CN105919584B - Heart rate method of estimation and device for wearable heart rate monitor apparatus - Google Patents
Heart rate method of estimation and device for wearable heart rate monitor apparatus Download PDFInfo
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- 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
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Abstract
The invention discloses a kind of heart rate methods of estimation and device for wearable heart rate monitor apparatus.It is removed the invention mainly comprises motion artifacts and heart rate spectral peak tracks two parts.Motion artifacts remove：The non-linear relation between the motion artifacts noise in noise reference signal and pulse wave signal is captured first with nonlinear adaptive filtering method, to effectively eliminate motion artifacts interference, then judge whether filtered pulse wave signal still contains much noise using the binary decision method based on classification, be that still noise-containing pulse wave signal further removes noise jamming using singular spectrum analysis method to judgement；Heart rate spectral peak tracking based on frequency spectrum again, positions the heart rate spectral peak of each time window, that is, is primarily based on non-linear positioning mode positioning heart rate spectral peak, if cannot successfully position, then is based on the legal position heart rate spectral peak of classification and orientation.The present invention estimates that computational accuracy is high, complexity is low for heart rate, to ensure that its exploitativeness in wearable monitoring device.
Description
Technical field
The present invention relates to processing of biomedical signals field more particularly to a kind of hearts for wearable heart rate monitor apparatus
Rate method of estimation and device.
Background technology
Heart rate is a very important index in human body physiological parameter, while heart rate can also be used as human motion physiology
The effectively reference of one of load objective evaluation, and the rhythm of the heart based on wearable device is control human motion intensity one
Important and effective means.
At present for the monitoring of heart rate there are mainly two types of method, one is traditional rhythm of the heart based on electrocardiosignal,
This method requires several electrodes in the different parts of human body while acquiring electro-physiological signals, then according to collected signal
Heart rate is calculated, this is a kind of most common method in clinical treatment.However the disadvantage is that the activity of the human body limited significantly,
Therefore this method is not particularly suited for the rhythm of the heart under human motion state.Another method is to be based on photoplethysmographic
The rhythm of the heart of graphical method, this method are to detect the variation of volumetric blood by human skin by photoelectric technology, and blood
The variation of liquid volume be by heart regularly diastole with shrink caused by, therefore can be according to collected photoelectricity volume pulsation
Wave signal (PPG signals) carrys out monitor heart rate.The advantages of this method is that signal acquisition is very convenient, it is only necessary to a photoelectric sensing
Device and skin contact, and the activity of human body can be unaffected, and this is also that current wearable heart rate monitor apparatus is the most frequently used
A kind of method.
But since photoplethysmographic signal is the signal acquired from skin surface, signal strength is weak, and easily by
Interference, industrial frequency noise, ambient noise, motion artifacts all can cause prodigious influence to the signal quality of acquisition, and wherein most main
It is wanting precisely due to human motion and caused by motion artifacts interfere, and this motion artifacts interference main frequency very much
In the case of can and the frequency of heart rate overlap, it is difficult to eliminate.Therefore, being believed based on photoplethysmographic under motion state
Number rhythm of the heart still have certain challenge.
In order to solve this problem, many methods attempt elimination motion artifacts noise at present.Adaptive-filtering
It is a kind of common denoising method, but it excessively relies on reference signal, if reference signal selection is improper, denoising effect
Fruit will be very undesirable.In addition, motion artifacts are not often linear correlation in strenuous exercise with pulse wave signal, this
Also have a great impact to the denoising effect of adaptive-filtering.In addition to this, singular spectrum analysis is another removal motion artifacts
The preferable method of effect, but this method based on signal decomposition often has prodigious calculation amount, this is for requiring low work(
For the wearable device of consumption and it is not suitable for.Therefore, there is the motion artifacts noise measuring list based on Neyman-Person detection
The two benches Denoising Algorithm of member and FDICA (Frequency Domain Independent Component Analysis).It should
Method is when subject motion's amount is small (motion artifacts are smaller), the de-noising effect that can have been obtained.But work as amount of exercise
When big, by critical noisy interference pulse wave signal and be unsatisfactory for ICA (Independent Component
Analysis the statistical independence required by), therefore it is not suitable for the big situation of amount of exercise.And based on empirical mode decomposition and
In the mixed method of spectrum-subtraction, the intrinsic mode functions and acceleration that are obtained after empirical mode decomposition using pulse wave signal
Linearly dependent coefficient between signal detects the presence of motion artifacts noise in the intrinsic mode functions, to further utilizing spectrum
Subtract method removal motion artifacts.But due to being usually not linearly related between pulse wave signal and motion artifacts,
The performance of mixed method that is subtracted based on empirical mode decomposition and spectrum is simultaneously unstable.The patent application of Publication No. CN104161505A
The method of a kind of filtering of combining adaptive and Mallat are proposed to eliminate motion artifacts noise, still, after adaptive-filtering
Pulse wave signal when containing only seldom noise when clean (i.e. filtered pulse wave signal), still use Mallat after
Continuous de-noising, this does not have the effect of denoising not only undoubtedly but also can increase algorithm calculation amount.In fact, not all time window
Pulse wave signal all contain much noise, at this time merely with adaptive filter algorithm (not needing Mallat algorithms) can will
Noise remove is clean.
In addition, there are also researchers to attempt method (the i.e. spectral peak tracking side that the searching heart rate in frequency spectrum corresponds to spectral peak
Method) improve the precision of the heart rate detection based on photoplethysmographic under motion state.Such as chased after based on didactic spectral peak
Track method (including spectral peak detection-phase and spectral peak Qualify Phase) is realized finds the corresponding spectral peak of heart rate in frequency spectrum.However, inspiring
There is rules to be overly dependent upon the shortcomings that artificially setting and parameter can be adjusted arbitrarily for formula method, can lead to the performance of detection
It is unstable.
Invention content
The goal of the invention of the present invention is：In view of the above problems, it provides a kind of for wearable rhythm of the heart
The heart rate method of estimation of equipment, the method accuracy is high, and computation complexity is low, can achieve the purpose that real-time estimation, Ke Yifang
Just be applied to wearable heart rate monitor apparatus in.
The heart rate method of estimation for wearable heart rate monitor apparatus of the present invention, includes the following steps：
To the original pulse wave signal under the motion state of photoplethysmographic sensor acquisition, motion sensor acquisition
Original motion signal (such as 3-axis acceleration signal) divide time window and to each time window carry out heart rate estimation：
Step 1：Bandpass filtering treatment is carried out to original pulse wave signal, the original motion signal of current time window, is obtained
Pulse wave signal s0And noise reference signal；
Step 2：Pulse wave signal s is obtained using nonlinear adaptable filter0With the nonlinear dependence of noise reference signal
System, i.e. noise estimate signal；Estimate signal to pulse wave signal s based on noise0It is filtered to obtain pulse wave signal sk；
Step 3：To pulse wave signal skCharacteristic information (such as time domain, frequency domain and small echo characteristic of field) is extracted, and is based on
The binary decision method of classification is by pulse wave signal skBe divided into totally with unclean two class；
It is clean pulse wave signal s for classificationk, then directly as pulse wave signal sc；
It is sordid pulse wave signal s for classificationk, then it is based on noise reference signal, uses singular spectrum analysis method
Remove pulse wave signal skIn noise jamming, obtain pulse wave signal sc；
Step 4：Obtain pulse wave signal scFrequency spectrum, be denoted as the first frequency spectrum；Acquisition Nonlinear Processing (such as scSquare,
Cube etc.) after pulse wave signal scFrequency spectrum, be denoted as the second frequency spectrum, any ways customary, such as period can be used in acquisition modes
Figure method；
The first frequency spectrum, the second frequency spectrum are obtained in default base frequency range R0Preceding D highest spectral peak, wherein corresponding first frequency spectrum
The spectrum peak position (i.e. horizontal axis index value, the wherein longitudinal axis indicate spectral peak amplitude) of preceding D highest spectral peak be denoted as f1,f2,…,fD, right
The spectrum peak position of the preceding D highest spectral peak of the second frequency spectrum is answered to be denoted as p1,p2,…,pD；
The heart rate spectral peak of current time window is positioned based on non-linear positioning mode, if cannot position, then is based on classification and orientation method
Positioning；
Wherein, non-linear positioning mode is：Search f1,f2,…,fDWith p1,p2,…,pDIn there are difference be less than or equal to it is pre-
If the spectrum peak position f of threshold value T1i, and fiIt is less than or equal to predetermined threshold value T2 with the difference of Prev, then spectrum peak position fiWhen being current
Between window heart rate spectrum peak position, wherein i ∈ { 1,2 ..., D }, Prev indicate the spectral peak position of the heart rate spectral peak of upper time window determination
It sets, the initial value of Prev is the pulse wave signal s of original time windowcFrequency spectrum top spectrum peak position；
Classification and orientation method is：By the pulse wave signal s of time windowscAs the training sample of grader, it is based on priori
Knowledge extracts pulse wave signal scCharacteristic information build grader, and the heart rate spectrum peak position of specified different classifications result；It carries
Take the pulse wave signal s of current time windowcCharacteristic information and input grader and carry out classification judgement, corresponded to based on current class
Heart rate spectrum peak position determine the heart rate spectrum peak position of current time window；
Step 5：Heart rate spectrum peak position based on time window calculates heart rate value, for example, first according to frequency spectrum (the first frequency spectrum or
Second frequency spectrum) frequency range and Fourier transformation points obtain heart rate spectrum peak position where coordinate system unit coordinate point frequency
Rate value, to obtain the frequency values of heart rate spectrum peak position, i.e., heart rate value per second.
Further, in the classification and orientation method of step 4, the class object that the grader of structure includes has C1, C2, C3 tri-
Class：
Judge spectrum peak position f1,f2,…,fDWith the first frequency spectrum in default harmonic frequency range R1Preceding D highest spectral peak spectral peak
Position h1,h2,…,hDWith the presence or absence of harmonic wave pair, that is, judge whether | 2fj-hm|≤T3, if so, the arteries and veins of current time window
Fight wave signal scBelong to classification C1, and the corresponding heart rate spectrum peak positions of classification C1 are fj, wherein j ∈ { 1,2 ..., D }, m ∈ 1,
2 ..., D }, T3 is predetermined threshold value；
Otherwise continue to judge f1,f2,…,fDIn whether there is spectral peak fjMeet | fj- Prev |≤T4, if so, when current
Between window pulse wave signal scBelong to classification C2, and the corresponding heart rate spectrum peak positions of classification C2 are fj, wherein j ∈ { 1,2 ..., D },
T4 is predetermined threshold value；Otherwise the pulse wave signal s of current time windowcBelong to classification C3, and classification C3 corresponding heart rate spectral peaks position
It is set to Prev.
Further, R0For [Prev- Δs, Prev+ Δs], R1For：[2 (Prev- Δs -1)+1,2 (Prev+ Δs -1)+1],
Middle Δ indicates parameter preset.
Meanwhile the invention also discloses a kind of wearable heart rate monitor apparatus, including signal gathering unit, Signal Pretreatment
Unit, signal denoising unit, rate calculation unit and output unit；
Wherein signal gathering unit includes photoplethysmographic sensor and motion sensor, is existed for acquiring measured
Original pulse wave signal, original motion signal under motion state are simultaneously transferred to Signal Pretreatment unit；
Signal Pretreatment unit carries out time window division to input signal and carries out bandpass filtering treatment, to signal denoising list
Member input pulse wave signal s0And noise reference signal；
Signal denoising unit captures pulse wave signal s by nonlinear adaptable filter0It is non-with noise reference signal
Linear relationship, i.e. noise estimate signal；Being based on noise again estimates signal to pulse wave signal s0It is filtered to obtain pulse
Wave signal sk；And pulse wave signal s is adjudicated using the binary decision method of classification by decision unitkWhether it is clean, if
It is, then directly by pulse wave signal skAs pulse wave signal scAnd input rate calculation unit；Otherwise noise reference is based on to believe
Number, use singular spectrum analysis method removal pulse wave signal skIn noise jamming, obtain pulse wave signal scInput heart rate again afterwards
Computing unit；
Rate calculation unit：As unit of time window, each time is positioned in conjunction with non-linear positioning mode and classification and orientation method
The pulse wave signal s of windowcHeart rate spectral peak, and heart rate spectrum peak position based on each time window calculates the heart rate of current time window
It is worth and is sent to output display unit；
Wherein, the pulse wave signal s of each time window is positionedcHeart rate spectral peak be specially：
Obtain pulse wave signal scFrequency spectrum, be denoted as the first frequency spectrum；Obtain the pulse wave signal s after Nonlinear ProcessingcFrequency
Spectrum, is denoted as the second frequency spectrum；The first frequency spectrum, the second frequency spectrum are obtained in default base frequency range R0Interior preceding D highest spectral peak, wherein right
The spectrum peak position of the preceding D highest spectral peak of the first frequency spectrum is answered to be denoted as f1,f2,…,fD, the preceding D highest spectral peak of corresponding second frequency spectrum
Spectrum peak position be denoted as p1,p2,…,pD
In non-linear positioning mode：Search f1,f2,…,fDWith p1,p2,…,pDIn be less than or equal to the presence or absence of difference it is pre-
If the spectrum peak position f of threshold value T1i, and fiIt is less than or equal to predetermined threshold value T2 with the difference of Prev, if it is present spectrum peak position
fiFor the heart rate spectrum peak position of current time window, wherein i ∈ { 1,2 ..., D },
In classification and orientation method：By the pulse wave signal s of time windowscAs the training sample of grader, arteries and veins is extracted
Fight wave signal scCharacteristic information build grader, and the heart rate spectrum peak position of specified different classifications result；Extract current time
The pulse wave signal s of windowcCharacteristic information and input grader and carry out classification judgement, be based on the corresponding heart rate spectral peak of current class
The heart rate spectrum peak position of location determination current time window；
Output unit：As unit of time window, real-time display rhythm of the heart is as a result, the i.e. heart rate value of current time window.
In conclusion by adopting the above-described technical solution, the beneficial effects of the invention are as follows：Calculated heart rate accuracy
Height, computation complexity are low.
Description of the drawings
Fig. 1 is the wearable heart rate monitor apparatus cellular construction schematic diagram of the present invention；
Fig. 2 is the heart rate estimation flow chart of the present invention.
Specific implementation mode
To make the object, technical solutions and advantages of the present invention clearer, with reference to embodiment and attached drawing, to this hair
It is bright to be described in further detail.
Referring to Fig. 1,2, wearable heart rate monitor apparatus of the invention include signal gathering unit, Signal Pretreatment unit,
Signal denoising unit, rate calculation unit and output unit.
Wherein, signal gathering unit includes photoplethysmographic sensor and motion sensor, for acquiring source signal
(include original pulse wave signal, original motion signal of the measured under motion state, the source signal used in the present embodiment is
Collected in the wrist of measured by photoelectric sensor and 3-axis acceleration sensor, the sample frequency of signal is 125Hz),
And it inputs to Signal Pretreatment unit and carries out Signal Pretreatment.
Signal Pretreatment unit divides time window to original signal using sliding window method and utilizes bandpass filter pair simultaneously
Signal carries out bandpass filtering, in the present embodiment, is split first to source signal, using sliding window method, window size is set as 8
Second, sliding step is set as 2 seconds, calculates the average heart rate of current time window.According to the heart rate range of mankind's actual capabilities, (40 arrive
It is per minute under 160), using the bandpass filter that passband is 0.4Hz~5Hz (under 24 to 300 per minute) to the letter after segmentation
Number carry out bandpass filtering, signal frequency range is limited in 0.4Hz~5Hz.It is denoted as s by pretreated pulse wave signal0,
3-axis acceleration signal is denoted as Acc, and wherein three axis signal of x, y, z is denoted as acc1, acc2, acc3 respectively.
Signal denoising unit captures pulse wave signal s by nonlinear adaptable filter0With noise reference signal (three
Axle acceleration signal Acc) non-linear relation, to pulse wave signal s0It is filtered：
Using 3-axis acceleration signal Acc as the input signal of nonlinear adaptable filter, pulse wave signal s0As
The desired signal of nonlinear adaptable filter.Before proceeding by adaptive-filtering, truncation Volterra sequence pairs are utilized
3-axis acceleration signal Acc is recombinated to obtain input signal x (k), i.e.,：
Wherein, i=1,2,3, k be sampled point serial number (k=1,2 ..., M), and M is time window length (M=in the present embodiment
1000)。
Input signal x (k) passes through nonlinear adaptable filter, and filtering system is constantly updated based on recurrence least square criterion
Number w (k) is restrained until object function ξ (k), wherein object functionSubscript " T " table
Show matrix transposition, similarly hereinafter.The iterative formula of filter factor w (k) is：Its
Middle λ is forgetting factor, and effect is to reinforce the influence of current data, reduces the influence of historical data, the present embodiment takes λ=0.1.
After by nonlinear adaptive filtering, output signal y (k) can be obtained, i.e. noise estimates signal.Based on making an uproar
Sound estimates signal y (k) to pulse signal s0It is filtered to obtain pulse wave signal sk：sk=s0(k)-y(k)。
Meanwhile signal denoising unit further includes decision unit, singular spectrum analysis denoising unit, i.e., passes through decision unit first
Using the binary decision method judgement pulse wave signal s of classificationkWhether it is clean (whether containing noise), if so, directly will
Pulse wave signal skAs pulse wave signal scAnd input rate calculation unit；Otherwise it is based on noise reference signal y (k), using strange
Different spectrum analysis denoising unit removal pulse wave signal skIn noise jamming, obtain pulse wave signal scInput rate calculation again afterwards
Unit；
In the present embodiment, the binary decision method of classification is realized based on random forests algorithm.I.e. first to pulse wave signal
skFeature extraction is carried out, including：Temporal signatures：Pulse wave signal skEnergy, mean value, variance；Frequency domain character：Pulse wave signal
skThe mean value of frequency spectrum, variance, notable wave crest quantity (referring to the wave crest that peak value is more than predetermined threshold value), pulse wave signal skFrequency spectrum
With pulse wave signal s0Frequency spectrum related coefficient, pulse wave signal skFrequency spectrum and noise reference signal frequency spectrum Pearson came
Related coefficient.Small echo characteristic of field：The energy of each subband signal, mean value, variance etc. after signal wavelet decomposition.The present embodiment uses 5
Layer wavelet decomposition, the morther wavelet selected is db4 small echos.
After extracting features above, these features are formed into a feature vector and are classified using grader.So
Feature vector is input to random forest afterwards, each decision tree in random forest is mutual indepedent according to the feature vector of input
Classification is made on ground, and the corresponding pulse wave signal of feature vector is divided into clean (being labeled as 0) or unclean (being labeled as 1) two
Then class obtains final classification results according to Voting principles.
Singular spectrum analysis denoising unit is to filtering pulse wave signal skNoise jamming is further removed, singular value decomposition is based on
By pulse wave signal skIt is decomposed into d time series ai(i=1,2 ..., d), while calculating the frequency spectrum of each time series and searching most
The corresponding frequency values of amplitude；And it calculates the frequency spectrum of noise reference signal and counts major frequency components (amplitude is more than default
The corresponding frequency content of spectral peak of threshold value)；Each time series is judged successively, if the maximum amplitude of current time sequence is corresponding
Frequency values are Chong Die with the major frequency components of noise reference signal, then delete current time sequence, to the time series of reservation into
Row reconstruct obtains pulse wave signal sc.Specific implementation mode is specially：
First by pulse wave signal skIt is mapped as the matrix S of a L × M, wherein K=N-L+1, L ＜ M/2, i.e.,：
M is time window length (M=1000 in the present embodiment).
Singular value decomposition is carried out to matrix S again：Wherein Si=σiμiνi T, and σi, μi,
νiRespectively i-th of singular value and corresponding left singular vector and right singular vector, and it is directed to each matrix SiIt is put down using diagonal
Equal method acquires corresponding time series ai.Obtaining time series a1,a2,…,adAfterwards, time series a is calculatediFrequency spectrum and lookup
The corresponding frequency values f of its frequency spectrum maximum amplitudei。
Then it calculates the frequency spectrum of noise reference signal Acc and counts wherein major frequency components (amplitude is more than predetermined threshold value
The corresponding frequency content of spectral peak) corresponding frequency values, constitute set Fa.If fiIn set FaMiddle appearance, then delete fiInstitute is right
I-th of the time series a answeredi.Remaining time series is finally recycled to be reconstructed to obtain the pulse after further denoising
Wave signal sc。
Rate calculation unit：To pulse wave signal scFrequency spectrum carry out heart rate spectral peak tracking, position the heart of each time window
Rate spectrum peak position, and the heart rate spectrum peak position based on each time window calculates the heart rate value of current time window and is sent to output and shows
Show unit.
Pulse wave signal s is obtained firstcFrequency spectrum (such as corresponding frequency spectrum is obtained based on period map method), be denoted as frequency spectrum 1, with
And the pulse wave signal s after Nonlinear ProcessingcFrequency spectrum, be denoted as frequency spectrum 2, in this way can more frequency spectrum versions, to increase
Find the probability that heart rate corresponds to spectral peak.In the present embodiment, Nonlinear Processing is by the way of squared.Then two frequency spectrums are set
Range, i.e. base frequency range R0：[Prev- Δs, Prev+ Δs], harmonic frequency range R1：[2(Prev-Δ-1)+1,2(Prev+Δ-1)+
1], for the ease of realizing, in the present embodiment, base frequency range R0, R1 the discrete coordinate (cross of frequency spectrum in wavelength coverage is preset for it
Discrete coordinate on axis).
Spectrum peak position before being found respectively within the scope of two of frequency spectrum 1 corresponding to 2 highest spectral peaks, i.e. highest spectral peak and
The horizontal axis index value of secondary high spectral peak, wherein in range R0That inside finds is denoted as f1, f2；That is found in range R1 is denoted as h1, h2.
The range R of frequency spectrum 20Highest, the secondary high spectral peak inside found is denoted as p1, p2.Wherein parameter preset Δ is positive integer.
Rate calculation unit is primarily based on the heart rate spectral peak of non-linear positioning mode positioning current time window, if cannot position,
Classification and orientation method is based on again to position；
Wherein, non-linear positioning mode is：Search f1,f2With p1,p2In with the presence or absence of difference be less than or equal to predetermined threshold value T1
(the usual value range of T1 is 0~3, and the present embodiment is taken as spectrum peak position f 2)i(i ∈ { 1,2 }), and fiWith the difference of Prev
Less than or equal to predetermined threshold value T2, (the usual value range of T2 is 0~6, and 3) the present embodiment is taken as, if it is present spectral peak position
Set fiFor the corresponding heart rate spectrum peak position of current time window.
Classification and orientation method is：Extract pulse wave signal scFeature vector, using random forests algorithm, structure includes classifying
The grader of target C1, C2, C3 three classes, f1,f2,…,fDWith h1,h2,…,hDArbitrary Term meet | 2fj-hm|≤T3, then its belong to
In classification C1, and the corresponding heart rate spectrum peak positions of classification C1 are fj, wherein j ∈ { 1,2 ..., D }, m ∈ { 1,2 ..., D } are preset
The usual value range of threshold value T3 is 0~2,2) the present embodiment is taken as；If in the presence of | fj- Prev |≤T4, then its belong to classification C2,
And the corresponding heart rate spectrum peak positions of classification C2 are fj, the usual value range of wherein j ∈ { 1,2 ..., D }, predetermined threshold value T4 are 0
~3, the present embodiment is taken as 2；Otherwise it belongs to classification C3, and the corresponding heart rate spectrum peak positions of classification C3 are Prev.
In classification and orientation method, pulse wave signal s is extractedcFeature vector include：Pulse wave signal skBelieve with noise reference
Related coefficient, pulse wave signal s number under time domain, frequency domainkIn spectral range R0Interior notable spectral peak number, f1,f2With p1,p2
Between with the presence or absence of harmonic wave to, f1,f2、p1,p2With the respective differences of Prev, wherein notable wave crest refer to peak value be more than predetermined threshold value
Wave crest.
After obtaining heart rate spectrum peak position (the corresponding horizontal axis index value of spectral peak) of current time window, rate calculation unit is again
According toIt calculates heart rate value and is sent to output display unit, to realize display testing result, wherein
The unit of heart rate value be under/minute, fsIndicate corresponding pulse wave signal scFrequency spectrum frequency range, N indicate Fourier transformation
(pulse wave signal scTransformation from time-frequency to frequency domain) points, i.e. fs/ N is the frequency values of unit coordinate points, and Loc refers to root
According to the non-linear positioning mode heart rate spectrum peak position (horizontal axis value) that either classification determines, because the start mark of Loc is 1, therefore
Loc-1 is needed when calculating.
The above description is merely a specific embodiment, any feature disclosed in this specification, except non-specifically
Narration, can be replaced by other alternative features that are equivalent or have similar purpose；Disclosed all features or all sides
Method or in the process the step of, other than mutually exclusive feature and/or step, can be combined in any way.
Claims (10)
1. the heart rate method of estimation for wearable heart rate monitor apparatus, which is characterized in that include the following steps：
To the original of original pulse wave signal, motion sensor acquisition under the motion state of photoplethysmographic sensor acquisition
Beginning motor message divides time window and carries out heart rate estimation to each time window：
Step 1：Bandpass filtering treatment is carried out to original pulse wave signal, the original motion signal of current time window, obtains pulse
Wave signal s0And noise reference signal；
Step 2：Pulse wave signal s is obtained using nonlinear adaptable filter0With the non-linear relation of noise reference signal, obtain
Estimate signal to noise；Estimate signal to pulse wave signal s based on noise0It is filtered to obtain pulse wave signal sk；
Step 3：To pulse wave signal skIt extracts characteristic information and binary decision method based on classification is by pulse wave signal skIt is divided into
Totally with unclean two class；
It is clean pulse wave signal s for classificationk, then directly as pulse wave signal sc；
It is sordid pulse wave signal s for classificationk, then it is based on noise reference signal, arteries and veins is removed using singular spectrum analysis method
Fight wave signal skIn noise jamming, obtain pulse wave signal sc；
Step 4：Obtain pulse wave signal scFrequency spectrum, be denoted as the first frequency spectrum；
To pulse wave signal scAfter carrying out Nonlinear Processing, its frequency spectrum is obtained, the second frequency spectrum is denoted as；
The first frequency spectrum, the second frequency spectrum are obtained in default base frequency range R0Preceding D highest spectral peak, wherein the preceding D of corresponding first frequency spectrum
The spectrum peak position of a highest spectral peak is denoted as f1,f2,…,fD, the spectrum peak position of the preceding D highest spectral peak of corresponding second frequency spectrum is denoted as
p1,p2,…,pD；
The heart rate spectral peak of current time window is positioned based on non-linear positioning mode, if cannot position, then is positioned based on classification and orientation method；
Wherein, non-linear positioning mode is：Search f1,f2,…,fDWith p1,p2,…,pDIn there are differences to be less than or equal to default threshold
The spectrum peak position f of value T1i, and fiIt is less than or equal to predetermined threshold value T2 with the difference of Prev, then spectrum peak position fiFor current time window
Heart rate spectrum peak position, wherein i ∈ { 1,2 ..., D }, Prev indicate the spectrum peak position of the heart rate spectral peak of upper time window determination,
The initial value of Prev is the pulse wave signal s of original time windowcFrequency spectrum top spectrum peak position；
Classification and orientation method is：By the pulse wave signal s of time windowscAs the training sample of grader, pulse wave signal is extracted
scCharacteristic information build grader, and the heart rate spectrum peak position of specified different classifications result；Extract the pulse of current time window
Wave signal scCharacteristic information and input grader and carry out classification judgement, determined based on the corresponding heart rate spectrum peak position of current class
The heart rate spectrum peak position of current time window；
Step 5：Heart rate spectrum peak position based on time window calculates heart rate value.
2. the method as described in claim 1, which is characterized in that in the classification and orientation method of step 4, the grader of structure includes
Class object has C1, C2, C3 three classes：
Judge spectrum peak position f1,f2,…,fDWith the first frequency spectrum in default harmonic frequency range R1Preceding D highest spectral peak spectrum peak position
h1,h2,…,hDWith the presence or absence of harmonic wave pair, that is, judge whether to meet | 2fj-hm|≤T3, if so, the pulse wave of current time window
Signal scBelong to classification C1, and the corresponding heart rate spectrum peak positions of classification C1 are fj, wherein j ∈ { 1,2 ..., D }, m ∈ 1,2 ...,
D }, T3 is predetermined threshold value；
Otherwise continue to judge spectrum peak position f1,f2,…,fDIn whether there is spectral peak fjMeet | fj- Prev |≤T4, if so, working as
The pulse wave signal s of preceding time windowcBelong to classification C2, and the corresponding heart rate spectrum peak positions of classification C2 are fj, wherein j ∈ 1,
2 ..., D }, T4 is predetermined threshold value；Otherwise the pulse wave signal s of current time windowcBelong to classification C3, and the corresponding hearts of classification C3
Rate spectrum peak position is Prev.
3. method as claimed in claim 2, which is characterized in that in step 4, base frequency range R0For [Prev- Δs, Prev+ Δs],
Harmonic frequency range R1For：[2 (Prev- Δs -1)+1,2 (Prev+ Δs -1)+1], wherein Δ indicate parameter preset.
4. the method as described in claim 1, which is characterized in that in step 3, be based on noise reference signal, use singular spectrum point
Analysis method removes pulse wave signal skIn noise jamming, obtain pulse wave signal scSpecially：
Based on singular value decomposition by pulse wave signal skIt is decomposed into some time sequence, while calculating the frequency spectrum of each time series simultaneously
Count major frequency components；
It calculates the frequency spectrum of noise reference signal and counts major frequency components；
Each time series is judged successively, if the major frequency components of the major frequency components of time series and noise reference signal
Overlapping, then delete current time sequence, the time series of reservation be reconstructed to obtain pulse wave signal sc。
5. method as claimed in claim 2, which is characterized in that in step 4, extract pulse wave signal scCharacteristic information include：
Pulse wave signal scWith Pearson correlation coefficient, pulse wave signal s of the noise reference signal under time domain, frequency domaincIn fundamental frequency
Range R0Interior notable spectral peak number, f1~fDWith h1~hDBetween with the presence or absence of harmonic wave to, f1~fD、h1~hDRespectively with Prev
Difference, wherein notable spectral peak refer to peak value be more than predetermined threshold value spectral peak.
6. the method as described in claim 1, which is characterized in that use time window length for 4~8 seconds, 1~2 is divided between sliding
The time slip-window of second carries out time window division to the original motion signal of original pulse wave signal, motion sensor acquisition.
7. the method as described in claim 1, which is characterized in that in step 1, the frequency range of bandpass filtering treatment is：0.4Hz
~5Hz.
8. the method as described in claim 1, which is characterized in that the original motion signal by motion sensor acquisition is three axis
Acceleration signal.
9. the method as described in claim 1, which is characterized in that in step 3, to pulse wave signal skExtracting characteristic information includes
Temporal signatures, frequency domain character and small echo characteristic of field：
Temporal signatures include：Pulse wave signal skEnergy, mean value, variance；
Frequency domain character includes：Pulse wave signal skFrequency spectrum mean value, variance, notable spectral peak quantity, pulse wave signal skFrequency spectrum
With pulse wave signal s0Frequency spectrum related coefficient, pulse wave signal skFrequency spectrum and noise reference signal frequency spectrum phase relation
Number, wherein notable spectral peak refers to the spectral peak that peak value is more than predetermined threshold value；
Small echo characteristic of field includes：Pulse wave signal skThe energy of each subband signal, mean value, variance after wavelet decomposition.
10. a kind of wearable heart rate monitor apparatus, which is characterized in that including signal gathering unit, Signal Pretreatment unit, signal
Denoising unit, rate calculation unit and output unit；
Wherein signal gathering unit includes photoplethysmographic sensor and motion sensor, is being moved for acquiring measured
Original pulse wave signal, original motion signal under state are simultaneously transferred to Signal Pretreatment unit；
Signal Pretreatment unit carries out time window division to input signal and carries out bandpass filtering treatment, defeated to signal denoising unit
Enter pulse wave signal s0And noise reference signal；
Signal denoising unit captures pulse wave signal s by nonlinear adaptable filter0With the nonlinear dependence of noise reference signal
System obtains noise estimation signal；Being based on noise again estimates signal to pulse wave signal s0It is filtered to obtain pulse wave letter
Number sk；And pulse wave signal s is adjudicated using the binary decision method of classification by decision unitkWhether it is clean, if so,
Directly by pulse wave signal skAs pulse wave signal scAnd input rate calculation unit；Otherwise it is based on noise reference signal, is used
Singular spectrum analysis method removes pulse wave signal skIn noise jamming, obtain pulse wave signal scInput rate calculation list again afterwards
Member；
Rate calculation unit：As unit of time window, in conjunction with non-linear positioning mode and classification and orientation method to the arteries and veins of each time window
Fight wave signal scFrequency spectrum carry out heart rate spectral peak tracking, position the heart rate spectral peak of each time window, and based on each time window
Heart rate spectrum peak position calculates the heart rate value of current time window and is sent to output unit；
Wherein, the pulse wave signal s of each time window is positionedcHeart rate spectral peak be specially：
Obtain pulse wave signal scFrequency spectrum, be denoted as the first frequency spectrum；To pulse wave signal scAfter carrying out Nonlinear Processing, then obtain
Its frequency spectrum is denoted as the second frequency spectrum；The first frequency spectrum, the second frequency spectrum are obtained in default base frequency range R0Interior preceding D highest spectral peak,
The spectrum peak position of the preceding D highest spectral peak of the first frequency spectrum of middle correspondence is denoted as f1,f2,…,fD, the preceding D highest of corresponding second frequency spectrum
The spectrum peak position of spectral peak is denoted as p1,p2,…,pD；
The heart rate spectral peak of current time window is positioned based on non-linear positioning mode, if cannot position, then is positioned based on classification and orientation method；
The non-linear positioning mode is：Search f1,f2,…,fDWith p1,p2,…,pDIn be less than or equal to the presence or absence of difference it is default
The spectrum peak position f of threshold value T1i, and fiIt is less than or equal to predetermined threshold value T2 with the difference of Prev, if it is present spectrum peak position fi
For the heart rate spectrum peak position of current time window, wherein i ∈ { 1,2 ..., D }, Prev indicates the heart rate spectral peak that a upper time window determines
Spectrum peak position, the initial value of Prev is the pulse wave signal s of original time windowcFrequency spectrum top spectrum peak position；
The classification and orientation method is：By the pulse wave signal s of time windowscAs the training sample of grader, pulse wave is extracted
Signal scCharacteristic information build grader, and the heart rate spectrum peak position of specified different classifications result；Extract current time window
Pulse wave signal scCharacteristic information and input grader and carry out classification judgement, be based on the corresponding heart rate spectrum peak position of current class
Determine the heart rate spectrum peak position of current time window；
Output unit：As unit of time window, the heart rate value of real-time display current time window.
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