CN110169764A - A kind of LMS adaptive-filtering PPG signal heart rate extracting method - Google Patents
A kind of LMS adaptive-filtering PPG signal heart rate extracting method Download PDFInfo
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Abstract
The present invention relates to a kind of cascade connection type LMS adaptive-filtering PPG signal heart rate extracting methods, comprising the following steps: original PPG signal is filtered out the out-of-band noise outside human heart rate's value normal range (NR) by bandpass filter, obtains preprocessed signal D；3-axis acceleration sensor synchronous acquisition XYZ 3-axis acceleration data are utilized when heart rate sensor acquires signal, construct the reference signal of LMS sef-adapting filter respectively using XYZ 3-axis acceleration data, and it is cascaded into triple channel cascade connection type LMS sef-adapting filter, as the input of filter after preprocessed signal, filter response Y is obtained；PPG heart rate value is calculated using spectral peak tracking algorithm of heart rate, and obtains the corresponding heart rate value of PPG signal through 3 sliding average algorithms and restriction formula optimization.The heart rate that the present invention is capable of providing a kind of PPG signal of anti-motion artifact extracts, and the method for improving the heart rate value counting accuracy of PPG signal.
Description
Technical field
The present invention relates to a kind of heart rate extracting method of PPG signal, especially a kind of LMS adaptive-filtering PPG signal heart
Rate extracting method belongs to heart rate detection technical field.
Background technique
Heart rate is very important physiological parameter, can help people at any time by carrying out round-the-clock monitoring to heart rate
Solve the health status of itself.Common heart rate signal acquisition method is photoplethysmography in wearable device at present
(photoplethysmography, PPG).The sensor for being integrated with PPG heart rate acquisition method has been applied on wearable device
, such as the model MAX30102 heart rate sensor of Maxim production.But in the collected PPG pulse wave signal of this method
Mixed motion artifacts, therefrom calculate accurate heart rate value need first to filter out it is therein because caused by motion process signal it is pseudo-
Shadow.
For the motion artifact in PPG acquisition signal, 2013, AlzahraniA etc. was acquired with three axis accelerometer and is moved
Information architecture motion artifacts reference signal, 3-axis acceleration signal have certain correlation, therefore benefit with motion disturbance signals
Doing filtering processing with 3-axis acceleration signal can reduce influence of the motion artifacts to PPG signal.2015, Po-HsiangLai
It is proposed using peak value number in the frequency spectrum of 3-axis acceleration signal and PPG signal, amplitude size, with reference to the relationship between heart rate etc.
A kind of anti motion interference heart rate extraction algorithm.In the same year, ZhilinZhang, which is proposed, to be adopted under motion conditions from wrist type equipment
The algorithm frame of heart rate is extracted in the PPG signal of collection, which includes signal decomposition, sparse reconstruct, spectral peak tracking three
Part.2016, Liu Jingsong et al. filtered motion artifacts noise in PPG signal with LMS adaptive filter algorithm.The above method is being transported
It works well when fatigue resistance is little, however, the effect is unsatisfactory in the case where there is mutation in the strong waveform of motion artifacts,
Still there is room for improvement on algorithm.
Summary of the invention
The purpose of the present invention is: inhibit motion artifact noise caused by PPG signal, improves PPG signal in human body each
The accuracy rate that heart rate value calculates under a motion state.
In order to achieve the above object, the technical solution of the present invention is to provide a kind of LMS adaptive-filtering PPG signal hearts rate
Extracting method, for extracting the approximation of human heart rate's value in the collected signal of PPG heart rate sensor, feature exists
In, comprising the following steps:
Step 1 constructs bandpass filter using human heart rate's value normal range (NR), and original PPG signal is filtered by band logical
Wave device filters out the out-of-band noise outside human heart rate's value normal range (NR), obtains preprocessed signal D；
Step 2 utilizes 3-axis acceleration sensor synchronous acquisition XYZ 3-axis acceleration when heart rate sensor acquires signal
Data construct the reference signal of LMS sef-adapting filter using XYZ 3-axis acceleration data respectively, and are cascaded into triple channel grade
Connection type LMS sef-adapting filter, the preprocessed signal D that step 1 is obtained is as triple channel cascade connection type LMS sef-adapting filter
Input obtains filter response Y；
Step 3, the filter response Y obtained using spectral peak tracking algorithm of heart rate according to step 2 calculate PPG heart rate value,
And the corresponding heart rate value of original PPG signal in step 1 is obtained with formula optimization is limited through 3 sliding average algorithms.
Preferably, the original PPG signal is the mean value of the N channel signal of sensor acquisition, in step 1, the band logical
Filter is H (f):ω indicates the frequency of original PPG signal.
Preferably, in step 2, the filtering of the triple channel cascade connection type LMS sef-adapting filter the following steps are included:
LMS sef-adapting filter adds the 3-axis acceleration sensor when step 201, calculating nth iteration
The response y (n) of speed input signal ACC (n), y (n)=ω (n) ACC (n), in formula, ω (n) indicates the coefficient of nth iteration
Vector；
Step 202, the error amount e (n), e (n)=d (n)-y (n) for calculating response y (n) and desired signal D (n)；
Step 203, according to error amount e (n) regulation coefficient vector: ω (n+1)=ω (n)+μ (n) e (n) x (n), in formula, μ
(n) be nth iteration step factor, convergent condition be 0 < μ (n) <, 1/ λmax, λmaxIt is acceleration input signal ACC (n)
The error amount e (n) of the maximum value of autocorrelation matrix, every level-one LMS sef-adapting filter is used as next stage LMS sef-adapting filter
Desired signal D (n).
Preferably, the step 3 the following steps are included:
Filter response Y in Time Domain Decomposition is the time window that a length is h by step 301, each time window it
Between the not only time be s, every time in individual time window filter response Y-signal carry out NFThe discrete fourier variation of point
Seek corresponding frequency spectrum；
Step 302, the estimation heart rate N by previous time window0Spectrum position as reference, and region of search P0=
[N0-Δs..., N0+Δs] interior highest spectrum peak position Ncur, in formula, ΔsIndicate 10；
Step 303, the estimated value for calculating heart rate valueCalculation formula are as follows:In formula, FsTable
Show the sample frequency of filter response Y,
Step 304 carries out 3 sliding average algorithms, and calculation formula is as follows:
In formula, B-1And B-2Indicate the estimation heart rate value of the first two continuous time window；α, β, γ indicate 0.9,0.05,
0.05；
Step 305 is defined formula optimization, limits formula are as follows:
In formula, λincAnd λdecIt indicates to limit constant, is empirical；BestIndicate finally obtained heart rate value；
A kind of adaptive reference of cascade connection type LMS adaptive-filtering PPG signal heart rate extracting method provided by the invention
Signal comes from 3-axis acceleration signal, and algorithm effect when filtering out the motion artifacts in PPG signal is more preferable, finally by spectrum cutting edge of a knife or a sword
The calculated heart rate value accuracy rate of back tracking method is higher, rear to increase what heart rate extracted using 3 sliding average algorithms and restriction formula
Reliability, and can improve under the various motion states of human body the accuracy rate of heart rate value.
On the basis of existing LMS adaptive filter algorithm PPG signal processing technology, LMS sef-adapting filter is improved
The reference signal of the LMS sef-adapting filter of existing single-stage is derived from the mean value of XYZ 3-axis acceleration by structure, is changed to tri- axis of XYZ
The cascade form of acceleration triple channel LMS sef-adapting filter, this improvement can be improved the reference letter because of LMS sef-adapting filter
Filter effect when number or correlation difference related to motion artifact part, further improves existing LMS sef-adapting filter
Validity, applicability and accuracy.
On the application foundation of existing spectral peak tracing algorithm, 3 points of sliding algorithms are combined with restriction formula, into one
Step improves the accuracy rate of the existing single calculated heart rate value of spectral peak tracing algorithm, show that the changes in heart rate of linear fit is bent
Line is more preferable.
Detailed description of the invention
Fig. 1 is a kind of LMS adaptive-filtering PPG signal heart rate extracting method flow chart in the embodiment of the present invention；
Fig. 2 is the front and back comparison diagram that out-of-band noise is filtered out in the embodiment of the present invention；
Fig. 3 is single-stage LMS sef-adapting filter flow chart in the embodiment of the present invention；
Fig. 4 is that single-stage LMS sef-adapting filter filters out motion artifacts failure frequency-domain analysis figure in the embodiment of the present invention；
Fig. 5 is that the embodiment of the present invention cascade type LMS sef-adapting filter inhibits motion artifacts spectrum analysis figure；
Fig. 6 is single-stage and cascading filter filter effect comparison diagram in the embodiment of the present invention；
Fig. 7 is single-stage and cascade LMS sef-adapting filter heart rate extraction accuracy curve comparison figure in the embodiment of the present invention；
Fig. 8 is the heart rate extraction accuracy curve graph that 3 points of sliding algorithms are not used in the embodiment of the present invention；
The error rate of two kinds of Processing Algorithms compares under different motion state in Fig. 9 the embodiment of the present invention.
Specific embodiment
Present invention will be further explained below with reference to specific examples.It should be understood that these embodiments are merely to illustrate the present invention
Rather than it limits the scope of the invention.In addition, it should also be understood that, after reading the content taught by the present invention, those skilled in the art
Member can make various changes or modifications the present invention, and such equivalent forms equally fall within the application the appended claims and limited
Range.
A kind of LMS adaptive-filtering PPG signal heart rate extracting method provided by the invention the following steps are included:
(1) bandpass filter is constructed using human heart rate's value normal range (NR), original PPG signal is passed through into bandpass filter
The out-of-band noise outside human heart rate's value normal range (NR) is filtered out, preprocessed signal D is obtained.
Original PPG signal is the mean value of the N channel signal of sensor acquisition, wherein the bandpass filter H (f) are as follows:ω indicates the frequency of original PPG signal.
(2) 3-axis acceleration sensor synchronous acquisition XYZ 3-axis acceleration number is utilized when heart rate sensor acquires signal
According to, construct the reference signal of LMS sef-adapting filter respectively using XYZ 3-axis acceleration data, and be cascaded into triple channel cascade
Type LMS sef-adapting filter, input of the preprocessed signal D as filter in (1) obtain filter response Y.
The filtering of triple channel cascade connection type LMS sef-adapting filter is as follows:
LMS sef-adapting filter adds the 3-axis acceleration sensor when step 201, calculating nth iteration
The response y (n) of speed input signal ACC (n), y (n)=ω (n) ACC (n), in formula, ω (n) indicates the coefficient of nth iteration
Vector；
Step 202, the error amount e (n), e (n)=d (n)-y (n) for calculating response y (n) and desired signal D (n)；
Step 203, according to error amount e (n) regulation coefficient vector: ω (n+1)=ω (n)+μ (n) e (n) x (n), in formula, μ
(n) be nth iteration step factor, convergent condition be 0 < μ (n) <, 1/ λmax, λmaxIt is acceleration input signal ACC (n)
The error amount e (n) of the maximum value of autocorrelation matrix, every level-one LMS sef-adapting filter is used as next stage LMS sef-adapting filter
Desired signal D (n).
(3) the filter response Y obtained using spectral peak tracking algorithm of heart rate according to (2) calculates PPG heart rate value, and through three
Point sliding average algorithm and restriction formula optimization obtain the corresponding heart rate value of original PPG signal in (1), comprising the following steps:
Filter response Y in Time Domain Decomposition is the time window that a length is h by step 301, each time window it
Between the not only time be s, every time in individual time window filter response Y-signal carry out NFThe discrete fourier variation of point
Seek corresponding frequency spectrum；
Step 302, the estimation heart rate N by previous time window0Spectrum position as reference, and region of search P0=
[N0-Δs..., N0+Δs] interior highest spectrum peak position Ncur, in formula, ΔsIndicate 10；
Step 303, the estimated value for calculating heart rate valueCalculation formula are as follows:In formula, FsTable
Show the sample frequency of filter response Y,
Step 304 carries out 3 sliding average algorithms, and calculation formula is as follows:
In formula, B-1And B-2Indicate the estimation heart rate value of the first two continuous time window；α, β, γ indicate 0.9,0.05,
0.05 and alpha+beta+γ=1；
Step 305 is defined formula optimization, limits formula are as follows:
In formula, λincAnd λdecIt indicates to limit constant, is empirical；BestIndicate finally obtained heart rate value；
It is 5 males that PPG, which acquires signal, in this example, 5 women, the age between 21~25 years old, use is static, jump,
It waves, four kinds of motion states of running, every kind of group data acquisition time is 2 minutes, and sample frequency and acceleration transducer sampling
Frequency is 125Hz, and the resolving time window that spectral peak tracks heart rate extraction algorithm is 8 seconds, the stepping time between each time window
It is 2 seconds, the discrete Fourier transform points of time window are 4096, and 3 points of sliding algorithm parameters are followed successively by 0.9,0.05 and 0.05.
Fig. 1 is a kind of LMS adaptive-filtering PPG signal heart rate extracting method flow chart.
As shown in Figure 1, filter out out-of-band noise with bandpass filter first, according to the normal heart rate range of human body be 24~
210 beats/min, the range of corresponding frequency domain is 0.4~3.5Hz, and Fig. 2 is PPG signal bandpass filtering result.
Fig. 3 is single-stage LMS sef-adapting filter flow chart.
As shown in figure 3, signal D (n) of the PGG signal after band-pass filter subtracts the fortune of adaptive-filtering construction
Dynamic puppet difference signal, obtain with signal e (n) similar in true PPG signal, the reference signal of single-stage LMS sef-adapting filter is XYZ
The mean value of 3-axis acceleration, Fig. 4 are that single-stage LMS sef-adapting filter filters out motion artifacts failure frequency-domain analysis figure, heart rate in figure
Reference signal is that ECG mode synchronizes measuring signal to heart rate, for analyzing comparison.
Fig. 5 is that cascade connection type LMS sef-adapting filter inhibits motion artifacts spectrum analysis figure.
As shown in figure 5, LMS sef-adapting filter is changed to the filter of XYZ 3-axis acceleration triple channel cascade structure, it will
The motion artifacts of tri- axis direction of XYZ carry out LMS adaptive-filtering respectively, obtained effect picture.
Fig. 6 is single-stage and cascading filter filter effect comparison diagram in the embodiment of the present invention.
As shown in fig. 6, the Spectrum Analysis Comparison in single window of PGG signal goes out the filter effect of cascading filter than single
The effect of grade filter show that cascading filter can be filtered out because the reference signal of acceleration signal construction is related to motion artifacts
Property difference error.
According to result e (n) signal that cascade connection type LMS sef-adapting filter obtains, in spectral peak tracing algorithm module, to e (n)
Signal is split as the window of 148 frames, and window stepping time is 2 seconds, carries out 4096 point quick Fourier transformation to each frame,
Be not in the rule of random fluctuation according to human heart rate's variation, correspond to frequency domain in the window and carry out range searching maximum value,
Middle search range is P0=[N0-Δs..., N0+Δs], N0For the spectral peak of a upper window.According to formulaObtain the estimated value of heart rate, FsFor the sample frequency of d signal, NFFor the point of Fast Fourier Transform (FFT)
Number.Estimate heart rate valueIn 3 points of sliding algorithms and the smooth changes in heart rate curve of formula is limited, certain group according to Fig. 7 and Fig. 8
The heart rate value change curve of single-stage LMS sef-adapting filter and cascade LMS sef-adapting filter that data obtain, and calculate this
The average absolute percentage of group data mean absolute percentage error and mean absolute error, single-stage LMS sef-adapting filter misses
Difference and mean absolute error are respectively as follows: 1.81%, 1.77, cascade LMS sef-adapting filter mean absolute percentage error and
Mean absolute error is respectively as follows: 1.38%, 1.35.Fig. 8 is cascade LMS sef-adapting filter before and after enabling 3 points of sliding algorithms
Heart rate accuracy curve comparison figure, cascade LMS sef-adapting filter mean absolute percentage error and mean absolute error
It is respectively as follows: 1.44%, 1.40.q
The statistical chart of the mean absolute error rate for the heart rate value that final 40 groups of data obtain under this methodology.It can be concluded that this
Invention improves the precision of PPG signal heart rate extraction to a certain extent, is able to suppress and mentions because of motion artifact to PPG signal heart rate
The influence taken, and it is suitable for the various motion states of human body, there is stronger generalization.
Claims (4)
1. a kind of LMS adaptive-filtering PPG signal heart rate extracting method, in the collected signal of PPG heart rate sensor
Extract the approximation of human heart rate's value, which comprises the following steps:
Step 1 constructs bandpass filter using human heart rate's value normal range (NR), and original PPG signal is passed through bandpass filter
The out-of-band noise outside human heart rate's value normal range (NR) is filtered out, preprocessed signal D is obtained；
Step 2 utilizes 3-axis acceleration sensor synchronous acquisition XYZ 3-axis acceleration number when heart rate sensor acquires signal
According to, construct the reference signal of LMS sef-adapting filter respectively using XYZ 3-axis acceleration data, and be cascaded into triple channel cascade
Type LMS sef-adapting filter, the preprocessed signal D that step 1 is obtained are defeated as triple channel cascade connection type LMS sef-adapting filter
Enter, obtains filter response Y；
Step 3, the filter response Y obtained using spectral peak tracking algorithm of heart rate according to step 2 are calculated PPG heart rate value, and passed through
3 sliding average algorithms obtain the corresponding heart rate value of original PPG signal in step 1 with formula optimization is limited.
2. a kind of LMS adaptive-filtering PPG signal heart rate extracting method according to claim 1, it is characterised in that: described
Original PPG signal is the mean value of the N channel signal of sensor acquisition, and in step 1, the bandpass filter is H (f):ω indicates the frequency of original PPG signal.
3. a kind of LMS adaptive-filtering PPG signal heart rate extracting method according to claim 1, it is characterised in that: step
In 2, the filtering of the triple channel cascade connection type LMS sef-adapting filter the following steps are included:
The acceleration that LMS sef-adapting filter obtains the 3-axis acceleration sensor when step 201, calculating nth iteration
The response y (n) of input signal ACC (n), y (n)=ω (n) ACC (n), in formula, ω (n) indicates the coefficient vector of nth iteration；
Step 202, the error amount e (n), e (n)=d (n)-y (n) for calculating response y (n) and desired signal D (n)；
Step 203, according to error amount e (n) regulation coefficient vector: ω (n+1)=ω (n)+μ (n) e (n) x (n), in formula, μ (n)
It is the step factor of nth iteration, convergent condition is 0 < μ (n) <, 1/ λmax, λmaxAcceleration input signal ACC (n) from
The maximum value of correlation matrix, the error amount e (n) of every level-one LMS sef-adapting filter is as next stage LMS sef-adapting filter
Desired signal D (n).
4. a kind of LMS adaptive-filtering PPG signal heart rate extracting method according to claim 1, it is characterised in that: described
Step 3 the following steps are included:
Filter response Y in Time Domain Decomposition is the time window that a length is h by step 301, between each time window
Not only the time is s, carries out N to the filter response Y-signal in individual time window every timeFThe discrete fourier variation of point is asked pair
The frequency spectrum answered；
Step 302, the estimation heart rate N by previous time window0Spectrum position as reference, and region of search P0=[N0-
Δs..., N0+Δs] interior highest spectrum peak position Ncur, in formula, ΔsIndicate 10；
Step 303, the estimated value for calculating heart rate valueCalculation formula are as follows:In formula, FsIndicate filter
Wave device responds the sample frequency of Y,
Step 304 carries out 3 sliding average algorithms, and calculation formula is as follows:
In formula, B-1And B-2Indicate the estimation heart rate value of the first two continuous time window；α, β, γ indicate 0.9,0.05,0.05；
Step 305 is defined formula optimization, limits formula are as follows:
In formula, λincAnd λdecIt indicates to limit constant, is empirical；BestIndicate finally obtained heart rate value；
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Cited By (8)
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| CN111048089A (en) * | 2019-12-26 | 2020-04-21 | 广东思派康电子科技有限公司 | Method for improving voice awakening success rate of intelligent wearable device, electronic device and computer readable storage medium |
| CN111616695A (en) * | 2020-06-29 | 2020-09-04 | 歌尔科技有限公司 | Heart rate acquisition method, device, system and medium |
| CN111714112A (en) * | 2020-04-09 | 2020-09-29 | 上海电气集团股份有限公司 | Real-time electrocardiogram artifact elimination method |
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| CN112370036A (en) * | 2020-10-20 | 2021-02-19 | 复旦大学 | PPG heart rate extraction device and method based on cascade RLS adaptive filtering |
| CN113349752A (en) * | 2021-05-08 | 2021-09-07 | 电子科技大学 | Wearable device real-time heart rate monitoring method based on sensing fusion |
| CN114136347A (en) * | 2021-11-30 | 2022-03-04 | 成都维客昕微电子有限公司 | Living body detection method and system based on photoplethysmography |
| WO2022227843A1 (en) * | 2021-04-26 | 2022-11-03 | 安徽华米健康医疗有限公司 | Wearable device, and heart rate tracking method therefor and heart rate tracking apparatus thereof |
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| CN112370036A (en) * | 2020-10-20 | 2021-02-19 | 复旦大学 | PPG heart rate extraction device and method based on cascade RLS adaptive filtering |
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| CN113349752B (en) * | 2021-05-08 | 2022-10-14 | 电子科技大学 | Wearable device real-time heart rate monitoring method based on sensing fusion |
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