CN106419884B  A kind of rate calculation method and system based on wavelet analysis  Google Patents
A kind of rate calculation method and system based on wavelet analysis Download PDFInfo
 Publication number
 CN106419884B CN106419884B CN201610839541.2A CN201610839541A CN106419884B CN 106419884 B CN106419884 B CN 106419884B CN 201610839541 A CN201610839541 A CN 201610839541A CN 106419884 B CN106419884 B CN 106419884B
 Authority
 CN
 China
 Prior art keywords
 motion state
 value
 wavelet
 wave crest
 heart rate
 Prior art date
Links
 238000004364 calculation methods Methods 0 abstract claims description title 30
 238000004458 analytical methods Methods 0 abstract claims description title 25
 210000002216 Heart Anatomy 0 abstract claims description 50
 230000001133 acceleration Effects 0 abstract claims description 38
 238000000354 decomposition Methods 0 abstract claims description 37
 238000001914 filtration Methods 0 claims description 10
 238000005070 sampling Methods 0 claims description 6
 230000003068 static Effects 0 claims description 6
 239000010410 layers Substances 0 claims description 4
 238000002592 echocardiography Methods 0 claims description 3
 238000007619 statistical methods Methods 0 claims description 3
 230000000875 corresponding Effects 0 description 2
 238000000034 methods Methods 0 description 2
 230000003935 attention Effects 0 description 1
 238000003745 diagnosis Methods 0 description 1
 230000000694 effects Effects 0 description 1
 230000036541 health Effects 0 description 1
 239000010912 leaf Substances 0 description 1
 238000006011 modification Methods 0 description 1
 230000004048 modification Effects 0 description 1
 238000002715 modification method Methods 0 description 1
 230000004224 protection Effects 0 description 1
Abstract
Description
Technical field
The present invention relates to the technical fields of information processing, more particularly, to a kind of rate calculation method based on wavelet analysis With system.
Background technique
In recent years, with the continuous improvement of people's living standards, national health receives more and more attention, Neng Gousui The intelligent wearable device of body measurement physiological parameter will be following research direction.
Heart rate refers to the number of heartbeat in the unit time.In human parameters detection, heart rate is an important life Index is managed, provides reference for medical diagnosis.Meanwhile heart rate also can be used as the objective evaluation index of human motion physiological stress.
Occur many bracelets that heart rate is calculated using photoelectric sensor on the market at present.But this bracelet is transported in user Sensor received signal has interference when dynamic, and rate calculation result has relatively large deviation.When especially body part moves, such as brush Tooth is shaken the hand, and the heart rate end value that bracelet measures is usually more higher than actual value.Patent CN103767696A passes through analysis extreme point Calculate heart rate, but this rate calculation method is easy the interference by local motion.Patent CN101176662A calculates pole After value point, the highest heart rate value of gained vote is exported using the method for majority ballot, and method needs the sampling of long period Exact value could be obtained.
Summary of the invention
Technical problem to be solved by the present invention lies in provide a kind of can accurately calculate heart rate under different motion states Method and system, be interfered the heart rate for causing to measure with solving photoelectric sensor received signal existing in the prior art As a result false problem.
To achieve the above object, the present invention provides the following technical scheme that
A kind of rate calculation method based on wavelet analysis, comprising the following steps:
S1. distinguish the acceleration signal and photosignal of acquisition units time；
S2. the statistical characteristics and wave crest characteristic value for analyzing acceleration signal, static to the motion state progress of user, The classification of motions of local motion and mass motion obtains the first motion state；
S3. N rank wavelet decomposition is carried out to photosignal, obtains N rank coefficient of wavelet decomposition deccoeff；In conjunction with the of S2 One motion state carries out denoising to N rank coefficient of wavelet decomposition deccoeff, carries out wavelet reconstruction later, obtains that treated Signal reccoeff；This, N=6.
S4. the extreme point of treated signal reccoeff is positioned, the first heart rate value is calculated.
When due to only distinguishing local motion and mass motion with acceleration signal, some local motions can be divided into whole In body movement.In order to reduce the erroneous judgement of motion state, further, the rate calculation method further includes whole to being mistaken for The amendment of first heart rate value of the local motion state of body movement:
If obtained first motion state of S2 is mass motion state, first obtained after executing step S3 and S4 Heart rate value is less than the heart rate threshold set, then the first motion state is modified to local motion state, and execute step again The operation of S3 and S4, and the first heart rate value is modified, obtain the second heart rate value.Second heart rate value is obtained after being modified , by wavelet decomposition twice and wavelet reconstruction, improve the accuracy in computation of heart rate value.
As further describing for technical solution of the present invention, the classification of motions of the step S2 is specifically included following Step:
(1) statistical characteristics of acceleration signal: mean value and variance is calculated；To the wave crest of acceleration signal into positioning, meter Calculation obtains wave crest characteristic value: the distance between wave crest number, the height of each wave crest, adjacent peaks, and average wave crest is calculated Height and average peak away from；
(2) according to the statistical characteristics of acceleration signal and wave crest characteristic value, static, office is carried out to the motion state of user The classification of motions of portion movement and mass motion, obtains the first motion state:
A. if mean value is less than mean value threshold value, variance is less than variance threshold values, and wave crest number is less than the first threshold of wave crest number Value, then the first motion state is set as stationary state；
B. if wave crest number is greater than wave crest number second threshold, average crest height is greater than the threshold of average crest height Value, average peak away from be less than average peak away from threshold value, then the first motion state is set as local motion state；By comparing wave crest Number, average crest height peace peak away from corresponding threshold value, the highfrequency movement such as can will shake hand, brush teeth and being correctly divided into In local motion.
C. in addition to a and b, remaining first motion state is set as mass motion state.
As further describing for technical solution of the present invention, the denoising of the step S3 specifically includes following step It is rapid:
(1) mean μ of ith layer of coefficient of wavelet decomposition is calculated_{i}And variances sigma_{i},
(2) according to obtained first motion state of S2, the λ value of denoising function is determined；
A. when the first motion state is stationary state, λ=σ_{i}；
B. when the first motion state is local motion state, λ=μ_{i}；
C. when the first motion state is mass motion state, λ=μ_{i}pσ_{i}, the p value is 0.1 or 0.2 or 0.3；
(3) selection denoising function softthreshold function or hard threshold function, and according to the λ value of the denoising function determined, it is right Coefficient of wavelet decomposition is denoised,
Softthreshold function are as follows:；
Hard threshold function are as follows:。
The present invention also provides a kind of rate calculation systems, and the system is using as described above based on the heart rate of wavelet analysis Calculation method specifically includes:
(1) acceleration of acquisition units time signal acquisition module: is distinguished by acceleration transducer and photoelectric sensor Signal and photosignal.
(2) acceleration signal analysis module: it is for statistical analysis to collected acceleration signal, obtain mean value and variance Statistical characteristics；To the wave crest of acceleration signal into positioning, wave crest characteristic value: the height of wave crest number, each wave crest is calculated Degree, the distance between adjacent peaks, and be calculated average crest height peace peak away from.
(3) photosignal wavelet decomposition module: being decomposed into multilayer by waveletdecomposing method to collected photosignal, After low frequency resolution filter is to original signal data filtering processing, then carry out downsampled obtaining approximation coefficient；In highfrequency decomposition After original signal data is filtered in filter, then carry out downsampled obtaining detail coefficients.
Denoising module: the λ value of the denoising function determined, selection denoising function carry out at denoising coefficient of wavelet decomposition Reason.
(4) wavelet reconstruction processing module: including lowfrequency reconfiguration filter and high frequency reconstruction filter；Approximation coefficient is adopted upper It after sample, is filtered by lowfrequency reconfiguration filter, obtains low frequency filtering result；Detail coefficients after upsampling, It is filtered by high frequency reconstruction filter, obtains High frequency filter result；Abovementioned low frequency filtering result and high frequency are filtered Wave results added has then obtained wavelet reconstruction result.
Based on abovementioned technical solution, technical effect that the present invention obtains are as follows:
(1) rate calculation method provided by the invention is right according to the statistical characteristics of acceleration signal and wave crest characteristic value Motion state is classified；Treated that photosignal combination motion state carries out small echo weight after denoising for wavelet decomposition Structure, the heart rate result precision being calculated is high, stability is good.Meanwhile in the case where user is in different motion state environment, fortune It may determine that motion state locating for user with the system of rate calculation method of the invention, and then further calculate heart rate value, The heart rate result reliability being calculated is good.
(2) in addition, in order to prevent to the erroneous judgement of motion state, the present invention by combine motion state, be calculated the One heart rate value and the heart rate threshold set, the amendment to the first heart rate value of the local motion state for being mistaken for mass motion, The confidence level that general warranty heart rate value calculates.
Detailed description of the invention
Fig. 1 is the flow diagram of the rate calculation method of the embodiment of the present invention.
Fig. 2 is the amendment of first heart rate value to the local motion state for being mistaken for mass motion of the embodiment of the present invention Flow diagram.
Specific embodiment
The invention will be further described with specific embodiment with reference to the accompanying drawing, but does not limit the scope of the invention.
Embodiment 1
As shown in Figure 1, giving a kind of rate calculation method based on wavelet analysis in the present embodiment.Wavelet analysis is Fu In leaf analysis one kind, Fourier analysis is the translation by function cosine function and stretches to indicate, and wavelet analysis It is to be indicated with generating function by translating and stretching.
With reference to the flow diagram of Fig. 1, this rate calculation method specifically includes the following steps: signal data acquisition 100, The acceleration signal and photosignal of acquisition units time respectively；Acceleration signal 211 is analyzed, this time includes to acceleration signal Statistical characteristics and wave crest characteristic value analyzed and calculated, so that next motion state to user carries out static, office The classification of motions of portion movement and mass motion, obtains the first motion state 212；Simultaneously, it is also desirable to photosignal is handled, Principle based on wavelet analysis this time carries out N rank wavelet decomposition 221 to photosignal, has obtained N rank coefficient of wavelet decomposition deccoeff 222；After having respectively obtained abovementioned the first motion state and N rank coefficient of wavelet decomposition deccoeff, the two knot Altogether so as to the progress denoising 300 of N rank coefficient of wavelet decomposition, in the present embodiment, N=6.Wavelet reconstruction is executed later, The signal reccoeff that obtains that treated, and positioning 400 is carried out to the extreme point of treated signal reccoeff, it is calculated First heart rate value 500.
In the present embodiment, acceleration signal 211 is analyzed in order to better understand and classification obtains the first motion state 212 Specific steps are this time described classification of motions:
The statistical characteristics for calculating acceleration signal, such as mean value and variance；Meanwhile to the wave crest of acceleration signal into calmly Position, is calculated wave crest characteristic value, such as the distance between wave crest number, the height of each wave crest, adjacent peaks, further calculates To average crest height peace peak away from；According to the statistical characteristics of acceleration signal and wave crest characteristic value, to needing to carry out the heart The motion state of the user of rate measurement carries out the classification of motions of static, local motion and mass motion, obtains the first motion state. Classification method is as follows:
If mean value is less than mean value threshold value, variance is less than variance threshold values, and wave crest number is less than wave crest number first threshold, then First motion state is set as stationary state；
If wave crest number is greater than wave crest number second threshold, average crest height is greater than the threshold value of average crest height, Average peak away from be less than average peak away from threshold value, then the first motion state is set as local motion state；By comparing wave crest number, Average crest height peace peak away from corresponding threshold value, the highfrequency movement such as can will shake hand, brush teeth and being correctly divided into office In portion's movement.
In addition to above two situation, remaining first motion state is set as mass motion state.
In the present embodiment, as further describing for 300 step of denoising, the step for specifically include:
By taking ith layer of coefficient of wavelet decomposition as an example, the mean μ of ith layer of coefficient of wavelet decomposition is calculated separately_{i}And variances sigma_{i}；According to The first obtained motion state 212 determines the λ value of denoising function；
When the first motion state is stationary state, λ=σ_{i}；When the first motion state is local motion state, λ=μ_{i}；When When first motion state is mass motion state, λ=μ_{i}pσ_{i}, p value can be 0.1 or 0.2 or 0.3, can in debugging process To be tested p value to obtain suitable debugging value；
As needed, the suitable denoising function of selection such as softthreshold function or hard threshold function, and is gone according to what is determined It makes an uproar the λ value of function, coefficient of wavelet decomposition is denoised,
Softthreshold function are as follows:；
Hard threshold function are as follows:。
Rate calculation method provided by the invention based on wavelet analysis, according to the statistical characteristics and wave of acceleration signal Peak characteristic value, classifies to motion state；Wavelet decomposition treated photosignal combination motion state passes through denoising After carry out wavelet reconstruction, the heart rate result precision being calculated is high, stability is good.
Embodiment 2
As shown in Fig. 2, when due to only distinguishing local motion and mass motion with acceleration signal, some local motion meetings It is divided into mass motion.It is in this embodiment, whole to being mistaken in rate calculation method in order to reduce the erroneous judgement of motion state The amendment of first heart rate value of the local motion state of body movement.With reference to Fig. 2 to the local motion shape for being mistaken for mass motion The modified flow diagram of first heart rate value of state, specific modification method are as follows: if it is obtained to classify in motion state First motion state is mass motion state 21, and the first heart rate value obtained after executing denoising and wavelet reconstruction step is small In the heart rate threshold 22 set, the heart rate threshold of the present embodiment is a threshold value being set in advance, usually an experience Value.In this way, the first motion state is then modified to local motion state 30, and executes again and coefficient of wavelet decomposition is denoised The operation of processing 40 and 50 step of wavelet reconstruction, and the first heart rate value is modified, obtain the second heart rate value 60.Second heart rate Value obtains after being modified, and by wavelet decomposition twice and wavelet reconstruction, improves the accuracy in computation of heart rate value.
Embodiment 3
Rate calculation method based on motion state classification, wavelet decomposition and wavelet reconstruction, present embodiments provides one kind It is small to specifically include signal acquisition module, acceleration signal analysis module, photosignal for rate calculation system based on wavelet analysis Wave Decomposition module, denoising module and wavelet reconstruction processing module.Wherein, signal acquisition module passes through acceleration transducer and photoelectricity The acceleration signal and photosignal of sensor difference acquisition units time.In acceleration signal analysis module, to collected Acceleration signal is for statistical analysis, obtains the statistical characteristics of mean value and variance；To the wave crest of acceleration signal into positioning, meter Calculation obtains wave crest characteristic value, including the distance between wave crest number, the height of each wave crest, adjacent peaks, and is calculated average Crest height and average peak away from.In photosignal wavelet decomposition module, wavelet decomposition side is passed through to collected photosignal Method is decomposed into multilayer, after low frequency resolution filter is to original signal data filtering processing, then carries out downsampled obtaining approximate system Number；After highfrequency decomposition filter is to original signal data filtering processing, then carry out downsampled obtaining detail coefficients.Denoise mould Block, then after the λ value of denoising function has determined, selection denoising function carries out denoising to coefficient of wavelet decomposition.Small echo Reconstruction processing module, including lowfrequency reconfiguration filter and high frequency reconstruction filter；Approximation coefficient passes through low frequency after upsampling Reconfigurable filter is filtered, and obtains low frequency filtering result；Detail coefficients are filtered after upsampling by high frequency reconstruction Device is filtered, and obtains High frequency filter result；Abovementioned low frequency filtering result and High frequency filter results added are then obtained Wavelet reconstruction result.
In the case where user is in different motion state environment, use may determine that with the system of the rate calculation of the present embodiment Motion state locating for family, and then heart rate value is further calculated, the heart rate result reliability being calculated in this way is good.
The above content is only method and system example and explanation of the invention, and description is more specific and detailed Carefully, but it cannot be understood as limitations on the scope of the patent of the present invention.It should be pointed out that for the common skill of this field For art personnel, without departing from the inventive concept of the premise, various modifications and improvements can be made, these are obvious Alternative forms all belong to the scope of protection of the present invention.
Claims (5)
Priority Applications (1)
Application Number  Priority Date  Filing Date  Title 

CN201610839541.2A CN106419884B (en)  20160922  20160922  A kind of rate calculation method and system based on wavelet analysis 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

CN201610839541.2A CN106419884B (en)  20160922  20160922  A kind of rate calculation method and system based on wavelet analysis 
Publications (2)
Publication Number  Publication Date 

CN106419884A CN106419884A (en)  20170222 
CN106419884B true CN106419884B (en)  20190702 
Family
ID=58166801
Family Applications (1)
Application Number  Title  Priority Date  Filing Date 

CN201610839541.2A CN106419884B (en)  20160922  20160922  A kind of rate calculation method and system based on wavelet analysis 
Country Status (1)
Country  Link 

CN (1)  CN106419884B (en) 
Citations (2)
Publication number  Priority date  Publication date  Assignee  Title 

CN105249951A (en) *  20150917  20160120  深圳市和虎科技有限公司  Ultralow power consumption exercise heart rate detection wireless module 
CN105326494A (en) *  20151125  20160217  山东师范大学  GSMbased human body remote blood oxygen heart rate monitoring system and method 
Family Cites Families (4)
Publication number  Priority date  Publication date  Assignee  Title 

CN102626310A (en) *  20120423  20120808  天津工业大学  Electrocardiogram signal feature detection algorithm based on wavelet transformation lifting and approximate envelope improving 
CN104665794B (en) *  20131129  20180612  深圳迈瑞生物医疗电子股份有限公司  Blood pressure detecting signal correction method and blood pressure detector 
CN103767710B (en) *  20131231  20151230  歌尔声学股份有限公司  Body movement status monitoring method and apparatus 
TWI538660B (en) *  20140926  20160621  Pixart Imaging Inc  Heartbeat detection module and detection, noise removal method 

2016
 20160922 CN CN201610839541.2A patent/CN106419884B/en active IP Right Grant
Patent Citations (2)
Publication number  Priority date  Publication date  Assignee  Title 

CN105249951A (en) *  20150917  20160120  深圳市和虎科技有限公司  Ultralow power consumption exercise heart rate detection wireless module 
CN105326494A (en) *  20151125  20160217  山东师范大学  GSMbased human body remote blood oxygen heart rate monitoring system and method 
Also Published As
Publication number  Publication date 

CN106419884A (en)  20170222 
Similar Documents
Publication  Publication Date  Title 

Cazelles et al.  Wavelet analysis of ecological time series  
ES2659945T3 (en)  Waste based monitoring of human health  
JP5478017B2 (en)  Method for detecting and discriminating respiratory patterns from respiratory signals  
Yan et al.  Reduction of motion artifact in pulse oximetry by smoothed pseudo WignerVille distribution  
Subha et al.  EEG signal analysis: a survey  
EP1590037B1 (en)  Online source reconstruction for eeg/meg and ecg/mcg  
Chowdhury et al.  Surface electromyography signal processing and classification techniques  
Tsanas et al.  Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests  
Hasan et al.  Detection and processing techniques of FECG signal for fetal monitoring  
Fojt et al.  Applying nonlinear dynamics to ECG signal processing  
Thomas et al.  Automatic ECG arrhythmia classification using dual tree complex wavelet based features  
Papadaniil et al.  Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features  
CN1718160A (en)  Sleep state estimating device, program and product  
US20070260151A1 (en)  Method and device for filtering, segmenting, compressing and classifying oscillatory signals  
Jayachandran  Analysis of myocardial infarction using discrete wavelet transform  
Kachuee et al.  Cuffless blood pressure estimation algorithms for continuous healthcare monitoring  
Sayadi et al.  Modelbased fiducial points extraction for baseline wandered electrocardiograms  
WO2003055395A1 (en)  Analysis of acoustic medical signals  
Harper et al.  Time series analysis and sleep research  
JP3923035B2 (en)  Biological condition analysis apparatus and biological condition analysis method  
CN102973253B (en)  Method and system for monitoring human physiological indexes by using visual information  
QuicenoManrique et al.  Selection of dynamic features based on time–frequency representations for heart murmur detection from phonocardiographic signals  
Hively et al.  Timely detection of dynamical change in scalp EEG signals  
Salehizadeh et al.  A novel timevarying spectral filtering algorithm for reconstruction of motion artifact corrupted heart rate signals during intense physical activities using a wearable photoplethysmogram sensor  
Güler et al.  A modified mixture of experts network structure for ECG beats classification with diverse features 
Legal Events
Date  Code  Title  Description 

C06  Publication  
C10  Entry into substantive examination  
GR01  Patent grant 