CN110025296A - A kind of acquisition method of the characteristic parameter of photoplethysmographic - Google Patents
A kind of acquisition method of the characteristic parameter of photoplethysmographic Download PDFInfo
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- CN110025296A CN110025296A CN201910154992.6A CN201910154992A CN110025296A CN 110025296 A CN110025296 A CN 110025296A CN 201910154992 A CN201910154992 A CN 201910154992A CN 110025296 A CN110025296 A CN 110025296A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
Abstract
A kind of acquisition method of the characteristic parameter of photoplethysmographic, comprising the following steps: the 1) photoplethysmographic signal for acquiring finger end sends host computer to after amplification to photoplethysmographic signal low-pass filtering；2) pulse wave signal of one of them cardiac cycle is filtered and obtained to collected signal using digital filter；3) pulse waveform fitting is carried out using 3 Gaussian term superpositions；4) coefficient qualifications are set to fitting function；5) according to least square principle, sum of squared errors function is listed to solve the coefficient of the Gaussian function；6) partial derivative is asked to each term coefficient in formula (3)；7) equation group is iteratively solved using the method for BFGS；8) position of pulse wave characteristic value is determined according to each translational component and intersection point information of Gaussian function；Solves the problems, such as the determination of situation lower eigenvalue unconspicuous for pulse waveform physiological characteristic position.
Description
Technical field
The invention belongs to bio signal processing technology field more particularly to a kind of characteristic parameters of photoplethysmographic
Acquisition method.
Background technique
Pulse waveform describes the process that intermittence relevant to heart beat cycles penetrates blood, and generation is due to heart
There are the contraction and diastole of rhythmicity, so that endaortic blood pressure generates pulsatile change, and is successively conducted into entire
Artery piping.Currently used pulse wave detecting method has photoplethysmographic graphical method and pressure pulse wave graphical method.It
Be the nothing that volumetric blood and pressure change in tissue are detected based on Photoelectric Detection means and pressure sensing means respectively
Create detection method.
The wave character of pulse wave can reflect physiological pathology of human body information abundant.And the method for Photoelectric Detection not only has
There is preferable sensitivity, while also overcoming pressure sensor to body measurements bring constriction, can be used to arteries and veins
Wave signal of fighting carries out continual monitoring for a long time.And using pulse wave carry out human body physical sign parameter monitoring when, need using
The characteristic parameter that pulse beating characteristic is able to reflect in pulse waveform is modeled, and therefore, accurately extracts feature locations pair
The accuracy of measurement model has important influence.
Currently, common pulse wave feature locations discriminating method have difference threshold algorithm, syntax pattern distinguishment method, curvature method,
EMD decomposition method, wavelet modulus maxima method etc..Mathematically, based on the method for Local Extremum to one group of signal
Calculus of differences is carried out, these feature locations points have corresponded to the zero crossing of first-order difference.But found in specific practice, in the heart
Dirty to penetrate the stopping of blood phase, the movement that blood flows back to ventricle in blood vessel generates pulse wave tidal wave and shows in waveform diagram it is failing edge song
The variation of rate, and might not have Local Extremum herein, therefore be difficult to determine its position with the zero crossing of first-order difference data
It sets, while then largely family increases the complexity of algorithm using the method successively decomposed.This feature point simultaneously
The error for setting examination is the important sources for influencing physiological parameter modeling accuracy.
Summary of the invention
To overcome above-mentioned the deficiencies in the prior art, the purpose of the present invention is to propose to a kind of feature of photoplethysmographic ginsengs
Several acquisition methods is solved for pulse waveform physiological characteristic position not based on the method for Gaussian function fitting pulse wave
The determination problem of apparent situation lower eigenvalue.
To achieve the above object, the technical solution adopted by the present invention is that: a kind of characteristic parameter of photoplethysmographic
Acquisition method, comprising the following steps:
Step 1, the principle based on lambert's beer's law is irradiated human body finger tip using the light source of infrared band, is visited by photoelectricity
Survey device acquisition of transmission and cross the optical signal of finger tip, to the photoplethysmographic signal containing high-frequency noise using analog filter into
Row low-pass filtering, and amplify collected photoplethysmographic signal by amplifying circuit, then carry out A/D to it and turn
Host computer is transferred data to after changing；
Step 2, it is brought in photoplethysmographic signal by capture card, human body respiration, shake using digital filter
Noise be filtered again, to obtain better signal-to-noise ratio, photoplethysmographic signal after being denoised, and using
Difference threshold algorithm obtains the pulse wave signal of one of them cardiac cycle；
Step 3, pulse waveform fitting is carried out using 3 Gaussian function superpositions:
Wherein, ViIt respectively corresponds as the peak value of one of Gaussian function, TiOne of Gaussian function is respectively corresponded
Offset of the main peak relative to abscissa zero point, UiCharacterize the width of each Gaussian peak；
Step 4, coefficient qualifications are set:
Wherein, ai、bi、ciThe respectively qualifications of nonlinear fitting；
Step 5, it being solved according to criterion of least squares, it is desirable that the equation of fitting and the error sum of squares of original signal are minimum, and
When formula (2) obtains minimum value, V is just obtainedi、Ti、UiOptimal solution,
In formula, sig is input signal, and Q is sum of squared errors function；
Step 6, partial derivative is asked to each term coefficient in formula (3), enables:
So that the solution that partial derivative is 0: Vi, Ti, UiThen constitute the optimal solution of error sum of squares Q；
Step 7, F (V is solvedi,Ti,Ui), for nonlinear multivariable equation, its analytic solutions is hardly resulted in, therefore uses base
Nonlinear equation is iterated in newton order _ 2 iterative methods BFGS method and seeks its optimal solution,
In formula, XkConstitute equation group F (Vi,Ti,Ui) kth time iterative solution, J (Xk) be equation group (4) Jacobian
Determinant；
Step 8, the translational component in the model of Gaussian function solution in each Gauss term coefficient has then corresponded to pulse wave wave
Feature locations in shape.
Compared with prior art, the present invention has the characteristics that following:
The invention proposes a kind of new algorithm for solving characteristic parameter in pulse waveform, pulses used in the present invention
Wave waveform diagram is using the collected human body finger tip photoplethysmographic signal of the infrared pulse transducer of HKG-07B type；It utilizes
Multinomial Gaussian function fitting method approaches photoplethysmographic signal, can more effectively obtain the feature in pulse waveform figure
Position can more accurately obtain the characteristic parameter of the column pulse wave by these positions, to correspondingly improve modeling
Efficiency.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is the waveform diagram of the photoplethysmographic after denoising.
Fig. 3 is the Gaussian function exploded view of a cardiac cycle.
Fig. 4 is the result figure using Gaussian function initial fitting.
Fig. 5 is the residual plot using Gaussian function initial fitting.
Fig. 6 is the residual distribution figure of current simulation result.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.
Referring to Fig. 1, the method proposed by the invention based on Gaussian function fitting pulse wave carries out the step of Feature point recognition
It is rapid as follows:
Step 1, it is based on lambert's beer's law, human body finger tip is irradiated using the light source of infrared band, is adopted by photodetector
Collect the optical signal transmitted through finger tip, low pass is carried out using analog filter to the photoplethysmographic signal containing high-frequency noise
Filtering, and amplify collected photoplethysmographic signal by amplifying circuit, then incite somebody to action after carrying out A/D conversion to it
Data transmission is to host computer；
Step 2, using Butterworth LPF in volume pulsation wave signal by capture card, human body respiration, shake
Etc. brings noise be filtered again, judge whether its filtered signal-to-noise ratio meets the requirements, the photoelectricity after being denoised
Volume pulsation wave signal；Signal after filtering processing referring to fig. 2, obtains the pulse wave signal of one of them cardiac cycle, such as schemes
(3) shown in；
Step 3, pulse waveform fitting is carried out using 3 Gaussian function superpositions,
Wherein, ViIt respectively corresponds as the peak value of one of Gaussian function, TiOne of Gaussian function is respectively corresponded
Offset of the main peak relative to abscissa zero point, UiCharacterize the width of each Gaussian peak；
Step 4, its coefficient qualifications is set:
Wherein, ViIt respectively corresponds as the peak value of one of Gaussian function, TiOne of Gaussian function is respectively corresponded
Offset of the main peak relative to abscissa zero point, UiCharacterize the width of each Gaussian peak, ai、bi、ciRespectively Nonlinear Quasi
The qualifications of conjunction；
Step 5, it being solved according to criterion of least squares, it is desirable that the equation of fitting and the error sum of squares of original signal are minimum, and
When formula (2) obtains minimum value, V is just obtainedi、Ti、UiOptimal solution,
In formula, sig is input signal, and Q is sum of squared errors function；
Step 6, partial derivative is asked to each term coefficient in formula (2), enables:
Making partial derivative in formula is 0 solution: Vi, Ti, UiThen constitute the optimal solution of error sum of squares Q；
Step 7, for nonlinear multivariable equation, its analytic solutions is hardly resulted in, therefore uses and is based on newton order _ 2 iteration
BFGS (tetra- scholars of Broyden, Flether, Goldfarb, Shanno propose) method of method is iterated nonlinear equation and asks
Its optimal solution is taken,
Wherein, XkConstitute equation group F (Vi,Ti,Ui) kth time iterative solution, J (Xk) be equation group (4) Jacobian
Determinant, BFGS algorithm flow chart is referring to Fig. 3；
Step 8, the translational component in the model of Gaussian function solution in each Gauss term coefficient has then corresponded to pulse wave wave
The intersection point of peak position in shape, every two Gauss clock corresponds to the incisura in pulse waveform；As shown in figure 4, c ', d ', e ',
F ', g ' abscissa both corresponded to the position of pulse wave characteristic value.The preliminary simulation result for carrying out pulse waveform fitting is shown in figure
Shown in 5, Fig. 6 is the residual distribution figure of current simulation result.
Claims (1)
1. a kind of acquisition method of the characteristic parameter of photoplethysmographic, which comprises the following steps:
Step 1, the principle based on lambert's beer's law irradiates human body finger tip using the light source of infrared band, by photodetector
Acquisition of transmission crosses the optical signal of finger tip, low using analog filter progress to the photoplethysmographic signal containing high-frequency noise
Pass filter, and amplify collected photoplethysmographic signal by amplifying circuit, then after carrying out A/D conversion to it
Transfer data to host computer；
Step 2, it is made an uproar in photoplethysmographic signal by capture card, human body respiration, shake bring using digital filter
Sound is filtered again, and to obtain better signal-to-noise ratio, photoplethysmographic signal after being denoised simultaneously uses difference
Threshold method obtains the pulse wave signal of one of them cardiac cycle；
Step 3, pulse waveform fitting is carried out using 3 Gaussian function superpositions:
Wherein, ViIt respectively corresponds as the peak value of one of Gaussian function, TiThe main peak of one of Gaussian function is respectively corresponded
Relative to the offset of abscissa zero point, UiCharacterize the width of each Gaussian peak；
Step 4, coefficient qualifications are set to fitting function:
Wherein, ai、bi、ciThe respectively qualifications of nonlinear fitting；
Step 5, it is solved according to criterion of least squares, it is desirable that the equation of fitting and the error sum of squares minimum and formula (2) of original signal
When obtaining minimum value, V is just obtainedi、Ti、UiOptimal solution,
In formula, sig is input signal, and Q is sum of squared errors function；
Step 6, partial derivative is asked to each term coefficient in formula (2), enables:
So that the solution that partial derivative is 0: Vi, Ti, UiThen constitute the optimal solution of error sum of squares Q；
Step 7, F (V is solvedi,Ti,Ui), for nonlinear multivariable equation, its analytic solutions is hardly resulted in, therefore uses and is based on ox
Order _ 2 iterative methods BFGS algorithm that pauses, which is iterated nonlinear equation, seeks its optimal solution,
In formula, XkConstitute equation group F (Vi,Ti,Ui) kth time iterative solution, J (Xk) be equation group (4) Jacobian ranks
Formula；
Step 8, the translational component in the model of Gaussian function solution in each Gauss term coefficient has then corresponded in pulse waveform
Feature locations.
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Cited By (2)
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| CN111134634A (en) * | 2019-12-20 | 2020-05-12 | 西安理工大学 | Photoelectric volume pulse wave analysis processing method based on cluster analysis |
| CN114259206A (en) * | 2021-12-02 | 2022-04-01 | 萤雪科技(佛山)有限公司 | Pulse wave fitting method, system, computer equipment and readable storage medium |
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Application publication date: 20190719 |