CN107582037A - Method based on pulse wave design medical product - Google Patents

Method based on pulse wave design medical product Download PDF

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CN107582037A
CN107582037A CN201710916372.2A CN201710916372A CN107582037A CN 107582037 A CN107582037 A CN 107582037A CN 201710916372 A CN201710916372 A CN 201710916372A CN 107582037 A CN107582037 A CN 107582037A
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physiological characteristic
pulse wave
model
data
index
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林祝发
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Qianhai Shenzhen Universal Health Technology Co Ltd
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Qianhai Shenzhen Universal Health Technology Co Ltd
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Abstract

A kind of medical product design method based on pulse wave, step include：1) pulse wave is collected using pulse wave harvester；2) the Pulse wave parameters computing module of deisgn product, for calculating and extracting all kinds of characteristic parameters of pulse wave；3) Numerical model of deisgn product or classification forecast model structure module, model is built for all kinds of characteristic parameters based on pulse wave, numerical prediction is carried out to the physiological characteristic for being used as target variable by model realization or classification is predicted, draws the corresponding physiological status of testee.The design method of the present invention, can effectively improve portability, the accuracy of medical product, while reduce the cost of medical product, and reduce the use threshold of medical product.

Description

Method based on pulse wave design medical product
Technical field
The invention belongs to biomedical engineering application field, the specifically method based on pulse wave design medical product.
Background technology
With the continuous improvement of our people's living standard, our people also increasingly carries to the attention degree of own health Height, it is also increasingly urgent to the demand of medical product.At present on the market Major medical product all there is cost is too high, portability compared with The problems such as requirement low, to operator is higher.
A kind of pulse wave is that the changes such as the diameter of biological peripheral circulation system, volume and pressure are adopted using biology sensor The bio signal of collection, these signals contain physiological and biochemical property very abundant in organism, and these physiological and biochemical properties are total Body is divided into following several classes：1st, the glucose content in the oxygen content of blood, blood in chemical signal, such as blood, swashing in blood Cellulose content etc.；2nd, physical signalling, such as blood vessel hardness, blood viscosity, blood vessel elasticity etc.；3rd, bio signal, such as nervous system In activity, the function of human body main organs etc..
The content of the invention
The present invention is the correlated characteristic by handling pulse wave, carries out the category filter of big data model to it, can obtain Go out the physiological status of tested personnel instantly.As a kind of noninvasive, physiological signal for easily gathering, the collection of pulse wave have into This is low, it is easy to operate the advantages that.Pulse wave harvester has the advantages of small volume.Therefore These characteristics are based on, by pulse wave Processing be a kind of low cost, easy to operate, medical product design method that can be portable.
The present invention the medical product design method based on pulse wave, can effectively improve medical product portability, Accuracy, while the cost of medical product is reduced, and reduce the use threshold of medical product.
The present invention design philosophy be：
Pulse wave signal is collected first with pulse wave harvester, transfers to signal processing module to carry out signal transacting；
Then the parameter of pulse wave is calculated；
Then model construction is carried out using Pulse wave parameters, by numerical prediction of the model realization to target variable or divided Class, draw the corresponding physiological status of testee.
Further, correlation model and data can also be uploaded to high in the clouds and carry out respective handling in above-mentioned steps.
Specifically, the step of medical product design method of the invention based on pulse wave is as follows：
Step 1 is the loading of pulse wave signal：
Pulse wave signal is obtained by pulse wave harvester；
Step 2 is the legitimacy detection of pulse wave signal：
Computing detection is carried out to pulse wave signal by the legitimacy detection module of product, judges whether this pulse wave is legal；
Step 3 is the denoising and conversion of pulse wave：
Denoising and conversion are carried out to pulse wave by the digital filtering module of product；
Step 4 is the extraction of Pulse wave parameters：
Handled to obtain Pulse wave parameters by the Pulse wave parameters computing module of product, and export to big data modeling and Sample database in determination module, specimen support is provided for the model construction of next step.Export simultaneously to Numerical model Or classification forecast model is predicted or judged；
Step 5 is the numerical prediction or classification prediction of physiological characteristic (target variable)；
Big data in the big data modeling of product and determination module models submodule to the pulse wave in sample database Parameter carries out feature extraction, builds Numerical model or classification forecast model, model is used using big data decision sub-module Realize the accurate judgement and assessment to testee's human body physiological characteristics；
Step 6 is Remote data processing：By networks such as product utilization GPRS network, 3G network, 4G networks or WIFI networks Connected mode accesses internet, and data transfer caused by above step and storage are arrived into remote server, real by remote server Now data are classified, inquired about, statistics, the function such as analysis and management.
Step 7 is that data are shown：
The display module of product realizes the result of the data, services function of providing remote server with form or figure Mode is shown on screen.
The medical product based on pulse wave that above-mentioned design method obtains, its job step include：
1) pulse wave is collected using pulse wave harvester；
2) calculate and extract all kinds of characteristic parameters of pulse wave；
3) all kinds of characteristic parameters structure model based on pulse wave, by model realization to physiological characteristic (i.e. target variable) Numerical prediction or classification prediction, draw the corresponding physiological status of testee.
4), step 1~3) in obtained model and/or data be uploaded to high in the clouds, remotely carry out data processing.
In the step 2), before all kinds of characteristic parameters for calculating and extracting pulse wave, in addition to step：
Computing detection first is carried out to pulse wave signal, judges whether this pulse wave is legal；
Denoising and conversion are carried out to legal pulse wave again：
Finally pulse wave is obtained to above-mentioned steps to calculate, obtain all kinds of characteristic parameters of pulse wave, and export to sample Database, the model construction of Numerical model or forecast model of classifying for step 3) provide specimen support；Export simultaneously It is predicted or judges to Numerical model or classification forecast model；
In the step 3), the characteristic parameter of the pulse wave obtained to step 2) extracts, and builds Numerical model Or classification forecast model, utilize accurate judgement and assessment of the model to the human body physiological characteristics of testee.)
In the step 4), internet is accessed using GPRS network, 3G network, 4G networks and/or WIFI network, by step 1) data transfer and storage~3) obtained arrives long-range cloud server, data are classified by server, inquires about, count, Analysis and management；
The result for the data, services function that the display module the reception server of product provides is shown in a manner of form or figure Show on the screen of product.
Brief description of the drawings
Fig. 1 designs to obtain the system of medical product and workflow diagram for this method；
Fig. 2 is big data modeling and determination module submodule workflow diagram；
Fig. 3 is pulse wave legitimacy overhaul flow chart；
Fig. 4 is digital filtering flow chart form 1；
Fig. 5 is digital filtering flow chart form 2；
Fig. 6 is that physiology index classification predicts flow chart.
Embodiment
A kind of medical product design method based on pulse wave, step include：
1) pulse wave is collected using pulse wave harvester；
2) the Pulse wave parameters computing module of deisgn product, for calculating and extracting all kinds of characteristic parameters of pulse wave；
3) Numerical model of deisgn product or classification forecast model structure module, for all kinds of spies based on pulse wave Parameter structure model is levied, numerical prediction is carried out to the physiological characteristic for being used as target variable by model realization or classification is predicted, is obtained Go out the corresponding physiological status of testee；
4) long-range cloud server is designed, step 1~3) in obtained model and/or data be uploaded to high in the clouds, remotely Carry out data processing.
In the step 2), before the Pulse wave parameters computing module of deisgn product, in addition to step：
The legitimacy detection module of first deisgn product, for carrying out computing detection to pulse wave signal, judge this pulse wave It is whether legal；
The digital filtering module of product is redesigned, for carrying out denoising and conversion to legal pulse wave：
Pulse wave is obtained to above-mentioned steps to calculate, obtain each of pulse wave by the Pulse wave parameters computing module of product Category feature parameter, and export to sample database, it is the model construction of the Numerical model of step 3) or forecast model of classifying Specimen support is provided；Output simultaneously is predicted or judged to Numerical model or classification forecast model；
In the step 3), the big data modeling of deisgn product and determination module, the spy of the pulse wave obtained to step 2) Sign parameter is extracted, and builds Numerical model or classification forecast model, special to the Human Physiology of testee using model Sign precisely judge and assess.
In the step 4), internet is accessed using GPRS network, 3G network, 4G networks and/or WIFI network, by step 1) data transfer and storage~3) obtained arrives long-range cloud server, data are classified by server, inquires about, count, Analysis and management；
The result for the data, services function that the display module the reception server of product provides is shown in a manner of form or figure Show on the screen of product.
In the step 2), the characteristic parameter of pulse wave is divided into three classes：Pulse wave time domain class parameter, pulse wave frequency domain class ginseng Number, pulse wave statistics class parameter；
The pulse wave time domain class parameter is that the position to the characteristic point of pulse wave sample sequence in time series is instant Between the parameter that draws of the combined factors analysis such as the area that surrounds of point, amplitude and each point；Here characteristic point includes pulse wave Crest, trough, flex point, dicrotic pulse point and pip；
The pulse wave frequency domain class parameter is position i.e. frequency, the width to the characteristic point of pulse wave frequency spectrum on frequency domain sequence The parameter that the analysis of the combined factors such as the area that degree and each point surround is drawn；Here characteristic point includes maximal peak point, each humorous Crest value point and specific frequency；
The pulse wave statistics class parameter is the pulse wave time domain class parameter and pulse wave frequency domain to multiple pulse wave signals Class parametric statistics constructs obtained parameter.
Specific to this example, such as Fig. 1, a kind of method that personal medical product is designed based on pulse wave, gathered using pulse wave Device obtains the pulse wave of measured, is delivered to the legitimacy of signal processing module detection pulse wave, and removes and be delivered to pulse The noises and pseudomorphism of ripple signal, Pulse wave parameters are calculated by parameter calculating module and are delivered to big data analysis determination module, The specific physiological status of measured is drawn, finally by data back to high in the clouds data module, is analyzed as sample lifting big data The accuracy and robustness of determination module.
Step 1 is the loading of pulse wave signal：
Pulse wave signal is passed in the system designed by pulse wave harvester this method；
Step 2 is the legitimacy detection of pulse wave signal：
Computing detection is carried out to pulse wave signal by the computing module of product, judges whether pulse wave is legal；
Step 3 is the denoising and conversion of pulse wave：
Line translation is entered to pulse wave by the digital filtering module of product, obtains effective pulse wave；
Mode 1 (wavelet transformation mode)：Wavelet decomposition is carried out to the pulse wave signal collected, obtains senior middle school's low frequency letter Number, dynamic threshold method is selected, is filtered respectively, removes invalid signals；
Mode 2 (Fourier's mode)：Classical filter device processing is carried out to the pulse wave signal collected, passes through trap respectively Wave filter, high-pass filter, low pass filter, bandpass filter remove invalid signals；
Step 4 is the extraction of Pulse wave parameters：
Handled to obtain Pulse wave parameters by the Pulse wave parameters computing module of product, and exported to the sample of Fig. 2 descriptions Database, specimen support is provided for the model construction of next step.Export simultaneously to Numerical model or classification forecast model It is predicted or judges；
Step 5 is structure and the use of the Numerical model of physiological characteristic or forecast model of classifying；
Big data in the big data modeling of product and determination module models pulse wave of the submodule according to sample database Parameter carries out feature extraction, builds Numerical model or classification forecast model, and utilize mould using big data decision sub-module Type realizes the accurate judgement and assessment to human body physiological state；
Step 6 is Remote data processing：By networks such as product utilization GPRS network, 3G network, 4G networks or WIFI networks Connected mode accesses internet, and data transfer caused by above step and storage are arrived into remote server, real by remote server Now data are classified, inquired about, statistics, the function such as analysis and management.
Step 7 is that data are shown：
The display module of product realizes the result of the data, services function of providing remote server with form or figure Mode is shown on screen.
Specifically, the medical product that this method designs to obtain is as shown in Figure 1：
Step 1 is the loading of pulse wave signal：
Pulse wave signal is passed in the product designed by pulse wave harvester this method；
Step 2 is the legitimacy detection (as shown in Figure 3) of pulse wave signal：
Pulse wave signal is first loaded into, pulse wave signal length is counted, judges whether its length meets minimum want Ask, if being unsatisfactory for requiring, the signal is illegal；It is 2048 points that length is carried out to signal, and step-length is the cutting of 300 points, Respectively and then the value length of signal 0 is calculated, if length is not in target zone.Then the signal is illegal；Fast Fourier change is carried out again Change, calculate the maximum of frequency spectrum whether in target zone, if not illegal in target zone, the signal.Calculate waveform pole Difference, if extreme difference in target zone, output signal；
Step 3 is the denoising and conversion of pulse wave:
Mode 1：It is 2048 points that legal signal is carried out to length to signal, and step-length is the cutting of 300 points, is carried out respectively Smothing filtering, medium filtering, then carry out wavelet decomposition (as shown in Figure 4), and wavelet decomposition formula is：
Wherein, X is the signal after conversion, and a is the time, and b is yardstick, and x (t) is original signal, and ψ is wavelet mother function, when t is Between independent variable
The use of sym8 small echos by signal decomposition is high frequency, intermediate frequency, low frequency three parts, be from sym small echos herein because It has regularity, orthogonality, has larger bearing length and supports wavelet transform simultaneously, meanwhile, become with reference to small echo Change itself has stronger protectiveness to bio signal, can retain the specificity information of pulse wave carrying to greatest extent, make Three parts signal is rebuild respectively with the wavelet coefficient of decomposition, and the signal less than threshold value is given up.Filter result is recombinated, and is obtained To useful signal.
Mode 2：Obtained legal signal is subjected to classical filter device conversion (as shown in Figure 5), classical filter method is more Maturation, such a method have operand small, and arithmetic speed is fast, more thorough to the noise filtering of fixed frequency band, pulse waveform The advantages that phse conversion can be predicted, respectively by notch filter, high-pass filter, low pass filter, bandpass filter, its Formula is：
Or：
Wherein：X be wave filter input signal, y be wave filter output signal, a0…aN, b0…bNFor coefficient, N, P, Q For filter order.
It is useful signal by the signal after wave filter.
Step 4 is the extraction of Pulse wave parameters：
Pulse wave parameters are divided into three classes：Pulse wave time domain class parameter, pulse wave frequency domain class parameter, pulse wave statistics class ginseng Number.This three classes parameter depicts the cardiovascular function and nervous function of human body from different aspect, and description content covers people The operation information of body main organs, and reference is provided to the quantitative description of human body items physical signs.
Pulse wave time domain class parameter：
Such shape parameter is characteristic point (such as crest, trough, flex point, dicrotic pulse point, the pip to pulse wave sample sequence Deng) position (time point) in time series, amplitude, and the ginseng that the combined factors analysis such as area for surrounding of each point is drawn Number；
The computational methods of pulse wave time domain class parameter：
First, the pulse wave signal being loaded into after step 3 processing；Secondly, each characteristic point is positioned；Again, each feature is calculated Position (time point), amplitude and the area of point；Pulse wave time domain class parameter is calculated finally by the combinations of attributes of each characteristic point.
Such as：Pulse wave translation time (PWTT) computational methods are：
PWTT=abs (x1-x2)
Wherein x1 is position (time point) corresponding to the pip of positioning, and x2 is the pulse wave acceleration maximum point pair of positioning The position (time point) answered.
First, the pulse wave signal after loading processing；Then, to above-mentioned signal carry out second differential, positioning first and Second crest, first crest correspond to x2, and second crest corresponds to x1, completes the positioning of x1 and x2 characteristic points；Finally by Pulse wave time domain class parameter PWTT is calculated in the poor absolute value of above-mentioned x1 and x2 positions.
The computational methods of pulse wave reflectance factor (RI) are：
Wherein, b is the amplitude that the non-differential pulse wave of dicrotic wave position correspondence is obtained under the first subdifferential, and a is non-differential arteries and veins Fight the amplitude of ripple maximum point.
First, the pulse wave signal after loading processing；Then, processing is ranked up to pulse wave sample sequence, positions arteries and veins Fight ripple maximum point；Then, once differentiation is carried out to pulse wave, positions pulse wave dicrotic pulse point；Finally by pulse wave maximum point width The ratio and normalized for spending a and pulse wave dicrotic pulse point amplitude b draw pulse wave time domain class parameter RI.
Pulse wave pulse cycle total time, t computational methods were：
T=x3-x0
Wherein, x3 is the final wave trough position of pulse wave, and x0 is that pulse wave originates wave trough position.
First, the pulse wave signal after loading processing；Then, processing is ranked up to pulse wave sample sequence, positions arteries and veins Ripple of fighting originates trough and final trough；Poor finally by the final trough x3 of pulse wave and starting trough x0 position (time point) Go out pulse wave time domain class parameter t.
Pulse wave rises phase index T1 computational methods：
T1=t1/t*100%
Wherein t is pulse cycle total time, and t1 is time pulse cycle rising stage.
First, the pulse wave signal after loading processing；Then, processing is ranked up to pulse wave sample sequence, positions arteries and veins Fight ripple maximum, starting trough and final trough；Then, the position (time point) by final trough x3 and starting trough x0 is poor Draw t；T1 is drawn by the position of pulse wave maximum and position (time point) difference of starting trough again；Finally by pulse week Rising stage phase time t1 and pulse cycle total time t ratio simultaneously are normalized to obtain T1.
Pulse wave declines phase index T2 computational methods：
T2=t2/t*100%
Wherein t is pulse cycle total time, and t2 is that pulse cycle declines time phase.
First, the pulse wave signal after loading processing；Then, processing is ranked up to pulse wave sample sequence, positions arteries and veins Fight ripple maximum, starting trough and final trough；Then, the position (time point) by final trough x3 and starting trough x0 is poor Draw t；T2 is drawn by the position of pulse wave maximum and position (time point) difference of final trough again；Finally by pulse week Rising stage phase time t1 and pulse cycle total time t ratio simultaneously are normalized to obtain T2.
Above-mentioned parameter and the sample database exported to big data modeling and determination module are calculated, is the model structure of next step Offer specimen support is provided.Output simultaneously is predicted or judged to Numerical model or classification forecast model；
2nd, pulse wave frequency domain class parameter.
Such shape parameter is characteristic point (such as maximal peak point, each harmonic spike point, the specific frequency to pulse wave frequency spectrum Deng) position (frequency) on frequency domain sequence, amplitude, and the parameter that the combined factors analysis such as area for surrounding of each point is drawn；
Pulse wave frequency domain parameter computational methods：
First, the pulse wave signal being loaded into after step 3 processing；Then, using modern spectrum analysis means, (such as Fourier becomes Change, AR Power estimations etc.) frequency spectrum of pulse wave is obtained, again, calculate position (frequency), amplitude and the area of each characteristic point；Finally Pulse wave frequency domain class parameter is calculated by the combinations of attributes of each characteristic point.
Such as：Pulse wave first harmonic ratio F1.The parameter is pulse wave second harmonic height and first harmonic height Ratio, circular are：First, the pulse wave signal being loaded into after step 3 processing；Then, modern spectrum analysis means are utilized (such as Fourier transformation, AR Power estimations) obtains the frequency spectrum of pulse wave, then processing is ranked up to frequency spectrum and navigates to pulse wave One harmonic wave maximum point and second harmonic maximum point；It is maximum finally by the height and first harmonic of pulse wave second harmonic maximum point The radiometer of the height of point calculates pulse wave frequency domain class parameter F1.
Pulse wave second harmonic ratio F2, the parameter are the ratio of pulse wave third harmonic height and second harmonic height. Circular is：First, the pulse wave signal being loaded into after step 3 processing；Secondly, modern spectrum analysis means (such as Fu is utilized In leaf transformation, AR Power estimations etc.) obtain the frequency spectrum of pulse wave, then processing is ranked up to frequency spectrum and navigates to pulse wave second harmonic Maximum point and third harmonic maximum point；Finally by the height and the height of second harmonic maximum point of pulse wave third harmonic maximum point The radiometer of degree calculates pulse wave frequency domain class parameter F2.
Pulse wave first harmonic power P 1, the parameter are pulse wave power spectrum first harmonic wave band general power.It is specific to calculate Method is：First, the pulse wave signal being loaded into after step 3 processing；Secondly, using modern spectrum analysis means, (such as Fourier becomes Change, AR Power estimations etc.) power spectrum of pulse wave is obtained, then processing is ranked up to power spectrum and navigates to pulse wave first harmonic most A little bigger and adjacent two both sides of first harmonic smallest point；Finally by pulse wave first harmonic maximum point and first harmonic two The area that the adjacent smallest point in both sides surrounds obtains pulse wave frequency domain class parameter P1.
3rd, pulse wave statistics class parameter.
Such shape parameter is the pulse wave time domain class parameter and pulse wave to multiple pulse wave signals using modern statistics The parameter that frequency domain class parametric configuration obtains.
Pulse wave counts class calculation method of parameters：
Such as：The standard deviation SDNN computational methods of whole pulse cycles are：
SDNN=std (RR)
Wherein：RR is whole pulse cycle sequences, the sequence of the pulse wave time domain class parameter t compositions of specially one section pulse wave Row, std are calculating standard deviation formula.
The difference standard deviation SDSD computational methods of whole pulse cycles are：
SDSD=std (diff (RR))
Wherein：RR is whole pulse cycle sequences, the sequence of the pulse wave time domain class parameter t compositions of specially one section pulse wave Row, diff are that differential process is carried out to sequence, and std is calculating standard deviation formula.
Pulse first harmonic ratio standard deviation VF1 computational methods are：
VF1=std (FF)
Wherein：FF is the sequence of the pulse wave frequency domain class parameter F1 compositions of individual different time measure, and std is calculating Standard deviation formula.
Step 5 is the numerical prediction or classification prediction (as shown in Figure 6) of physiological characteristic：
First, data prediction
The Pulse wave parameters data in sample database are pre-processed using big data modeling submodule.Step is such as Under：
First, invalid value present in data is rejected, cleans exceptional value, interpolation is carried out to missing values.
Second, data conversion, such as normalization, discretization, exponential transform, logarithmic transformation.
The higher Pulse wave parameters data of quality can be obtained by two above step.Afterwards, Feature Engineering is used, Choose suitable Pulse wave parameters generation modeling index.
2nd, Feature Engineering
Index generation is carried out to Pulse wave parameters caused by data prediction, mode is as follows：
Mode one, Data Dimensionality Reduction.The dimensionality reduction technologies such as PCA, LDA are taken to carry out Data Dimensionality Reduction to Pulse wave parameters, acquisition is built Modular character.
Mode two, feature selecting.Mainly there is the feature selecting scheme of three types.
The feature selecting of 1.Filter schemes.The program mainly considers Pulse wave parameters (independent variable) and physiological characteristic (mesh Mark variable) between association.Main method has related-coefficient test, Chi-square Test, information gain and mutual information etc., so as to raw Into modeling index.
The feature selecting of 2.Wrapper schemes.The program is mainly decided whether a pulse wave by object function Parameter is added in model.Main method has search, heuristic search and random search etc. completely, refers to so as to generate modeling Mark.
The feature selecting of 3.Embedded schemes.The program is mainly special by machine learning progress automatic to Pulse wave parameters Sign selection.Main method has regularization, entropy and information gain etc., so as to generate modeling index.
Mode three, variable derive.Pulse wave parameters (initial parameter) are processed, generate significant variable.Mainly Pass through the means such as functional transformation.
By three of the above mode, can obtain needing the index for establishing model.After obtaining modeling index, to modeling The relation of index and physiological characteristic establishes corresponding model.
3rd, Numerical model or classification forecast model are constructed.
Prediction is broadly divided into two kinds of scenes, and the first scene is to carry out numerical prediction to physiological characteristic；Second is to physiology Feature is classified.According to different application scenarios, Numerical model or classification forecast model are constructed.
1. physiological characteristic numerical prediction.
When physiological characteristic is numeric type variable, according to different application scenarios and to modeling index and physiological characteristic relation Understanding it is different, mainly have following three kinds of modeling methods.
1.1 Application of Parametric Model Forecasting physiological characteristic numerical value.The clear and definite scene of relation for modeling index and physiological characteristic, I Model built using parameter model method realize the numerical prediction to physiological characteristic.Comprise the following steps that：First, by data (being made up of modeling index and physiological characteristic) is divided into training set and test set.Second, on the basis of training set, tested using intersection Card method, by homing method come the relation being fitted between modeling index and physiological characteristic, main computational methods have a most young waiter in a wineshop or an inn Multiply method, Maximum-likelihood estimation and quantile estimate etc., by above method computation model parameter, determine model, so as to real Now to the accurate prediction of physiological characteristic numerical value.
1.2 nonparametric models predict physiological characteristic numerical value.For the indefinite field of relation of modeling index and physiological characteristic Scape, we build model to realize the numerical prediction to physiological characteristic using Non-parameter modeling method.Comprise the following steps that：The One, data (being made up of modeling index and physiological characteristic) are divided into training set and test set.Second, on the basis of training set, Using cross validation method, nonparametric is built by KERNEL FUNCTION METHOD, arest neighbors function method, splines method, wavelet function method etc. Model.The accurate prediction to physiological characteristic numerical value is realized by the nonparametric model of above-mentioned structure.
1.3 semi-parameter models predict physiological characteristic numerical value.Clear and definite, the portion for the relation of part modeling index and physiological characteristic The indefinite scene of relation of point modeling index and physiological characteristic, we are realized pair using half parameter model method structure model The numerical prediction of physiological characteristic.Comprise the following steps that：First, data are divided into training set and test set.Second, in training set On the basis of, using cross validation method, by the way that the nonparametric model of the parameter model of a part of index and a part of index is had Machine combined structure semi-parameter model.The accurate prediction to physiological characteristic numerical value is realized by the semi-parameter model of above-mentioned structure.
2. physiological characteristic classification prediction
When physiological characteristic is classification type variable, physiological characteristic is classified, structure classification forecast model.Classification prediction The method of model can be divided into two kinds of single classifier and integrated classifier.
2.1. method one：Single classifier
Single classifier mainly has decision tree classifier, neural network classifier, support vector machine classifier, Bayes's classification Device and logistic regression grader etc..
A. decision tree classifier
In the case where modeling index and physiological characteristic have more obvious linear relationship, decision tree classifier can be used Physiological characteristic is classified.
It is special according to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiology Data are levied as training set, based on training set, construct decision tree.Comprise the following steps that：First, information gain maximize or Under the maximized principle of Gini coefficient instructs, selective goal；Second, predictive pruning or rear Pruning strategy can be used, trims decision tree, It is final to meet that loss function minimizes this qualifications, form the decision tree that may finally classify to data.3rd, by certainly Plan tree is realized to classification type physiological characteristic exact classification.
B. neural network classifier
In the case where modeling data amount is larger, physiological characteristic can be classified using neural network classifier.
It is special according to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiology Data are levied as training set, based on training set, construct artificial neural network.According to practical application scene, neutral net is determined The number of the number of plies and each hidden layer neuron；It is determined that after the number of plies and neuron number, principle is declined etc. based on gradient The parameter of neutral net is adjusted, constantly updates parameter, until the condition for meeting to terminate, finally meets that loss function minimizes this Qualifications, so as to form the neutral net that may finally classify to data (being made up of modeling index and physiological characteristic).For The scene of mass data collection, high dimensional feature index, based on systems such as deep learning construction convolutional neural networks, Recognition with Recurrent Neural Network Row neural network model (artificial intelligence) can realize efficient, the accurate differentiation to physiological characteristic.
C. support vector machine classifier
It is higher in modeling index dimension, in the case that physiological characteristic data amount is less, support vector machine classifier can be used Physiological characteristic is classified.
It is special according to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiology Data are levied as training set, based on training set, construct SVMs.Suitable kernel function is chosen for different scenes, with And punishment parameter, supporting vector is finally determined, to meet that loss function minimizes this qualifications, formation may finally be to life Manage the SVMs of tagsort.
D. Bayes classifier
It is smaller in the correlation of modeling index and in the case that data target dimension is higher, Bayes classifier pair can be used Physiological characteristic is classified.
It is special according to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiology Data are levied as training set, based on training set, construct Bayes classifier.By analyzing and training collection, physiological characteristic data is obtained The prior probability of distribution, under the guidance that posterior probability maximizes criterion, build the pattra leaves of physiological characteristic data exact classification This grader.
E. logistic regression grader
It is index related weaker modeling, and to the required precision of classification it is not high in the case of, can be using logistic regression point Base grader of the class device as integrated classifier, the final exact classification realized to physiological characteristic.
It is special according to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiology Data are levied as training set, based on training set, constitutive logic returns grader.Under conditions of meeting that loss function minimizes, Calculating logic returns the parameter of grader, forms the logistic regression grader that may finally classify to data.
2.2 methods two：Integrated classifier
In the case where single classifier is bad to physiological characteristic classifying quality, the essence of judgement is improved using integrated classifier Degree.Integrated classifier, by the combination to single grader, forms strong classifier based on single grader, final to realize To the exact classification of data
Integrated classifier can use Boosting classes and bagging classes etc..
Boosting integrated classifiers mainly have Adaboost, Xgboost etc..
C.Adaboost graders
Situation for can not voluntarily carry out characteristic index selection, feature work can be carried out automatically using Adaboost graders Journey, and complete to model, realize the accurate differentiation of physiological characteristic.Comprise the following steps that：
The physiological characteristic data combined according to existing modeling index and its corresponding physiological characteristic, selected part physiology Characteristic, based on training set, constructs Adaboost graders as training set.According to Adaboost algorithm, instruction is constantly updated Practice the sample weights of collection and the weight of base grader, ultimately form the linear combination of base grader, may finally be right so as to be formed The Adaboost graders of physiological characteristic data classification.
D.Xgboost graders
Situation about classifying for the physiological characteristic for needing voluntarily to construct loss function and discriminant criterion, can use Xgboost Grader is precisely differentiated to physiological characteristic.It is as follows to implement step：
The physiological characteristic data combined according to existing modeling index and its corresponding physiological characteristic, selected part physiology Characteristic, based on training set, constructs Xgboost graders as training set.Iterations t is set, builds t grader. The initial value of the grader of each round is the residual error of last round of predicted value and actual value.Xgboost parameter is adjusted, it is final to obtain Strong classifier, so as to realize the exact classification to physiological characteristic data.
The main of bagging algorithms includes random forest scheduling algorithm.It is larger for physiological characteristic data amount, characteristic index compared with It is more, the situation of feature selecting can not be voluntarily carried out, using random forest grader.The step of building random forest grader is such as Under：
The physiological characteristic data combined according to existing modeling index and its corresponding physiological characteristic, selected part physiology Characteristic, based on training set, constructs random forest grader as training set.A tree m for setting tree, from original training set Extract a part of data, m new training datas of construction；Extract part modeling index from original index, construction m is new Index set.Combine new training data and new index set are corresponding, construct base grader, most base classifiers combination at last, press The mode chosen according to ballot, strong classifier is formed, so as to realize the exact classification to physiological characteristic data.
4th, Numerical model or classification forecast model are assessed.
Predicted and required according to different physiological characteristics, build Numerical model or classification forecast model.Model completes it Afterwards, on test set evaluation model quality.Numerical model is mainly with the error mean and error of predicted value and actual value Standard deviation is as evaluation index.Classify forecast model with a series of fingers such as the accuracy rate of concrete class and prediction classification, recall rates It is denoted as evaluation index.If model quality can not meet application request, the step for backout feature engineering, regenerate New physical signs and new model.
5th, determine and use model.
It is determined that the model of generation meets application request, the model is effectively stored, and it is special to novel physiological using the model Sign data are predicted.
6th, Numerical model and classification forecast model example
1st, Numerical model example
Numerical prediction example is blood pressure forecast model.
The first step, blood pressure data pretreatment.Data cleansing, logarithm value type index normalization, to class are carried out to blood pressure data Other type index factor, these indexs are PWTT (pulse wave translation time), height (height), weight (body weight), age (age), gender (sex).Data are divided into training set and test set.
Second step, blood pressure characteristics engineering.Using related-coefficient test selection physical signs PWTT, height and weight.
3rd step, blood pressure model construction.Because index PWTT passes through theoretical proof, and there is linear relationship in blood pressure, and its Remaining index and the relation of blood pressure are not especially clear and definite, therefore select semi-parametric regression model being trained as blood pressure forecast model Collection constructs semi-parametric regression model by cross validation.
4th step, blood pressure model evaluation.Blood pressure predicted value and blood pressure actual value error mean on test set are 4.72 Millimetres of mercury, the error to standard deviation of blood pressure actual value and blood pressure predicted value is 7.3 millimetress of mercury, meets national standard error mean Less than 5 millimetress of mercury, error to standard deviation is less than the requirement of 8 millimetress of mercury.
5th step, the determination and use of model.Because semi-parameter model performance meets application requirement, have to the model Effect stores and carries out blood pressure prediction to new physiological characteristic data using the model.
2nd, classification forecast model example.
Classification prediction example is drug abuse decision model.
The first step, drug abuse data prediction.Logarithm value index normalizes, to classification index factor, to target variable because Sonization.Index is PWTT (pulse wave translation time), height (height), weight (body weight), age (age), gender (property Not), T1 (pulse wave rising phase index), T2 (pulse wave decline phase index).Target variable is TF (whether taking drugs).
Second step, drug abuse Feature Engineering.Physiological characteristic index is screened using lasso logistic regressions, rejects age indexs.
3rd step, first time drug abuse grader structure.Decision tree is built by cross validation in training set, increased according to information Beneficial principle and rear beta pruning step, reject variable height, weight.Form grader A.
4th step, first time drug abuse grader A are assessed.Grader A classifying qualities are verified on test set, find prediction Accuracy rate is 75%, and recall rate 55%, model quality is poor, backout feature engineering.
5th step, second of Feature Engineering and drug abuse grader structure.Because single classifier prediction effect is poor, selection is random Forest is as strong classifier.Feature Engineering is carried out by cross validation automatically in training set, and builds random forest grader, shape Constituent class device B.
6th step, second of drug abuse grader B are assessed.Random forest classifying quality is verified on test set, finds prediction Accuracy rate be 95%, recall rate 91%, model quality is higher, is not required to model again, retains grader B.
7th step, the determination and use of grader.The drug abuse grader B of second of structure is effectively stored and used The grader is classified to new physiological characteristic data.
Step 6 is Remote data processing：By networks such as product utilization GPRS network, 3G network, 4G networks or WIFI networks Connected mode accesses internet, data transfer caused by above step and storage is arrived into remote server, by remote server pair Data are classified, and are inquired about, statistics, the function such as analysis and management.
Step 7 is that data are shown：
The display module of product realizes the result of the data, services function of providing remote server with form or figure Mode is shown on screen.
It should be noted last that the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted.Although ginseng The present invention is described in detail according to embodiment, it will be understood by those within the art that, to the technical side of the present invention Case is modified or equivalent substitution, and without departure from the spirit and scope of technical solution of the present invention, it all should cover in the present invention Right among.

Claims (7)

  1. A kind of 1. medical product design method based on pulse wave, it is characterized in that step includes：
    1) pulse wave is collected using pulse wave harvester；
    2) the Pulse wave parameters computing module of deisgn product, for calculating and extracting all kinds of characteristic parameters of pulse wave；
    3) Numerical model of deisgn product or classification forecast model structure module, for each category feature ginseng based on pulse wave Number structure model, by model realization to be used as target variable physiological characteristic carry out numerical prediction or classification predict, draw by The corresponding physiological status of tester.
  2. 2. design method according to claim 1, it is characterized in that also including step 4) designs long-range cloud server, step The model and/or data obtained in rapid 1~3) is uploaded to high in the clouds, remotely carries out data processing.
  3. 3. design method according to claim 1, it is characterized in that in the step 2), in the Pulse wave parameters of deisgn product Before computing module, in addition to step：
    The legitimacy detection module of first deisgn product, for carrying out computing detection to pulse wave signal, whether judge this pulse wave It is legal；
    The digital filtering module of product is redesigned, for carrying out denoising and conversion to legal pulse wave：
    Pulse wave is obtained to above-mentioned steps to calculate, obtain all kinds of spies of pulse wave by the Pulse wave parameters computing module of product Parameter is levied, and exports to sample database, the model construction of Numerical model or forecast model of classifying for step 3) and provides Specimen support；Output simultaneously is predicted or judged to Numerical model or classification forecast model.
  4. 4. design method according to claim 1, it is characterized in that in the step 3),
    The big data modeling of deisgn product and determination module, the characteristic parameter of the pulse wave obtained to step 2) extract, structure Numerical model or classification forecast model are built, the human body physiological characteristics of testee are precisely judged and commented using model Estimate.
  5. 5. design method according to claim 1, it is characterized in that in the step 4)：
    Internet is accessed using GPRS network, 3G network, 4G networks and/or WIFI network, by step 1)~3) obtained data Transmit and store long-range cloud server, data are classified, inquire about, count, analyze and managed by server；
    The result for the data, services function that the display module the reception server of product provides is shown in a manner of form or figure On the screen of product.
  6. 6. design method according to claim 1, it is characterized in that in the step 2), the characteristic parameter of pulse wave is divided into three Class：Pulse wave time domain class parameter, pulse wave frequency domain class parameter, pulse wave statistics class parameter；
    The pulse wave time domain class parameter be to position i.e. time point of the characteristic point of pulse wave sample sequence in time series, The parameter that the analysis of the combined factors such as the area that amplitude and each point surround is drawn；Crest of the characteristic point here including pulse wave, Trough, flex point, dicrotic pulse point and pip；
    The pulse wave frequency domain class parameter be to position i.e. frequency of the characteristic point of pulse wave frequency spectrum on frequency domain sequence, amplitude, And the parameter that the combined factors analysis such as area for surrounding of each point is drawn；Here characteristic point includes maximal peak point, each harmonic wave Peak point and specific frequency；
    The pulse wave statistics class parameter is pulse wave time domain class parameter and pulse wave frequency domain the class ginseng to multiple pulse wave signals The parameter that number statistics construction obtains.
  7. 7. design method according to claim 1, it is characterized in that in the step 3), Numerical model or classification prediction Model building method is：
    3.1) data prediction
    The Pulse wave parameters data obtained to step 2) pre-process, and step is：
    The first step, invalid value present in data is rejected, clean exceptional value, interpolation is carried out to missing values；
    Second step, data conversion, method include normalization, discretization, exponential transform or logarithmic transformation；
    3.2) Feature Engineering
    Index generation is carried out to Pulse wave parameters caused by data prediction, mode is：
    Mode one, Data Dimensionality Reduction, take the dimension-reduction algorithms such as PCA, LDA to carry out Data Dimensionality Reduction to Pulse wave parameters, obtain modeling and refer to Mark；
    Mode two, feature selecting, scheme have three kinds, are respectively
    The feature selecting of 1.Filter schemes：It is used as the Pulse wave parameters of independent variable and the physiology as target variable by investigating Association between feature, so as to generate modeling index；
    The feature selecting of 2.Wrapper schemes：Decide whether a Pulse wave parameters being added to model by object function In, so as to generate modeling index；
    The feature selecting of 3.Embedded schemes：By machine learning carry out feature selecting automatic to Pulse wave parameters, so as to raw Into modeling index.
    Mode three, variable derive, the Pulse wave parameters as initial parameter are processed, generate significant variable；
    Obtain needing the index for establishing model by three of the above mode；Phase is established to the relation of modeling index and physiological characteristic again The model answered；
    3.3) Numerical model or classification forecast model are constructed
    Prediction is divided into two kinds of scenes, and the first scene is to carry out numerical prediction to physiological characteristic；Second is that physiological characteristic is carried out Classification；According to different application scenarios, Numerical model or classification forecast model are constructed；
    1. physiological characteristic numerical prediction
    When physiological characteristic is numeric type variable, according to different application scenarios and to modeling index and the reason of physiological characteristic relation Solution is different, including following three kinds of modeling methods：
    1.1 Application of Parametric Model Forecasting physiological characteristic numerical value
    For modeling index and the clear and definite scene of relation of physiological characteristic, model is built using parameter model method to realize to life The numerical prediction of feature is managed, step is as follows：
    The first step, the data being made up of modeling index and physiological characteristic are divided into training set and test set；
    Second step, on the basis of training set, using cross validation method, it is special that modeling index and physiology are fitted by homing method Relation between sign；
    1.2 nonparametric models predict physiological characteristic numerical value
    For modeling index and the indefinite scene of relation of physiological characteristic, model is built using Non-parameter modeling method to realize Numerical prediction to physiological characteristic, step are as follows：
    The first step, the data being made up of modeling index and physiological characteristic are divided into training set and test set；
    Second step, on the basis of training set, using cross validation method, pass through KERNEL FUNCTION METHOD, arest neighbors function method, spline function Method or wavelet function method structure nonparametric model；
    1.3 semi-parameter models predict physiological characteristic numerical value
    Clear and definite for the relation of part modeling index and physiological characteristic, the relation of part modeling index and physiological characteristic is indefinite Scene, model is built using half parameter model method to realize the numerical prediction to physiological characteristic, step is as follows：
    The first step, data are divided into training set and test set；
    Second step, on the basis of training set, using cross validation method, by by the parameter model and nonparametric of a part of index Models coupling constructs semi-parameter model；
    2. physiological characteristic classification prediction
    When physiological characteristic is classification type variable, physiological characteristic is classified, structure classification forecast model, forecast model of classifying Method is divided into two kinds of single classifier and integrated classifier.
    2.1. method one：Single classifier
    Single classifier include decision tree classifier, neural network classifier, support vector machine classifier, Bayes classifier and Logistic regression grader；
    A. decision tree classifier
    In the case where modeling index and physiological characteristic have more obvious linear relationship, using decision tree classifier to physiology Feature is classified；
    According to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiological characteristic number According to as training set, based on training set, decision tree is constructed, step is as follows：
    The first step, in the case where information gain maximizes or the maximized principle of Gini coefficient instructs, selective goal；
    Second step, using predictive pruning or rear Pruning strategy, decision tree is trimmed, finally meets that loss function minimizes this restriction bar Part, form the decision tree that may finally classify to data；
    3rd step, realized by decision tree to classification type physiological characteristic exact classification；
    B. neural network classifier
    In the case where modeling data amount is big, physiological characteristic is classified using neural network classifier；
    According to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiological characteristic number According to as training set, based on training set, artificial neural network is constructed；According to practical application scene, the number of plies of neutral net is determined And the number of each hidden layer neuron；It is determined that after the number of plies and neuron number, adjusted based on principles such as gradient declines The parameter of neutral net, parameter is constantly updated, until the condition for meeting to terminate, finally meet that loss function minimizes this restriction Condition, so as to form the neutral net finally classified to the data being made up of modeling index and physiological characteristic；
    For mass data collection, the scene of high dimensional feature index, based on deep learning constructing neural network model realization to physiology Efficient, the accurate differentiation of feature；
    C. support vector machine classifier
    It is higher in modeling index dimension, in the case that physiological characteristic data amount is less, using support vector machine classifier to physiology Feature is classified；
    According to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiological characteristic number According to as training set, based on training set, SVMs is constructed；Suitable kernel function is chosen for different scenes, and is punished Penalty parameter, supporting vector is finally determined, to meet that loss function minimizes this qualifications, formation may finally be special to physiology Levy the SVMs of classification；
    D. Bayes classifier
    It is smaller in the correlation of modeling index and in the case that data target dimension is higher, it is special to physiology using Bayes classifier Sign is classified；
    According to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiological characteristic number According to as training set, based on training set, Bayes classifier is constructed；By analyzing and training collection, physiological characteristic data distribution is obtained Prior probability, posterior probability maximize criterion guidance under, build the Bayes classifier of physiological characteristic exact classification；
    E. logistic regression grader
    Model it is index related weaker, and to the required precision of classification it is not high in the case of, using logistic regression grader make For the base grader of integrated classifier, the final exact classification realized to physiological characteristic；
    According to existing modeling index and its corresponding physiological characteristic combination producing physiological characteristic data, selected part physiological characteristic number According to as training set, based on training set, constitutive logic returns grader；Under conditions of meeting that loss function minimizes, calculate The parameter of logistic regression grader, form the logistic regression grader that may finally classify to physiological characteristic；
    2.2 methods two：Integrated classifier
    In the case where single classifier is bad to physiological characteristic classifying quality, the precision of judgement is improved using integrated classifier. Integrated classifier, by the combination to single grader, is formed strong classifier, finally realizes logarithm based on single grader According to exact classification；
    Integrated classifier includes Boosting classes or bagging classes；
    Boosting integrated classifiers include Adaboost, Xgboost；
    A.Adaboost graders
    Situation for can not voluntarily carry out characteristic index selection, Feature Engineering is carried out using Adaboost graders automatically, and Modeling is completed, realizes the accurate differentiation of physiological characteristic, step is as follows：
    Physiological characteristic data, selected part physiological characteristic number are combined according to existing modeling index and its corresponding physiological characteristic According to as training set, based on training set, Adaboost graders are constructed；According to Adaboost algorithm, training set is constantly updated The weight of sample weights and base grader, the linear combination of base grader is ultimately formed, may finally be special to physiology so as to be formed Levy the Adaboost graders of data classification；
    B.Xgboost graders
    Situation about classifying for the physiological characteristic for needing voluntarily to construct loss function and discriminant criterion, using Xgboost graders Physiological characteristic is precisely differentiated, step is as follows：
    Physiological characteristic data, selected part physiological characteristic number are combined according to existing modeling index and its corresponding physiological characteristic According to as training set, based on training set, Xgboost graders are constructed；Iterations t is set, builds t grader；Each round Grader initial value be last round of predicted value and actual value residual error；Xgboost parameter is adjusted, it is final to obtain strong classification Device, so as to realize the exact classification to physiological characteristic data；
    Bagging algorithms mainly include random forest scheduling algorithm；Larger for physiological characteristic data amount, characteristic index is more, nothing Method voluntarily carries out the situation of feature selecting, and using random forest grader, the step of building random forest grader is as follows：
    Physiological characteristic data, selected part physiological characteristic number are combined according to existing modeling index and its corresponding physiological characteristic According to as training set, based on training set, random forest grader is constructed；A tree m for setting tree, one is extracted from original training set Partial data, m new training datas of construction；Part modeling index, m new indexs of construction are extracted from original index Collection；Combine new training data and new index set are corresponding, construct base grader, most base classifiers combination at last, according to throwing The mode taken is voted for, strong classifier is formed, so as to realize the exact classification to physiological characteristic data；
    3.4) Numerical model or classification forecast model are assessed
    Predicted and required according to different physiological characteristics, build Numerical model or classification forecast model；After model is completed, The quality of evaluation model on test set；Numerical model is mainly with the error mean and error to standard deviation of predicted value and actual value As evaluation index；Classify forecast model using a series of indexs such as concrete class and the prediction accuracy rate of classification, recall rate as Evaluation index；If model quality can not meet application request, backout feature engineering step, regenerate new physiology and refer to Mark and new model；
    3.5) determine and use model
    It is determined that the model of generation meets application request, the model is effectively stored, and using the model to novel physiological characteristic According to being predicted.
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Application publication date: 20180116