CN104644151B - A kind of pressure pulse wave wave travel Forecasting Methodology based on photoelectricity volume pulse signal - Google Patents
A kind of pressure pulse wave wave travel Forecasting Methodology based on photoelectricity volume pulse signal Download PDFInfo
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Abstract
A kind of pressure pulse wave wave travel prediction meanss based on photoelectricity volume pulse signal, it is characterised in that：Including Waveform Input module, waveform conditioning module, waveform fitting module, waveform modular converter, and waveform output module.Wherein：Waveform conditioning module includes pretreatment circuit, it is single to clap separator and normalization circuit, waveform fitting module includes fitting function setting apparatus, waveform fitting device and waveform quality arbiter, waveform modular converter includes position setting apparatus, object function setting apparatus, characterizing population group's setting apparatus, parameter converter and waveform synthesizer.The device can have partes corporis humani position pressure pulse wave signal using partes corporis humani's position photoelectricity volume pulse signal according to the prediction of physiology statistical law, and the use scope of the device, prediction effect and stability all improve compared with existing apparatus.
Description
Technical Field
The invention relates to the technical field of medical equipment, in particular to a method for predicting pressure pulse wave waveforms of all parts based on photoplethysmography.
Background art:
the pulse wave contains abundant hemodynamic information. A large number of clinical practical results prove that the characteristics of pulse waves have close relation with cardiovascular physiological states, which are taken as the basis of clinical diagnosis and treatment. The comprehensive information of the pulse wave in terms of shape, strength, speed and rhythm reflects many physiological and pathological features of the human cardiovascular system.
In the aspect of the collection principle, the current pulse wave collection method mainly includes pressure pulse wave collection using a pressure sensor or photoplethysmography pulse wave collection using a photoelectric sensor. The intra-arterial pressure pulse wave is analyzed and researched more comprehensively, the fluid mechanics model is more definite, and the corresponding hemodynamics characteristics and cardiovascular physiological significance are more widely applied. However, the collection of the human body pressure pulse wave is very easily interfered by various aspects such as the collection position, the required operation skill requirement is high, the repeatability is lacking, and the continuous detection is inconvenient. The photoelectric plethysmography pulse wave finger-tip acquisition has better stability and repeatability, but the accuracy of the photoelectric plethysmography pulse wave finger-tip acquisition on the cardiovascular function judgment is not good enough compared with the pressure pulse wave which is researched and more perfect.
The invention content is as follows:
the existing technical scheme mainly utilizes a non-physiological parameter model such as a transfer function established by analyzing a power spectrum of a photoelectric volume signal of a collecting part and a pressure pulse wave signal corresponding to a target part, and mainly focuses on a finger tip volume pulse signal and a radial artery pressure pulse signal. Because the individual difference of the pulse signals is large, the device has poor stability and single function when being applied in a large scale, and the established model has no physiological significance and is not beneficial to the correction and the improvement of the model.
In order to solve the problems, the invention respectively provides a parameter-containing expression of the waveform of the volume pulse signal and the pressure pulse signal based on the physiological characteristics of the pulse wave, and establishes a regression equation set between the photoplethysmography expression parameters of the acquisition part and the pressure pulse waveform expression parameters of the corresponding target part by utilizing a priori statistical law. Thereby realizing the conversion of the input photoplethysmography signals into the pressure pulse wave signals of the target part. The waveform fitting method has better stability, the regression equation set has more definite physiological significance, and fine adjustment and improvement can be conveniently carried out according to different physiological states. Therefore, the problems that the predicted waveform stability is poor and the model cannot be corrected in the prior art can be solved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a pressure pulse wave propagation prediction device based on a photoplethysmography signal is characterized in that: the device comprises a signal input conditioning module, a waveform fitting module, a waveform conversion module, a waveform synthesis module and an output module.
The waveform input module receives a time domain photoplethysmographic pulse signal actually measured from a certain part of a human body.
The signal conditioning module is used for preprocessing an input time domain photoplethysmogram signal, decomposing the input time domain photoplethysmogram signal into single-beat signals corresponding to a single cardiac cycle by utilizing the prior art, and normalizing the amplitude and wavelength of each single-beat signal.
The waveform fitting module receives the normalized single-beat pulse signal and utilizes a given waveform containing a parameter expression fIFitting the pulse waveform by using a curve fitting algorithm, determining the parameter-containing expression of the waveform by adding the parameter-containing expressions respectively representing the main wave, the dicrotic wave and the reflected wave of the pulse waveform, and obtaining analytical expressions by fittingThe term parameter vector is IIAs a waveform characteristic parameter vector.
The parameter-containing expression of the photoplethysmography signal is as follows:
wherein Hin，bin，WinT is an argument and represents the number of sampling points. In the expression, the portions where n is 1, 2, and 3 correspond to the main wave, the reflected wave, and the dicrotic wave of the pulse wave waveform, respectively. Wherein HinRepresenting the amplitude of the wave, binIndicating the center position of the wave, WinThe width of the undulations.
The curve fitting process algorithm adopts a least square algorithm, limits each parameter range according to each fluctuation physiological significance, and then sets fitting initial conditions Hi1>Hi2>Hi3，bi1<bi2<bi3,Win>0. Fitting the determined characteristic parameter vector IIComprises the following steps:
II＝[Hi1,Hi2,Hi3,bi1,bi2,bi3,Wi1,Wi2,Wi3]
under the condition that the waveform contains the parameter expression, the fitting effect mainly depends on the interference degree of pulse wave acquisition, so that the coefficient R is determined by fitting2As a quantification standard for waveform quality discrimination. Fitting to determine the coefficient R2Is a commonly used calculation method for judging the similarity degree of two curves. In the invention R2Also as a collection quality discrimination parameter, corresponding to R2The single beat waveform less than a certain value is regarded as poor in collection quality and discarded.
R2The calculation formula is as follows:
wherein f isi，Respectively representing the actually measured photoplethysmogram data point, the actually measured photoplethysmogram data average value and the data point fitting expected value, wherein pl is the number of single-wave data points.
The waveform conversion module is used for grouping according to sex, age and average arterial pressure indexes of a measured person, and calculating and predicting the waveform characteristic parameters of the pressure pulse wave of the corresponding part by utilizing the actually measured photoelectric volume pulse wave characteristic parameters according to the prior statistical rule between the actually measured part of the photoelectric volume pulse wave and the waveform characteristic parameters of the part of the pressure pulse wave to be predicted under the corresponding grouping.
The method for establishing the prior statistical rule between the waveform characteristic parameters of the actual measurement part of the photoplethysmogram and the part of the pressure pulse wave needing to be predicted under the corresponding classification comprises the following steps:
(1) first, the population participating in the experiment was grouped by gender, age, Mean Arterial Pressure (MAP), with age starting at 20 years and 5 years apart. Mean arterial pressure was initiated at 70mmHg and 10mmHg intervals. The groups of people participating in the experiment were grouped. And simultaneously detecting photoelectric volume pulse waves at ear, finger and toe positions of the experimental groups respectively, and detecting pressure pulse wave signals at radial artery, brachial artery and carotid artery positions by using pressure sensors. Thereby obtaining the actually measured pressure pulse wave and the photoplethysmography pulse wave signals of different crowd characteristics.
(2) And then establishing a statistical relationship between the waveform characteristic parameters of the actually measured photoplethysmogram and the actually measured pressure pulse wave of the target part. Similar with above-mentioned photoplethysmography pulse wave characteristic extraction mode, for extracting pressure pulse wave waveform characteristic parameter, utilize pressure pulse wave expression to carry out the fitting to actual measurement pressure pulse wave waveform, pressure pulse wave contains the parameter expression and is:
the expression parameters and the definition domain are all equal to fIThe same is true. The fitting process algorithm adopts a least square algorithm, limits each parameter range according to each fluctuation physiological significance, and then sets fitting initial conditions Ho1>Ho2>Ho3，bo1<bo2<bo3,Won>0。
The characteristic parameter vector is IO＝[Ho1,Ho2,Ho3,bo1,bo2,bo3,Wo1,Wo2,Wo3]
(3) Respectively using f for the measured waveforms of different groups and different partsIAnd fOFitting the actually measured photoplethysmogram waveform and pressure pulse waveform by a waveform fitting module to obtain I corresponding to each actually measured photoplethysmogram signalIAnd I of pressure pulse signalOAnd (5) vector quantity. For each part of the pressure pulse signal IOEstablishing a multiple linear regression equation of the feature parameter vectors of the photoplethysmography signals which correspond to different groups and are acquired simultaneously for each parameter of the vector, namely:
Ho1＝TM11×Hi1+TM12×Hi2+......+TM19×Wi3+CM1
Ho2＝TM21×Hi1+TM22×Hi2+......+TM29×Wi3+CM2
Ho3＝TM31×Hi1+TM32×Hi2+......+TM39×Wi3+CM3
bo1＝TM41×Hi1+TM42×Hi2+......+TM49×Wi3+CM4
bo2＝TM51×Hi1+TM52×Hi2+......+TM59×Wi3+CM5
bo3＝TM61×Hi1+TM62×Hi2+......+TM69×Wi3+CM6
Wo1＝TM71×Hi1+TM72×Hi2+......+TM79×Wi3+CM7
Wo2＝TM81×Hi1+TM82×Hi2+......+TM89×Wi3+CM8
Wo3＝TM91×Hi1+TM92×Hi2+......+TM99×Wi3+CM9
the coefficients of the TM and CM terms are determined by a multiple linear regression equation. Finishing IOAnd establishing an equation set by each parameter, and sorting the coefficient matrix and the constant matrix to obtain TM and CM, wherein TM is a 9-order square matrix, and CM is a 9-element column vector.
According to the TM and CM matrixes obtained by using the prior probability, in the application process, the waveform characteristic parameter vector I of the collected part is usedICalculating a feature parameter vector I of the target regionO. Extracting characteristic I from the measured photoplethysmographyIThen, the feature vector I of the target portion can be obtainedOComprises the following steps:
IO＝TM×II+CM
then obtaining the target part characteristic parameter vector IOSubstituting into the corresponding part of the pressure pulse wave containing parameter expression fOAnd obtaining a corresponding pressure pulse wave analytic expression. The waveform conversion is completed.
The waveform output module outputs the aboveResult of waveform synthesis module and target part characteristic parameter vector IOAnd outputting according to a required form.
The invention has the beneficial effects that:
(1) the device can obtain pressure pulse wave signals of different parts under certain precision by only detecting the photoplethysmography signals of a single part of a human body. The operation is simple and convenient, and the waveform quality is stable. The problems that the conventional pressure pulse wave acquisition process is complex in test flow, inconvenient to use and difficult to acquire stable pulse waveforms are solved. In the practical application process, the direct acquisition times of the pressure pulse waves can be effectively reduced, the requirements on the operation skills are reduced, and the comfort degree of the testee is improved. A large number of verification experiments prove that the effect is good.
(2) The waveform fitting device based on the physiological fluctuation characteristics of the pulse wave is applied to the multi-position pulse waveform for the first time, so that the physiological change in the propagation process of the multi-position pulse waveform is analyzed. The model is convenient to adjust and correct, and a certain foundation is laid for large-scale clinical application.
Description of the drawings:
FIG. 1 is a block diagram of the present invention
FIG. 2 is a flow chart of the operation of the present invention
FIG. 3 is a schematic diagram of waveform fitting and characteristic parameters
FIG. 4 is an example of a measured pressure waveform and shows a comparison with the measured pressure waveform
FIG. 5 is a graph showing the effect of the present invention on predicting the pulse wave of radial artery pressure by using the photoplethysmography at the finger tip. R2The data is the cross validation effect between the single-beat waveform of the measured pressure pulse and the single-beat waveform of the prediction pulse, and the total experiment is 426 cases.
The specific implementation mode is as follows:
a more typical embodiment of the present invention will be described in detail below with reference to the accompanying drawings.
The invention discloses a typical application scene, which is to predict a radial artery pressure pulse wave signal by using a human finger tip photoplethysmography signal. Therefore, the data of the pulse wave of the radial artery pressure with more definite physiological significance can be obtained by utilizing the mature technology of finger tip acquisition and the high-quality waveform.
As shown in fig. 1, after the prediction process is started, the corresponding characteristic population is selected according to the gender and age of the subject and the blood pressure, and the corresponding predicted part is selected as required. For example, a female aged 56 years with a blood pressure value of 90/130mmHg, the sex, age, and blood pressure value are set in step S201, the collection site is the tip of a finger, and the target prediction site is the radial artery. In S202, according to the setting, the system automatically selects the TM and CM matrices corresponding to the population and the location according to the grouping condition of the prior statistical rule.
In step S203, the system starts to receive the measured waveform, in step S204, the input waveform is conditioned to filter the baseline and the power frequency interference, the continuous waveform is separated into discrete waveforms according to the cardiac cycle, and the waveform amplitude and the wavelength are normalized by taking the waveform as a unit, wherein the normalization of the wavelength is realized by an interpolation method.
In step S205, a curve is fitted to each of the single beat waveforms by the least square method according to the collected waveform fitting expression given in S203.
The fitting expression is:
initial conditions are set. Thereby obtaining I corresponding to each single-beat waveformIAnd calculating R2。
In this example, the waveform amplitude and wavelength are defined as 100 units respectively, then
II＝[Hi1,Hi2,Hi3,bi1,bi2,bi3,Wi1,Wi2,Wi3]＝[43,69,49,15,27,48,14,25,52]
R2>0.99
In step S206 according to R2The numerical value is subjected to waveform quality judgment, and R is subjected to2<The 0.95 waveform is discarded and the discard ratio is recorded. If the waveform is abandoned, the next beat waveform is extracted again for analysis. In the general case of R2<Waveform ratio of 0.95<When the rejection ratio is greater than 5%, the acquisition mode should be adjusted.
Step S207 for I of the quality-qualified waveform in S206IConverting to calculate the characteristic vector I of the target part waveformOAccording to the formula:
IO＝TM×II+CM
in this example, the TM and CM are both based on the pulse wave of radial artery pressure and the photoplethysmography signals of finger tip measured simultaneously before the patientiAnd foFitting, and respectively establishing corresponding regression equations according to the obtained parameters to obtain.
The calculation results show that:
IO＝[Ho1,Ho2,Ho3,bo1,bo2,bo3,Wo1,Wo2,Wo3]＝[64,71,35,14,25,49,13,22,48]
in step S208, IOSubstituting each parameter into a given target site pressure pulse wave expression, i.e.
And obtaining the target part prediction waveform expression and the main wave, the reflected wave and the dicrotic wave prediction waveforms respectively corresponding to the target part prediction waveform expression.
Step S209 outputs the waveform and the parameter in a predetermined format.
Claims (1)
1. A pressure pulse wave waveform propagation prediction method based on photoplethysmography signals is characterized by comprising the following steps: the device comprises a waveform input module, a signal conditioning module, a waveform fitting module, a waveform conversion module and a waveform output module;
the waveform input module receives a time domain photoelectric volume pulse signal actually measured from a certain part of a human body;
the signal conditioning module is used for preprocessing the input time domain photoplethysmography pulse signals, decomposing the preprocessed time domain photoplethysmography pulse signals into single-beat pulse signals corresponding to a single cardiac cycle, and normalizing the amplitude and wavelength of each single-beat pulse signal;
the waveform fitting module receives the normalized single-beat pulse signal and utilizes a given waveform containing a parameter expression fIFitting the pulse waveform by using a curve fitting algorithm, wherein the parameter-containing expressions of the waveform are determined by adding the parameter-containing expressions respectively representing the main wave, the dicrotic wave and the reflected wave of the pulse waveform, and each parameter vector of the analytical expression obtained by fitting is IIAs a waveform characteristic parameter vector;
the waveform of the photoplethysmography signal contains a reference expression as follows:
wherein Hin，bin，WinIs a parameter, t is an independent variable and represents the number of sampling points; in the expression, the parts of n being 1, 2 and 3 respectively correspond to a main wave, a reflected wave and a dicrotic wave of the pulse wave waveform; wherein HinRepresenting the amplitude of the wave, binIndicating the center position of the wave, WinThe width of the undulations;
the curve fitting process algorithm adopts a least square algorithm, limits each parameter range according to each fluctuation physiological significance, and then sets fitting initial conditions Hi1>Hi2>Hi3，bi1<bi2<bi3,Win>0; fitting the determined characteristic parameter vector IIComprises the following steps:
II＝[Hi1,Hi2,Hi3,bi1,bi2,bi3,Wi1,Wi2,Wi3]
under the condition that the waveform contains the parameter expression, the fitting effect mainly depends on the interference degree of pulse wave acquisition, so that the coefficient R is determined by fitting2As the quantification standard for waveform quality discrimination; fitting to determine the coefficient R2The method is a commonly used calculation method for judging the similarity degree of two curves; r2As an acquisition quality discrimination parameter, corresponds to R2A single beat waveform less than a certain value is considered to be sampledCollecting the quality difference and abandoning;
R2the calculation formula is as follows:
wherein,respectively representing an actually measured photoplethysmogram data point, an actually measured photoplethysmogram data average value and a data point fitting expected value, wherein pl is the number of data points of a single-beat pulse signal;
the waveform conversion module is used for grouping according to sex, age and average arterial pressure indexes of a measured person, and calculating and predicting pressure pulse wave waveform characteristic parameters of a corresponding part by utilizing measured photoplethysmography pulse wave characteristic parameters according to a priori statistical rule between the measured part of the photoplethysmography pulse wave and the waveform characteristic parameters of the part of the pressure pulse wave to be predicted under the corresponding grouping;
the method for establishing the prior statistical rule between the waveform characteristic parameters of the actual measurement part of the photoplethysmogram and the part of the pressure pulse wave needing to be predicted under the corresponding grouping comprises the following steps:
(1) firstly, grouping the population participating in the experiment according to gender, age and mean arterial pressure, wherein the age is started at 20 years and is separated from the age at 5 years; mean arterial pressure started at 70mmHg, spaced at 10 mmHg; grouping the population participating in the experiment; respectively and simultaneously detecting photoelectric volume pulse waves at ear, finger and toe positions of each group of experimental population, and detecting pressure pulse wave signals at radial artery, brachial artery and carotid artery positions by using pressure sensors; thereby obtaining actually measured pressure pulse waves and photoplethysmography signals of different crowd characteristics;
(2) then, establishing a statistical relationship between the waveform characteristic parameters of the actually measured photoplethysmogram and the actually measured pressure pulse wave of the target part; similar with above-mentioned photoplethysmography pulse wave waveform characteristic parameter vector extraction mode, for extracting pressure pulse wave waveform characteristic parameter, utilize pressure pulse wave waveform to contain the parameter expression and carry out the fitting to actual measurement pressure pulse wave waveform, pressure pulse wave waveform contains the parameter expression and is:
the expression parameters and the definition domain are all equal to fIThe same; the fitting process algorithm adopts a least square algorithm, limits each parameter range according to each fluctuation physiological significance, and then sets fitting initial conditions Ho1>Ho2>Ho3，bo1<bo2<bo3,Won>0；
The characteristic parameter vector is IO＝[Ho1,Ho2,Ho3,bo1,bo2,bo3,Wo1,Wo2,Wo3]
(3) Respectively using f for the measured waveforms of different groups and different partsIAnd fOFitting the actually measured photoplethysmogram waveform and pressure pulse waveform by a waveform fitting module to obtain I corresponding to each actually measured photoplethysmogram signalIAnd I of pressure pulse signalOVector quantity; for each part of the pressure pulse signal IOEstablishing a multiple linear regression equation of the feature parameter vectors of the photoplethysmography signals which correspond to different groups and are acquired simultaneously for each parameter of the vector, namely:
Ho1＝TM11×Hi1+TM12×Hi2+......+TM19×Wi3+CM1
Ho2＝TM21×Hi1+TM22×Hi2+......+TM29×Wi3+CM2
Ho3＝TM31×Hi1+TM32×Hi2+......+TM39×Wi3+CM3
bo1＝TM41×Hi1+TM42×Hi2+......+TM49×Wi3+CM4
bo2＝TM51×Hi1+TM52×Hi2+......+TM59×Wi3+CM5
bo3＝TM61×Hi1+TM62×Hi2+......+TM69×Wi3+CM6
Wo1＝TM71×Hi1+TM72×Hi2+......+TM79×Wi3+CM7
Wo2＝TM81×Hi1+TM82×Hi2+......+TM89×Wi3+CM8
Wo3＝TM91×Hi1+TM92×Hi2+......+TM99×Wi3+CM9
each coefficient of TM and CM is determined by multiple linear regression equation; finishing IOEstablishing an equation set by each parameter, and sorting a coefficient matrix and a constant matrix to obtain TM and CM, wherein TM is a 9-order square matrix, and CM is a 9-element column vector;
according to the TM and CM matrixes obtained by using the prior probability, in the application process, the waveform characteristic parameter vector I of the collected part is usedICalculating a feature parameter vector I of the target regionO(ii) a Extracting characteristic I from the measured photoplethysmographyIObtaining the characteristic parameter vector I of the target partOComprises the following steps:
IO＝TM×II+CM
then obtaining the target part characteristic parameter vector IOSubstituting into the corresponding part of the pressure pulse waveform containing parameter expression fOObtaining a corresponding pressure pulse wave analytic expression; completing the waveform conversion;
the waveform output module converts the result of the waveform conversion module and the characteristic parameter vector I of the target partOAnd outputting according to a required form.
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| CN105615845B (en) * | 2016-02-25 | 2020-05-19 | 广州视源电子科技股份有限公司 | Method and system for detecting interference pulse signal |
| CN109662702A (en) * | 2018-05-23 | 2019-04-23 | 李芝宏 | Condenser type pulse detection system and method |
| CN114040705A (en) * | 2019-07-30 | 2022-02-11 | 深圳迈瑞生物医疗电子股份有限公司 | Analysis method, monitoring device and monitoring system for regularity evaluation information |
| CN111685749B (en) * | 2020-06-18 | 2022-09-02 | 郑昕 | Construction method of pulse pressure wave mathematical model |
| WO2022116160A1 (en) * | 2020-12-04 | 2022-06-09 | Huawei Technologies Co., Ltd. | Method for predicting blood pressure, blood pressure prediction apparatus and computer program |
| CN112826459B (en) * | 2021-01-08 | 2022-11-29 | 北京工业大学 | Pulse wave waveform reconstruction method and system based on convolution self-encoder |
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