a specific frequency band interval;
the frequency point power calculation unit is used for calculating the real part of the frequency domain signal by using a first transformation polynomial, calculating the imaginary part of the frequency domain signal by using a second transformation polynomial and calculating the power of the pulse wave signal at the frequency point according to the real part and the imaginary part of the frequency domain signal;
The signal spectrum acquisition unit is used for acquiring the power of the pulse wave signals in the specific frequency band interval at each frequency point, and generating the signal spectrum of the pulse wave signals in the whole concerned frequency band interval after superposition;
The frequency point power calculation unit is respectively connected with the specific frequency band interval setting unit and the signal spectrum acquisition unit.
The real-time dynamic heart rate monitoring device is characterized in that the heart rate frequency point selection module specifically comprises a heart rate frequency point selection unit and a heart rate frequency point dynamic tracking unit,
The heart rate frequency point selection unit is used for acquiring the frequency point position where a spectrum peak in a pulse wave signal spectrum is located as a heart rate frequency point in an initial state;
The heart rate frequency point dynamic tracking unit is used for carrying out high-precision frequency domain analysis in a frequency spectrum range with a specific width at the left and right of the center by taking the heart rate frequency point output in the previous tracking period as the center in a preset fixed tracking period, and selecting the frequency point position where a spectrum peak is positioned as the heart rate frequency point to output;
the heart rate frequency point selection unit is connected with the heart rate frequency point dynamic tracking unit.
The real-time dynamic heart rate monitoring device, wherein the first and second transform polynomials are orthogonal polynomials.
the real-time dynamic heart rate monitoring device, wherein the first transformation polynomial is one of legendre polynomial, jacobian polynomial, laguerre polynomial, chebyshev polynomial and hermitian polynomial;
the second transformation polynomial is one of Legendre polynomial, Jacobian polynomial, Laguerre polynomial, Chebyshev polynomial and Hermite polynomial.
A monitoring method based on the real-time dynamic heart rate monitoring device comprises the following steps:
A. Acquiring a pulse wave signal, and eliminating a baseline drift interference signal through a baseline drift elimination module;
B. the band-pass filtering module filters the signal output by the baseline drift elimination module and reserves the signal component belonging to the heart rate frequency band;
C. the frequency domain analysis module acquires a signal frequency spectrum of the pulse wave signal in a preset specific frequency band interval;
D. And the heart rate frequency point selection module acquires and outputs a frequency point corresponding to the heart rate according to the signal frequency.
the real-time dynamic heart rate monitoring method comprises the following specific steps:
A1, obtaining a baseline drift trend term signal with the same length as the original pulse wave signal by a signal filtering method or a curve fitting method;
a2, storing the original pulse wave signals and the baseline wandering trend item signals by using row vectors or column vectors, and then subtracting the baseline wandering trend item signals from the original pulse wave signals according to a matrix addition rule to obtain the pulse wave signals with the baseline wandering removed.
The real-time dynamic heart rate monitoring method comprises the following specific steps:
B1, acquiring preset pass band lower limit and upper limit frequencies, preset stop band lower limit and upper limit frequencies, preset pass band internal attenuation coefficients and preset stop band internal attenuation coefficients;
and B2, after the order and the coefficient of the filtering module are obtained, filtering the pulse wave signals of which the baseline drift is eliminated by the baseline drift elimination module to obtain the pulse wave signals of which the noise is eliminated.
the invention provides a real-time dynamic heart rate monitoring device and a monitoring method, which can eliminate the influence of human body movement and muscle activity on heart rate analysis, reduce hardware cost, flexibly select an attention frequency band interval, improve the frequency domain precision of signals only in the attention frequency band interval, avoid bringing excessive calculation and storage expenses, select a technical route of signal spectrum analysis, and avoid the problems that time domain signal waveform characteristic points are difficult to find and specific values of algorithm parameters are difficult to determine.
drawings
fig. 1 is a functional block diagram of a preferred embodiment of a real-time dynamic heart rate monitoring apparatus according to the present invention.
fig. 2 is a flowchart of a monitoring method of a real-time dynamic heart rate monitoring apparatus according to a preferred embodiment of the present invention.
Detailed Description
in order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is described in further detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
the invention also provides a functional schematic diagram of a preferred embodiment of a real-time dynamic heart rate monitoring device, as shown in fig. 1, wherein the device comprises a baseline drift elimination module 100, a band-pass filtering module 200, a frequency domain analysis module 300, and a heart rate frequency point selection module 400;
the baseline wander elimination module 100 is configured to eliminate an interference signal causing a wander of a baseline of the pulse wave signal; the band-pass filtering module 200 is configured to obtain a frequency signal component belonging to a heart rate frequency band and eliminate a noise signal outside the heart rate frequency band; the frequency domain analysis module 300 is configured to obtain a signal spectrum of the pulse wave signal in a preset specific frequency band interval; the heart rate frequency point selection module 400 is configured to obtain a frequency point corresponding to a heart rate and output the frequency point; the baseline drift elimination module 100 is connected with the band-pass filter module 200, the band-pass filter module 200 is connected with the frequency domain analysis module 300, and the frequency domain analysis module 300 is further connected with the heart rate frequency point selection module 400.
In specific implementation, the baseline wander elimination module specifically comprises a baseline wander trend term signal extraction unit and a signal linear superposition unit;
The baseline drift trend item signal extraction unit is used for acquiring a baseline drift trend item signal which is as long as the original pulse wave signal;
the signal linear superposition unit is used for subtracting the baseline drift trend term signal from the original pulse wave signal to obtain a pulse wave signal without the baseline drift;
The baseline drift trend term signal extraction unit is connected with the signal linear superposition unit.
in specific implementation, the baseline of the pulse wave signal can drift up and down due to respiration, limb movement or movement, and the baseline drift is a low-frequency signal essentially and can interfere with judgment of key information. The main function of the baseline wander elimination module is to reduce baseline wander interference caused by respiration and movement on pulse wave signals and avoid dependence on accelerometer auxiliary information.
the baseline wander elimination module consists of two functional units: the device comprises a baseline drift trend term signal extraction unit and a signal linear superposition unit.
the specific implementation modes of the baseline wandering trend term signal extraction unit are two types, wherein the first type is a signal filtering method, and the second type is a curve fitting method. The specific implementation mode of the signal filtering method is median filtering or mean filtering, a sliding window is used for traversing the pulse wave signals, the median or mean of all signal values in the window is calculated, and finally a baseline drift trend item signal which is as long as the original pulse wave signals is obtained. According to the specific implementation mode of the curve fitting method, the baseline drift signal is regarded as a time function which can be expressed as a high-order polynomial, the pulse wave signal is used as an input data sample, various coefficients of the high-order polynomial are obtained by adopting a nonlinear fitting method, and finally a baseline drift trend term signal which is as long as the original pulse wave signal is obtained through calculation of the polynomial function.
the specific implementation mode of the signal linear superposition unit is that the original pulse wave signals and the baseline wandering trend item signals are stored by using row vectors or column vectors, then the baseline wandering trend item signals are subtracted from the original pulse wave signals according to a matrix addition rule, and finally the pulse wave signals with the baseline wandering removed are obtained.
further, the band-pass filtering module specifically comprises a filtering parameter setting unit and a filtering unit,
the filter parameter setting unit is used for setting the lower limit and the upper limit frequency of a pass band, the lower limit and the upper limit frequency of a stop band, the attenuation coefficient in the pass band and the attenuation coefficient in the stop band;
the filtering unit is used for filtering the pulse wave signals of which the base lines are eliminated by the base line drift elimination module after acquiring the order and the coefficient of the filtering module, and acquiring the pulse wave signals of which the noise is eliminated;
And the filtering parameter setting unit is connected with the filtering unit.
in particular, a number of medical practices have shown that human heart rates generally range from 40 to 220 beats/minute, which indicates that the frequency of the heart rate signal ranges from 0.6 to 3.7 Hz. Therefore, signal frequency bands of 0 Hz to 0.6 Hz, and 3.7 Hz or more can be regarded as noise frequency bands, which include glitch noise caused by a sensor circuit, noise introduced by human body activity, and the like. The main function of the band-pass filter module is to only retain signal components belonging to the heart rate frequency band, and simultaneously eliminate the interference of out-of-band noise to the heart rate signal discovery.
The band-pass filter module, a specific implementation mode, may adopt a butterworth filter, and the filter order and the coefficient are calculated by setting a passband lower limit and upper limit frequency, a stopband lower limit and upper limit frequency, a passband internal attenuation coefficient, and a stopband internal attenuation coefficient, referring to a butterworth filter design flow. And inputting the pulse wave signal without the baseline drift into a band-pass filter module to finally obtain the pulse wave signal without the noise.
in specific implementation, heart rate signals under extreme conditions (diseases and violent exercises) are not excluded, and in order to ensure that the heart rate change is comprehensively monitored, the dynamic range of a heart rate frequency band can be properly enlarged, for example, the dynamic range is set to be 0.5 Hz to 4 Hz; the bandpass filter may be implemented by selecting a chebyshev filter or the like in addition to the butterworth filter.
in a further embodiment, the frequency domain analysis module specifically includes a specific frequency band interval setting unit, a frequency point power calculation unit and a signal spectrum acquisition unit;
The specific frequency band interval setting unit is used for setting a starting frequency point, an ending frequency point and a frequency point subdivision number of a specific frequency band interval;
The frequency point power calculation unit is used for calculating the real part of the frequency domain signal by using a first transformation polynomial, calculating the imaginary part of the frequency domain signal by using a second transformation polynomial and calculating the power of the pulse wave signal at the frequency point according to the real part and the imaginary part of the frequency domain signal;
the signal spectrum acquisition unit is used for acquiring the power of the pulse wave signals in the specific frequency band interval at each frequency point, and generating the signal spectrum of the pulse wave signals in the whole concerned frequency band interval after superposition;
the frequency point power calculation unit is respectively connected with the specific frequency band interval setting unit and the signal spectrum acquisition unit.
in specific implementation, the pulse wave signal without noise is a time domain signal, and in order to extract heart rate information more accurately, a signal transformation method needs to be adopted to obtain a frequency domain signal corresponding to the pulse wave signal, and it is ensured that a signal frequency spectrum has high enough frequency domain accuracy. The classic time-frequency signal transformation method is Fast Fourier Transformation (FFT), and the disadvantage is that calculation and storage expenses which are hard to bear by embedded equipment are needed for improving the frequency domain precision of the signal. The high-precision frequency domain analysis module has the main functions of flexibly selecting the concerned frequency band interval, improving the signal frequency domain precision only in the concerned frequency band interval and avoiding excessive calculation and storage expenses.
the specific implementation manner of the high-precision frequency domain analysis module is to set a starting frequency point f1, an ending frequency point f2 and a frequency point subdivision number Nf of the concerned frequency band interval.
A pulse wave signal sequence having a data length N is denoted by q (N), N =0, 1, …, N-1. For each frequency bin f within the frequency bin interval of interest, the real part of the frequency domain signal is calculated using the following transform polynomial,
Qr = q(0) + q(1) * cos(f) + q(2) * T(2) + … + q(N-1) * T(N-1)
and calculating a frequency domain signal imaginary part using the following transform polynomial,
Qi= -q(1) * sin(f) – q(2) * sin(f) * U(2) - … - q(N-1) * sin(f) * U(N-1)
T (n) is a first transform polynomial, U (n) is a second transform polynomial, and the first transform polynomial and the second transform polynomial are orthogonal polynomials. The first transformation polynomial is one of Legendre polynomial, Jacobian polynomial, Laguerre polynomial, Chebyshev polynomial and Hermite polynomial; the second transformation polynomial is one of Legendre polynomial, Jacobian polynomial, Laguerre polynomial, Chebyshev polynomial and Hermite polynomial.
and calculating the power of the pulse wave signal at the frequency point f according to the Qr and the Qi.
After each frequency point in the concerned frequency band interval is calculated, the signal spectrum of the pulse wave signal in the whole concerned frequency band interval can be obtained. The quantization index of the high-frequency domain precision is related to the frequency point subdivision number Nf, and the frequency domain precision is higher when the frequency point subdivision number is larger.
Further, the heart rate frequency point selection module specifically comprises a heart rate frequency point selection unit and a heart rate frequency point dynamic tracking unit,
The heart rate frequency point selection unit is used for acquiring the frequency point position where a spectrum peak in a pulse wave signal spectrum is located as a heart rate frequency point in an initial state;
The heart rate frequency point dynamic tracking unit is used for carrying out high-precision frequency domain analysis in a frequency spectrum range with a specific width at the left and right of the center by taking the heart rate frequency point output in the previous tracking period as the center in a preset fixed tracking period, and selecting the frequency point position where a spectrum peak is positioned as the heart rate frequency point to output;
the heart rate frequency point selection unit is connected with the heart rate frequency point dynamic tracking unit.
In specific implementation, after the signal frequency spectrum of the pulse wave signal in the frequency band interval is concerned, the heart rate value cannot be directly calculated, but the heart rate value can be calculated only by analyzing and judging the frequency points representing the heart rate, which is the effect of the heart rate frequency point selection module.
the heart rate frequency point selection module, a specific implementation mode, is divided into two stages. The first stage is accurate heart rate frequency point selection in an initial state; the second stage is heart rate frequency point dynamic tracking.
The specific implementation mode is that the heart rate frequency point is accurately selected in the initial state, the human body is ensured to be in a quiet state, the pulse wave signal frequency spectrum is obtained by eliminating the baseline drift module, the band-pass filter module and the high-precision frequency domain analysis module, the frequency point position of a spectrum peak is the frequency point representing the heart rate, and the heart rate value can be calculated.
The specific implementation mode is that the dynamic tracking is carried out in a fixed tracking period, in each tracking period, a pulse wave signal without noise is obtained by eliminating a baseline drift module and a band-pass filter module, the heart rate frequency point output in the previous tracking period is taken as the center, high-precision frequency domain analysis is carried out in a frequency spectrum range with a narrow width on the left and right of the center, and the frequency point position where a spectrum peak is located is selected as the heart rate frequency point to be output.
the invention also provides a preferred embodiment of a monitoring method based on the real-time dynamic heart rate monitoring device, as shown in fig. 2, wherein the method comprises the following steps:
s100, obtaining a pulse wave signal, and eliminating a baseline drift interference signal through a baseline drift elimination module;
s200, a band-pass filtering module filters the signal output by the baseline drift elimination module and reserves the signal component belonging to the heart rate frequency band;
step S300, a frequency domain analysis module acquires a signal frequency spectrum of the pulse wave signal in a preset specific frequency band interval;
and S400, acquiring and outputting a frequency point corresponding to the heart rate according to the signal frequency by the heart rate frequency point selection module.
in specific implementation, high-frequency noise mixed in the signal acquisition process is removed through various filter technologies, signal baseline drift caused by muscle jitter and respiration is removed, a high-precision signal frequency domain analysis method with complexity lower than FFT calculation is adopted, frequency points representing heart rate are screened from a signal frequency spectrum, and the heart rate is dynamically monitored in real time. Hardware costs and resource overhead caused by the accelerometer are reduced. Secondly, the problems that time domain signal waveform characteristic points are difficult to find and specific values of algorithm parameters are difficult to determine are solved. Third, the computational complexity of the algorithm is affordable by common embedded modules. The specific monitoring method is as described above for the specific embodiment of the monitoring device.
Further, the step S100 specifically includes:
s101, obtaining a baseline drift trend item signal which is as long as the original pulse wave signal through a signal filtering method or a curve fitting method;
And S102, storing the original pulse wave signals and the baseline wandering trend item signals by using row vectors or column vectors, and then subtracting the baseline wandering trend item signals from the original pulse wave signals according to a matrix addition rule to obtain the pulse wave signals with the baseline wandering removed.
In specific implementation, the specific implementation of the signal filtering method in step S101 is median filtering or mean filtering, a sliding window is used to traverse the pulse wave signal, a median or a mean of all signal values in the window is calculated, and finally a baseline drift trend term signal with the same length as the original pulse wave signal is obtained. According to the specific implementation mode of the curve fitting method, the baseline drift signal is regarded as a time function which can be expressed as a high-order polynomial, the pulse wave signal is used as an input data sample, various coefficients of the high-order polynomial are obtained by adopting a nonlinear fitting method, and finally a baseline drift trend term signal which is as long as the original pulse wave signal is obtained through calculation of the polynomial function.
In a further embodiment, the step S200 specifically includes:
Step S201, obtaining preset pass band lower limit and upper limit frequency, stop band lower limit and upper limit frequency, pass band internal attenuation coefficient and stop band internal attenuation coefficient;
Step S202, after the order and the coefficient of the filtering module are obtained, the pulse wave signals of which the base lines are eliminated by the base line drift elimination module are filtered, and the pulse wave signals of which the noise is eliminated are obtained.
in specific implementation, a Butterworth filter is adopted, and the order and the coefficient of the filter are calculated according to the design process of the Butterworth filter by setting the lower limit and the upper limit frequency of a pass band, the lower limit and the upper limit frequency of a stop band, the attenuation coefficient in the pass band and the attenuation coefficient in the stop band. And inputting the pulse wave signal without the baseline drift into a band-pass filter module to finally obtain the pulse wave signal without the noise.
In specific implementation, heart rate signals under extreme conditions (diseases and violent exercises) are not excluded, and in order to ensure that the heart rate change is comprehensively monitored, the dynamic range of a heart rate frequency band can be properly enlarged, for example, the dynamic range is set to be 0.5 Hz to 4 Hz; the bandpass filter may be implemented by selecting a chebyshev filter or the like in addition to the butterworth filter.
in summary, the present invention provides a real-time dynamic heart rate monitoring device and a monitoring method, the device includes a baseline drift elimination module, a band-pass filtering module, a frequency domain analysis module, and a heart rate frequency point selection module; the baseline wander elimination module is used for eliminating interference signals causing wander of a baseline line of the pulse wave signals; the band-pass filtering module is used for acquiring frequency signal components belonging to a heart rate frequency band and eliminating noise signals outside the heart rate frequency band; the frequency domain analysis module is used for acquiring a signal frequency spectrum of the pulse wave signal in a preset specific frequency band interval; the heart rate frequency point selection module is used for acquiring a frequency point corresponding to the heart rate and outputting the frequency point. The invention eliminates the influence of human body movement and muscle activity on heart rate analysis, reduces hardware cost, can flexibly select the concerned frequency band interval, improves the frequency domain precision of signals only in the concerned frequency band interval, and avoids causing excessive calculation and storage expenses.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (8)
1. A real-time dynamic heart rate monitoring device is characterized by comprising a baseline drift elimination module, a band-pass filtering module, a frequency domain analysis module and a heart rate frequency point selection module;
the baseline wander elimination module is used for eliminating interference signals causing wander of a baseline line of the pulse wave signals;
The band-pass filtering module is used for acquiring frequency signal components belonging to a heart rate frequency band and eliminating noise signals outside the heart rate frequency band;
the frequency domain analysis module is used for acquiring a signal frequency spectrum of the pulse wave signal in a preset specific frequency band interval;
the heart rate frequency point selection module is used for acquiring a frequency point corresponding to a heart rate and outputting the frequency point;
the baseline drift elimination module is connected with the band-pass filtering module, the band-pass filtering module is connected with the frequency domain analysis module, and the frequency domain analysis module is also connected with the heart rate frequency point selection module;
The baseline drift elimination module specifically comprises a baseline drift trend term signal extraction unit and a signal linear superposition unit;
The baseline drift trend item signal extraction unit is used for acquiring a baseline drift trend item signal which is as long as the original pulse wave signal;
the signal linear superposition unit is used for subtracting the baseline drift trend term signal from the original pulse wave signal to obtain a pulse wave signal without the baseline drift;
the baseline drift trend item signal extraction unit is connected with the signal linear superposition unit;
The signal linear superposition unit is used for storing the original pulse wave signals and the baseline wandering trend item signals by using row vectors or column vectors, and then subtracting the baseline wandering trend item signals from the original pulse wave signals according to a matrix addition rule to obtain the pulse wave signals with baseline wandering removed;
the frequency domain analysis module specifically comprises a specific frequency band interval setting unit, a frequency point power calculation unit and a signal spectrum acquisition unit;
the specific frequency band interval setting unit is used for setting a starting frequency point, an ending frequency point and a frequency point subdivision number of a specific frequency band interval;
the frequency point power calculation unit is used for calculating the real part of the frequency domain signal by using a first transformation polynomial, calculating the imaginary part of the frequency domain signal by using a second transformation polynomial and calculating the power of the pulse wave signal at the frequency point f according to the real part and the imaginary part of the frequency domain signal;
a pulse wave signal sequence with a data length N is represented by q (N), N is 0, 1, … …, N-1,
the calculating the real part of the frequency domain signal by using the first transform polynomial specifically comprises:
Qr＝q(0)+q(1)*cos(f)+q(2)*T(2)+…+q(N-1)*T(N-1)；
The step of calculating the imaginary part of the frequency domain signal by using the second transform polynomial specifically includes:
Qi＝-q(1)*sin(f)–q(2)*sin(f)*U(2)-…-q(N-1)*sin(f)*U(N-1)；
t (n) is a first transformation polynomial, U (n) is a second transformation polynomial, and f is a frequency point;
The signal spectrum acquisition unit is used for acquiring the power of the pulse wave signals in the specific frequency band interval at each frequency point, and generating the signal spectrum of the pulse wave signals in the whole concerned frequency band interval after superposition;
the heart rate frequency point selection module is specifically used for two stages, wherein the first stage is accurate heart rate frequency point selection in an initial state, and the second stage is dynamic heart rate frequency point tracking;
the selection of the accurate heart rate frequency points in the initial state specifically comprises the following steps: and ensuring that the human body is in a quiet state, and obtaining a pulse wave signal frequency spectrum by eliminating the baseline drift module, the band-pass filter module and the frequency domain analysis module, wherein the frequency point position of a spectrum peak is the frequency point representing the heart rate.
2. the real-time dynamic heart rate monitoring device according to claim 1, wherein the band-pass filtering module comprises a filtering parameter setting unit and a filtering unit,
the filter parameter setting unit is used for setting the lower limit and the upper limit frequency of a pass band, the lower limit and the upper limit frequency of a stop band, the attenuation coefficient in the pass band and the attenuation coefficient in the stop band;
the filtering unit is used for filtering the pulse wave signals of which the base lines are eliminated by the base line drift elimination module after acquiring the order and the coefficient of the filtering module, and acquiring the pulse wave signals of which the noise is eliminated;
and the filtering parameter setting unit is connected with the filtering unit.
3. real-time dynamic heart rate monitoring device according to claim 2,
The frequency point power calculation unit is respectively connected with the specific frequency band interval setting unit and the signal spectrum acquisition unit.
4. The real-time dynamic heart rate monitoring device according to claim 3, wherein the heart rate frequency point selection module specifically comprises a heart rate frequency point selection unit and a heart rate frequency point dynamic tracking unit,
The heart rate frequency point selection unit is used for acquiring the frequency point position where a spectrum peak in a pulse wave signal spectrum is located as a heart rate frequency point in an initial state;
The heart rate frequency point dynamic tracking unit is used for carrying out high-precision frequency domain analysis in a frequency spectrum range with a specific width at the left and right of the center by taking the heart rate frequency point output in the previous tracking period as the center in a preset fixed tracking period, and selecting the frequency point position where a spectrum peak is positioned as the heart rate frequency point to output;
the heart rate frequency point selection unit is connected with the heart rate frequency point dynamic tracking unit.
5. The real-time dynamic heart rate monitoring device of claim 4, wherein the first and second transform polynomials are orthogonal polynomials.
6. the real-time dynamic heart rate monitoring device of claim 5, wherein the first transform polynomial is one of a Legendre polynomial, a Jacobian polynomial, a Laguerre polynomial, a Chebyshev polynomial, and an Hermite polynomial;
The second transformation polynomial is one of Legendre polynomial, Jacobian polynomial, Laguerre polynomial, Chebyshev polynomial, and Hermite polynomial.
7. a monitoring method based on the real-time dynamic heart rate monitoring device of claim 1, characterized in that the method comprises the steps of:
A. Acquiring a pulse wave signal, and eliminating a baseline drift interference signal through a baseline drift elimination module;
B. the band-pass filtering module filters the signal output by the baseline drift elimination module and reserves the signal component belonging to the heart rate frequency band;
C. The frequency domain analysis module acquires a signal frequency spectrum of the pulse wave signal in a preset specific frequency band interval;
D. the heart rate frequency point selection module acquires and outputs a frequency point corresponding to the heart rate according to the signal frequency;
the step A specifically comprises the following steps:
a1, obtaining a baseline drift trend term signal with the same length as the original pulse wave signal by a signal filtering method or a curve fitting method;
a2, storing the original pulse wave signals and the baseline wandering trend item signals by using row vectors or column vectors, and then subtracting the baseline wandering trend item signals from the original pulse wave signals according to a matrix addition rule to obtain the pulse wave signals with the baseline wandering removed;
The frequency domain analysis module is used for setting a starting frequency point, an ending frequency point and a frequency point subdivision number of a specific frequency band interval; for each frequency point of the specific frequency band interval, respectively calculating a real part of the frequency domain signal by using a first transformation polynomial, calculating an imaginary part of the frequency domain signal by using a second transformation polynomial, and calculating the power of the pulse wave signal at the frequency point f according to the real part and the imaginary part of the frequency domain signal; acquiring the power of pulse wave signals in a specific frequency band interval at each frequency point, and generating a signal frequency spectrum of the pulse wave signals in the whole concerned frequency band interval after superposition;
A pulse wave signal sequence with a data length N is represented by q (N), N is 0, 1, … …, N-1,
the calculating the real part of the frequency domain signal by using the first transform polynomial specifically comprises:
Qr＝q(0)+q(1)*cos(f)+q(2)*T(2)+…+q(N-1)*T(N-1)；
The step of calculating the imaginary part of the frequency domain signal by using the second transform polynomial specifically includes:
Qi＝-q(1)*sin(f)–q(2)*sin(f)*U(2)-…-q(N-1)*sin(f)*U(N-1)
t (n) is a first transformation polynomial, U (n) is a second transformation polynomial, and f is a frequency point;
The heart rate frequency point selection module is specifically used for two stages, wherein the first stage is accurate heart rate frequency point selection in an initial state, and the second stage is dynamic heart rate frequency point tracking;
the selection of the accurate heart rate frequency points in the initial state specifically comprises the following steps: and ensuring that the human body is in a quiet state, and obtaining a pulse wave signal frequency spectrum by eliminating the baseline drift module, the band-pass filter module and the frequency domain analysis module, wherein the frequency point position of a spectrum peak is the frequency point representing the heart rate.
8. the monitoring method according to claim 7, wherein the step B specifically comprises:
b1, acquiring preset pass band lower limit and upper limit frequencies, preset stop band lower limit and upper limit frequencies, preset pass band internal attenuation coefficients and preset stop band internal attenuation coefficients;
and B2, after the order and the coefficient of the filtering module are obtained, filtering the pulse wave signals of which the baseline drift is eliminated by the baseline drift elimination module to obtain the pulse wave signals of which the noise is eliminated.
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