CN104127194A  Depression evaluating system and method based on heart rate variability analytical method  Google Patents
Depression evaluating system and method based on heart rate variability analytical method Download PDFInfo
 Publication number
 CN104127194A CN104127194A CN201410334899.0A CN201410334899A CN104127194A CN 104127194 A CN104127194 A CN 104127194A CN 201410334899 A CN201410334899 A CN 201410334899A CN 104127194 A CN104127194 A CN 104127194A
 Authority
 CN
 China
 Prior art keywords
 data
 interval
 module
 heart rate
 parameter
 Prior art date
Links
 238000004458 analytical method Methods 0.000 title claims abstract description 42
 241001364929 Havel River virus Species 0.000 claims abstract description 59
 238000000034 method Methods 0.000 claims description 34
 230000029058 respiratory gaseous exchange Effects 0.000 claims description 17
 238000001228 spectrum Methods 0.000 claims description 16
 230000000747 cardiac effect Effects 0.000 claims description 13
 238000003745 diagnosis Methods 0.000 claims description 11
 239000000203 mixture Substances 0.000 claims description 11
 230000000694 effects Effects 0.000 claims description 7
 230000005540 biological transmission Effects 0.000 claims description 5
 241000272194 Ciconiiformes Species 0.000 claims description 4
 230000001915 proofreading Effects 0.000 claims description 4
 238000004364 calculation method Methods 0.000 claims description 3
 238000006243 chemical reaction Methods 0.000 claims description 3
 230000000875 corresponding Effects 0.000 claims description 2
 238000004321 preservation Methods 0.000 claims description 2
 238000001514 detection method Methods 0.000 claims 6
 238000011158 quantitative evaluation Methods 0.000 abstract 1
 201000009916 postpartum depression Diseases 0.000 description 6
 230000003001 depressive Effects 0.000 description 4
 230000035935 pregnancy Effects 0.000 description 4
 201000010099 diseases Diseases 0.000 description 3
 230000003340 mental Effects 0.000 description 3
 206010022114 Injuries Diseases 0.000 description 2
 230000035606 childbirth Effects 0.000 description 2
 238000005516 engineering processes Methods 0.000 description 2
 206010002855 Anxiety Diseases 0.000 description 1
 206010057666 Anxiety diseases Diseases 0.000 description 1
 241001269238 Data Species 0.000 description 1
 206010012378 Depression Diseases 0.000 description 1
 229940088597 Hormone Drugs 0.000 description 1
 206010025482 Malaise Diseases 0.000 description 1
 230000036506 anxiety Effects 0.000 description 1
 238000004195 computeraided diagnosis Methods 0.000 description 1
 230000032686 female pregnancy Effects 0.000 description 1
 239000000745 gonadal hormones Substances 0.000 description 1
 239000005556 hormones Substances 0.000 description 1
 238000007689 inspection Methods 0.000 description 1
 230000004048 modification Effects 0.000 description 1
 238000006011 modification reactions Methods 0.000 description 1
 230000035479 physiological effects, processes and functions Effects 0.000 description 1
 230000002265 prevention Effects 0.000 description 1
 230000011218 segmentation Effects 0.000 description 1
 230000000638 stimulation Effects 0.000 description 1
Abstract
The invention discloses a depression evaluating system and method based on a heart rate variability analytical method, particularly a depression evaluating system and method in a perinatal period. The depression evaluating system comprises a data storage module used for storing electrocardio and pulse wave signals, a peak detecting module used for acquiring a peak point sequence of recording data, a data correcting module used for acquiring sinus beat NN interval sequence, a heart rate variability curve acquiring module used for acquiring a heart rate variability curve, an HRV analyzing module conducting time domain analysis, frequency domain analysis and nonlinear analysis, a feature parameter selecting module used for selecting a feature parameter from HRV parameters, a modeling module used for obtaining a perinatal period depression classification model, and a model applying module used for inputting the data of a testee into the classification module to obtain a depression degree. By means of the depression evaluating system and method based on the heart rate variability analytical method, degree quantitative evaluation of perinatal period depression is achieved, a scientific research method of the depression based on the technical field of physiological information examination is enriched, a test is simple and practicable, medical resources can be effectively saved, and good clinical practicality is achieved.
Description
Technical field
The present invention relates to a kind of computeraided diagnosis technology, particularly a kind of evaluating system of the depression based on heart rate variance analyzing method and method
Background technology
Gestation and childbirth are women's great experience in life, aspect physiology, will experience each vitals of health and gonadal hormone and a series of great change of associated hormone pregnancy period to puerperal; At psychological aspects, adapt to the variation of the relations such as work, family and colleague, be concerned about neonatal growing up healthy and sound, consider the arrangement of economic aspect etc.; The variation of these complexity and the stimulation of other undesirable elements just can be brought out perinatal stage depression.Pregnant and lyingin women's depressive emotion is usually in trimester of pregnancy, and especially 1 year late trimester of pregnancy to puerperal, the depressive emotion therefore this particular time being occurred calls perinatal stage depression.According to the research at CDC mother and child care center, show, in China, term depression prevalence up to 1020%.Since 2003, the annual Population Birth number of China basicly stable with 1,600 ten thousand left and right, wherein, within 2010, Population Birth number is 1,588 ten thousand, within 2011, Population Birth number is 1,604 ten thousand, and Population Birth number in 2012 is 1,635 ten thousand (Data Sources: the 6th census in the whole nation).China's perinatal stage depression is a problem that is worth attaching great importance to.Main Basis medical history, mental symptom inspection are determined in the diagnosis of perinatal stage depression, and consider in conjunction with the rule of course advancement.At present clinical common depression assessment scale has: anxiety, self rating depressive scale, Edinburgh postnatal depression scale and selfdefined scale.Domestic conventional postpartum depression self evaluation scale is Edinburgh postnatal depression scale (Edinburgh Postnatal Depression Scale, EDPS).According to the injury of international disease and Clasification standard the tenth edition, China's patients with postpartum depression great majority are slight depression, and minority is moderate depressive patients, rarely found severe depression patient.The survey showed that after 6 weeks in childbirth to take Edinburg depression scale: postpartum depression incidence rate is 22%, and wherein patients with mild is 90.6%, and moderate is 9.4%.Different from other medical domains, almost place one's entire reliance upon patient's medical history of mental sickness states to assess its order of severity whether ill and disease.This assessment mode is subject to manysided impact, and as patient's mental status, ability to express, the situation that even exists oneself to cover up, prior art exists following shortcoming with not enough:
1, scale test is low with psychological inquiring efficiency, can not meet current huge and everincreasingly treat examination crowd.
2, scale appraisal result can not accurately reflect tester's psychologic status, may have the subjective situation that conceals the state of an illness.
3, the scoring of single rating scale can not be used for making a definite diagnosis depression.
4, the diagnostic result of shrink can be subject to the impact of self subjective factors and actual clinical experience.
Summary of the invention
Primary and foremost purpose of the present invention is that the shortcoming that overcomes prior art, with not enough, provides a kind of evaluating system of the depression based on heart rate variance analyzing method, and this evaluating system is a kind of perinatal stage depression evaluating system.
The shortcoming that another object of the present invention is to overcome prior art, with not enough, provides a kind of appraisal procedure of evaluating system of the depression based on heart rate variance analyzing method, and this appraisal procedure can realize fast, objective diagnosis perinatal stage depression grade.
Primary and foremost purpose of the present invention is achieved through the following technical solutions: a kind of evaluating system of the depression based on heart rate variance analyzing method, comprise: data are preserved module, peak detection block, Data correction module, obtain heart rate variability curve module, HRV analysis module, selects characteristic parameter module, model building module, model application module.
Data are preserved module for recording test process pregnant and lyingin women's electrocardiosignal and pulse wave signal, and electrocardiosignal is preserved into electrocardiosignal file and pulse wave signal file with pulse wave signal with binary data form.Described test process comprises: quiescent condition, deep breathing state, Valsalva maneuver and standing activities one of four states, record electrocardio and pulse wave data under different conditions, each state recording duration 2 minutes.Described quiescent condition refers to that tester sits quietly 2 minutes, keeps eupnea; Described deep breathing state be take 5 second aspiratory action exhale action in 5 second be a breathing cycle, repeat 12 times; Described Valsalva maneuver is with deeply airbreathing, holds one's breath for 15 seconds, and then forced expiration loosens 15 seconds is an action cycle, repeats four times.Described standing state refers to that tester becomes erectposition from seat, keeps standing state 2 minutes, and eupnea, avoids body action.
Peak detection block is for obtaining the peak point sequence of electrocardiosignal and pulse wave signal, and described peak point refers to the peak of each heart beat cycle electrocardiosignal and pulse wave signal waveform, and the interval of adjacent peak point is a heart beat cycle.
Data correction module is for proofreading and correct the data of peak value point sequence, removes nonhole cardiac signal.Described nonhole cardiac signal comprises: the short RR interval producing due to ectopic beat and follow Long RR interval thereafter; When peak value detects, due to the undetected Long RR interval causing (being actually the RR interval sum of two above heartbeats); While detecting due to artifact or peak value, detect the too low ghost peak producing of threshold value, this will make a normal hole IBI be divided into two NN intervals, thus the rubout signal of generation.The process that Data correction module is carried out Data correction is: while there is continuously 3 hole IBIs, a hole IBI in the middle of choosing is as the NN data of interval.
Obtain the electrocardiosignal of heart rate variability curve module after for calculation correction and the hole heartbeat NN interval series of pulse wave signal, and the NN interval series data Replica obtaining is become to duration is the NN interval series of 10 minutes, the duration of each NN interval of take in sequence is the longitudinal axis, take the time that NN interval occurs is transverse axis, obtains heart rate variability linearity curve.
HRV analysis module, for calculating the parameter of heart rate variability linearity curve, comprises time domain parameter, frequency domain parameter and nonlinear parameter.
Select characteristic parameter module for calculating the characteristic parameter of all HRV parameters under 4 kinds of test modes, according to the characteristic parameter collection of best prioritization criteria and CfsSubsetEval attribute appraisal procedure acquisition heart rate variability analysis.1, selecting a heart rate variability parameter is initial attribute collection R1, the scoring of calculating current property set; 2, the heart rate variability analysis parameter that increases property set, obtains second property set R
_{2}, the scoring of calculating second property set; 3, repeating step 2, obtain i (i is positive integer) property set R
_{i}; 4, compare property set R
_{i}with R
_{i ?1}score, if the inferior R of N continuous (N is positive integer)
_{i}scoring is higher than R
_{i ?1}, get back to step 3; Otherwise calculate, finish, property set Ri is characteristic parameter collection.
Model building module carries out category of model training for use characteristic parameter, based on AdaBoost algorithm, obtains can be used in the perinatal stage depression disaggregated model of diagnosis perinatal stage depression grade.Initialization sample space is that the distribution probability of n sample is q by N the even distribution and constitution of sample
_{n0}, through training for the first time, obtain Weak Classifier h
_{1}, the accuracy rate of classification results is α
_{1}, then revise sample space, the weight of the correct sample that reduces to classify, the weight of the sample of extend assortment mistake, obtains second sample space X1, through training for the second time, obtains Weak Classifier h
_{2}, the accuracy rate of classification results is α
_{2}, through t repetition training, obtain t Weak Classifier h
_{i}, the accuracy rate of classification results is α
_{τ}, described h
_{i}={ h
_{i} i=1,2,3 ..., t}, t is positive integer, the expression formula of described Degree of Depression quantitative appraisement model is:
wherein, h
_{t}represent t Weak Classifier, α
_{t}the weight that represents t Weak Classifier; p
_{i}weight for each grader of calculating according to the accuracy rate of grader.
Model application module, for tester's data input perinatal stage depression disaggregated model being detected, obtains current tester's depression grade.
Data are preserved module and according to data acquisition time, are divided four sections of preservations by the electrocardiosignal of collection and pulse wave signal, the corresponding test mode of every segment data, and the electrocardiosignal of preserving and pulse wave signal are inputted as the data of peak detection block; Peak detection block is obtained the peak point sequence of electrocardiosignal and pulse wave signal, and the input using it as Data correction module; Data correction module is proofreaied and correct peak point sequence, removes nonhole cardiac signal and obtains hole heartbeat NN interval series, as the input of heart rate variability linearity curve; Obtaining heart rate variability curve module, hole heartbeat NN interval series is copied into duration is the NN interval series of 10 minutes, the duration of each NN interval of take in sequence is the longitudinal axis, take the time that NN interval occurs is transverse axis, obtain heart rate variability linearity curve, input as HRV analysis module, HRV analysis module carries out timedomain analysis, frequencydomain analysis and nonlinear analysis to HRV curve, obtains HRV parameter, as the date processing basis of selecting characteristic parameter module; Select characteristic parameter module to assess the HRV parameter under four test modes, in all HRV parameters from one of four states, select characteristic parameter, the data basis of setting up as model; Model building module use characteristic parameter is carried out category of model training, based on AdaBoost algorithm, obtains can be used in the perinatal stage depression disaggregated model of diagnosis perinatal stage depression grade; Model application module is tester's data input perinatal stage depression disaggregated model to be detected, obtains current tester's depression grade.
The evaluating system of the perinatal stage depression based on heart rate variance analyzing method, also comprises with lower module: building database module, model correcting module.
Building database module is for management to all data, can complete according to keywords retrieve data, by particular community sorting data with by particular community garbled data.
Model correcting module is used for proofreading and correct the test result of current data, and result is saved in data base, the abundant sample size of setting up model use.
HRV analysis module, comprises time domain parameter computing unit, nonlinear parameter computing unit, timefrequency converting unit and frequency domain parameter computing unit.
Time domain parameter computing unit for according to the NN interval series of HRV curve, calculates following five time domain parameter: MEAN, SDNN, RMSSD, pNN50 and SDSD, and described MEAN is all hole NN average of interval; Described SDNN is all hole NN standard deviation of interval, and described RMSSD is the rootmeansquare of adjacent NN interval difference, and described pNN50 is the ratios of the above adjacent NN interval difference of 50 ms intervals, and described SDSD is the standard deviation of adjacent NN between interval.
The data object of nonlinear parameter computing unit is HRV curve, for calculating following nonlinear parameter: SD1, SD2, α
_{1}and α
_{2}; Described SD2 is the scatterplot of the HRV curve the longest distance between two points in scatterplot region in X=Y direction, and described SD1 is the distance perpendicular to the longest pointtopoint transmission in scatterplot region in X=Y direction, described α
_{1}for HRV curve removes the slope of trend fluction analysis first (1st～11 points) fitting a straight line, described α
_{2}for HRV curve removes the slope of trend fluction analysis second portion (the 12nd point is to last point) fitting a straight line.
Timefrequency converting unit is for resampling to HRV curve, then section length and overlapped data length are set, every segment data is carried out to windowing process, described window function is Hamming window, calculate the power spectrum of every segment data, the power spectrum of each segment data is integrated into the power spectrum of all HRV curves.
Frequency domain parameter computing unit is used for calculating frequency domain parameter, and described frequency domain parameter comprises: VLF, LF, HF, TP, pVLF, pLF, pHF, nLF, nHF and LF/HF.Described VLF is the power of extremely low frequency composition 0.0033～0.04Hz, and described LF is the power of lowfrequency component 0.04～0.15Hz; Described HF is the power of radiofrequency component 0.15～0.4Hz; Described TP is general power, described pVLF is the percentage ratio of heart rate variability linearity curve extremely low frequency composition, described pLF is the percentage ratio of heart rate variability linearity curve lowfrequency component, described pHF is the percentage ratio of heart rate variability linearity curve radiofrequency component, described nLF is normalized low frequency power, described nHF is normalized radiofrequency component, and described LF/HF is the ratio of lowfrequency component and radiofrequency component.
Another object of the present invention is achieved through the following technical solutions: the appraisal procedure of the perinatal stage depression based on heart rate variance analyzing method, comprises the following steps:
Step 1, data are preserved.Record electrocardiosignal and the pulse wave signal of pregnant and lyingin women in test process, and electrocardiosignal is preserved into electrocardiosignal file and pulse wave signal file with pulse wave signal with binary data form.Described test process comprises: quiescent condition, deep breathing state, Valsalva maneuver and standing activities one of four states, record electrocardio and pulse wave data under different conditions, each state recording duration 2 minutes.Described quiescent condition refers to that tester sits quietly 2 minutes, keeps eupnea; Described deep breathing state be take 5 second aspiratory action exhale action in 5 second be a breathing cycle, repeat 12 times; Described Valsalva maneuver is with deeply airbreathing, holds one's breath for 15 seconds, and then forced expiration loosens 15 seconds is an action cycle, repeats four times.Described standing state refers to that tester becomes erectposition from seat, keeps standing state 2 minutes, and eupnea, avoids body action.
Step 2, peak value detect.Obtain the peak point sequence of electrocardiosignal and pulse wave signal, described peak point refers to the peak of each heart beat cycle electrocardiosignal and pulse wave signal waveform, and the interval of adjacent peak point is a heart beat cycle.
Step 3, Data correction.Proofread and correct the data of peak value point sequence, remove nonhole cardiac signal.Described nonhole cardiac signal comprises: the short RR interval producing due to ectopic beat and follow Long RR interval thereafter; When peak value detects, due to the undetected Long RR interval causing (being actually the RR interval sum of two above heartbeats); While detecting due to artifact or peak value, detect the too low ghost peak producing of threshold value, this will make a normal hole IBI be divided into two NN intervals, thus the rubout signal of generation.The process that Data correction module is carried out Data correction is: while there is continuously 3 hole IBIs, a hole IBI in the middle of choosing is as the NN data of interval.
Step 4, obtain heart rate variability linearity curve.The hole heartbeat NN interval series of the electrocardiosignal after calculation correction and pulse wave signal, and the NN interval series data Replica obtaining is become to duration is the NN interval series of 10 minutes, the duration of each NN interval of take in sequence is the longitudinal axis, take the time that NN interval occurs is transverse axis, obtains heart rate variability linearity curve.
Step 5, HRV analyze.The parameter of calculating heart rate variability linearity curve, comprises time domain parameter, frequency domain parameter and nonlinear parameter.
Step 6, selection characteristic parameter.Calculate the characteristic parameter of all HRV parameters under 4 kinds of test modes, according to the characteristic parameter collection of best prioritization criteria and CfsSubsetEval attribute appraisal procedure acquisition heart rate variability analysis.1, selecting a heart rate variability parameter is initial attribute collection R1, the scoring of calculating current property set; 2, the heart rate variability analysis parameter that increases property set, obtains second property set R
_{2}, the scoring of calculating second property set; 3, repeating step 2, obtain i (i is positive integer) property set R
_{i}; 4, compare property set R
_{i}with R
_{i ?1}score, if the inferior R of N continuous (N is positive integer)
_{i}scoring is higher than R
_{i ?1}, get back to step 3; Otherwise calculate, finish, property set Ri is characteristic parameter collection.
Step 7, model are set up.Use characteristic parameter is carried out category of model training, based on AdaBoost algorithm, obtains can be used in the perinatal stage depression disaggregated model of diagnosis perinatal stage depression grade.Initialization sample space is that the distribution probability of n sample is q by N the even distribution and constitution of sample
_{n0}, through training for the first time, obtain Weak Classifier h
_{1}, the accuracy rate of classification results is α
_{1}, then revise sample space, the weight of the correct sample that reduces to classify, the weight of the sample of extend assortment mistake, obtains second sample space X1, through training for the second time, obtains Weak Classifier h
_{2}, the accuracy rate of classification results is α
_{2}, through t repetition training, obtain t Weak Classifier h
_{i}, the accuracy rate of classification results is α
_{τ}, described hi={hii=1,2,3 ..., t}, t is positive integer, the expression formula of described Degree of Depression quantitative appraisement model is:
wherein, h
_{t}represent t Weak Classifier, α
_{t}the weight that represents t Weak Classifier; p
_{i}weight for each grader of calculating according to the accuracy rate of grader.
Step 8, model application.To tester's data input perinatal stage depression disaggregated model be detected, obtain current tester's depression grade.
Step 9, building database.Data management, can complete according to keywords retrieve data, by particular community sorting data with by particular community garbled data.The conveniently management to all data in system of data base, practicality and the operability of increase system.
Step 10, model correction.Proofread and correct the test result of current data, and result is saved in data base.Model tuning can increase the training sample of perinatal stage depression model, can enrich and set up the sample size that model is used, and increases the feature of different brackets data in model, is conducive to improve the accuracy rate of category of model.
Calculate heart rate variability analysis parameter, comprise the following steps:
Step 51, time domain parameter calculate.According to the NN interval series of HRV curve, calculate following five time domain parameter: MEAN, SDNN, RMSSD, pNN50 and SDSD, described MEAN is all hole NN average of interval; Described SDNN is all hole NN standard deviation of interval, and described RMSSD is the rootmeansquare of adjacent NN interval difference, and described pNN50 is the ratios of the above adjacent NN interval difference of 50 ms intervals, and described SDSD is the standard deviation of adjacent NN between interval.
Step 52, nonlinear parameter calculate.Calculate following nonlinear parameter: SD1, SD2, α
_{1}and α
_{2}; Described SD2 is the scatterplot of the HRV curve the longest distance between two points in scatterplot region in X=Y direction, and described SD1 is the distance perpendicular to the longest pointtopoint transmission in scatterplot region in X=Y direction, described α
_{1}for HRV curve removes the slope of trend fluction analysis first (1st～11 points) fitting a straight line, described α
_{2}for HRV curve removes the slope of trend fluction analysis second portion (the 12nd point is to last point) fitting a straight line.
Step 53, timefrequency conversion.HRV curve is resampled, then section length and overlapped data length are set, every segment data is carried out to windowing process, described window function is Hamming window, calculate the power spectrum of every segment data, the power spectrum of each segment data is integrated into the power spectrum of all HRV curves.
Step 54, frequency domain parameter calculate.Calculate frequency domain parameter, described frequency domain parameter comprises: VLF, LF, HF, TP, pVLF, pLF, pHF, nLF, nHF and LF/HF.Described VLF is the power of extremely low frequency composition 0.0033～0.04Hz, and described LF is the power of lowfrequency component 0.04～0.15Hz; Described HF is the power of radiofrequency component 0.15～0.4Hz; Described TP is general power, described pVLF is the percentage ratio of heart rate variability linearity curve extremely low frequency composition, described pLF is the percentage ratio of heart rate variability linearity curve lowfrequency component, described pHF is the percentage ratio of heart rate variability linearity curve radiofrequency component, described nLF is normalized low frequency power, described nHF is normalized radiofrequency component, and described LF/HF is the ratio of lowfrequency component and radiofrequency component.
The present invention has following advantage and effect with respect to prior art:
1, realize perinatal stage Degree of Depression quantitative evaluation, avoided pregnant and lyingin women to use subjectivity and the polytropy of scale test.
2, enrich depression and based on physiologic information, checked the scientific research methods of technical field.
3, only need to obtain the electrocardiograph pulse wave datum of testee, test simple and easy to doly, facilitate pregnant and lyingin women to test examination, can effectively save medical resource, can have good Clinical practicability; Can realize quick, objective diagnosis perinatal stage depression grade.
Accompanying drawing explanation
Fig. 1 is system module figure.
Fig. 2 is peak value overhaul flow chart.
Fig. 3 is timefrequency flow path switch figure.
Fig. 4 characteristic parameter and model Establishing process figure.
The specific embodiment
Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited to this.
Embodiment
The present embodiment is the electrocardio under different conditions and pulse wave data with 92 routine pregnant and lyingin women, as the training sample of perinatal stage depression disaggregated model.Different conditions refers to quiescent condition, deep breathing state, Valsalva maneuver and standing activities one of four states, each state recording duration 2 minutes.Described different conditions refers to quiescent condition, deep breathing state, Valsalva maneuver and standing activities one of four states, records electrocardio and pulse wave data under different conditions, each state recording duration 2 minutes.Quiescent condition refers to that tester sits quietly 2 minutes, keeps eupnea; Deep breathing state be take 5 second aspiratory action exhale action in 5 second be a breathing cycle, repeat 12 times; Valsalva maneuver is with deeply airbreathing, holds one's breath for 15 seconds, and then forced expiration loosens 15 seconds is an action cycle, repeats four times.Standing state refers to that tester becomes erectposition from seat, keeps standing state 2 minutes, and eupnea, avoids body action.
A kind of evaluating system of the depression based on heart rate variance analyzing method as shown in Figure 1, comprise data preservation module, peak detection block, Data correction module, obtain heart rate variability curve module, HRV analysis module, selects characteristic parameter module, model building module, model application module; Also comprise building database module, model correcting module.The concrete implementation step of appraisal procedure of the depression based on heart rate variance analyzing method of this system is as follows:
Step 1, data are preserved.Record electrocardiosignal and the pulse wave signal of pregnant and lyingin women in test process, and electrocardiosignal is preserved into electrocardiosignal file and pulse wave signal file with pulse wave signal with binary data form.
Step 2, peak value detect.Extract the electrocardio under different conditions, the peak point of pulse wave signal, the flow chart that peak value detects as shown in Figure 2.The single order differential F ' that calculates electrocardiosignal or pulse wave signal (p), secondorder differential F " (p); gather n2 the zero point of calculating secondorder differential; near (n) the set n1 of >K of F ' in set of computations n2; K is slope threshold value; electrocardiosignal K=100; pulse wave signal K=20, searches for F ' (n1+i) n1, makes F ' (n1+i)=0 invocation point be peak point.
Step 3, Data correction.Electrocardio under different conditions, pulse wave signal are carried out to Data correction, remove nonhole cardiac signal.Due to Data Detection or human body self physiological reason, can cause in electrocardio and pulse waveform and may be mixed with expense hole cardiac signal, the electrocardiograph pulse wave train of analyzing for HRV, must be by the heart beat cycle sequence due to hole heartbeat, therefore to carry out Data correction to image data, remove nonhole heartbeat.The nonhole cardiac signal of generally, sneaking in NN interval data has: the short RR interval that ectopic beat produces and follow Long RR interval thereafter; When QRS detects, due to the undetected Long RR interval causing (being actually the RR interval sum of two above heartbeats); While detecting due to artifact or QRS, detect the too low false QRS producing of threshold value, this will make the RR interval of a normal hole heartbeat be divided into two short RR intervals, thus the rubout signal producing.Described Data correction comprises: while 1, there is continuously 3 hole heartbeats, the RR interval of a heartbeat in the middle of choosing is as the NN data of interval; 2: the NN interval data of choosing by abovementioned rule occur continuously, and while reaching certain quantity, as a data segment that can be used for carrying out HRV analysis, otherwise this segment data is given up.According to abovementioned correction rule, obtain NN interval series.
Step 4, obtains heart rate variability linearity curve, by NN interval series long data when total that is copied into 10 minutes, obtains 10 minutes heart rate variability linearity curves.Original data sequence is 2 minute datas of detection record under different conditions, the total duration of NN interval series after peak value detection and Data correction is less than or equals 2 minutes, on this basis NN interval data sequence is copied into the data sequence of 10 minutes durations, NN interval series with 10 minutes obtains heart rate variability linearity curve, the basis of processing as followup data.
Step 5, HRV analyzes.Calculate heart rate variability analysis parameter.Heart rate variability analysis parameter comprises time domain parameter, frequency domain parameter, nonlinear parameter.
Time domain parameter comprises: MEAN, SDNN, RMSSD, pNN50 and SDSD, and described MEAN is all hole NN average of interval; SDNN is all hole NN standard deviation of interval,
n is the number of NN interval in 10 minutes HRV curves, and i is the sequence number of order; Described RMSSD is the rootmeansquare of adjacent NN interval difference,
Δ NNi is the poor of two adjacent NN intervals, and described pNN50 is the ratios of the above adjacent NN interval difference of 50 ms intervals, and SDSD is the standard deviation of adjacent NN between interval,
mean Δ NN is the meansigma methods of all adjacent NN interval differences;
NN interval data sequence is resampled and timefrequency conversion, its flow chart as shown in Figure 3: 1, the sample frequency that timefrequency changes being set is Fs=N/600, and wherein N is the number of NN interval series in 10 minutes HRV curves; 2, to the HRV curve segmentation of 10 minutes, segment length L=512, overlap length nov=400, segments n is the positive integer that is not more than N/L, every segment data is designated as X
_{i}; 3, to data segment X
_{i}windowing process, obtains Xwi=X
_{i}* W, take Hamming window as example, window function expression formula W=0.54 ?0.46*cos (2*pi*x), x={i/ (m ?1)  i is positive integer, i<L/2}; 4, to each data segment rated output spectrum and each section of spectrum component, must power spectrum be all sections of power spectrum and.Frequency domain parameter comprises: VLF, LF, HF, TP, pVLF, pLF, pHF, nLF, nHF and LF/HF.VLF is the power of extremely low frequency composition 0.0033～0.04Hz, and LF is the power of lowfrequency component 0.04～0.15Hz; HF is the power of radiofrequency component 0.15～0.4Hz; TP is general power, pVLF is the percentage ratio of heart rate variability linearity curve extremely low frequency composition, pLF is the percentage ratio of heart rate variability linearity curve lowfrequency component, pHF is the percentage ratio of heart rate variability linearity curve radiofrequency component, nLF is normalized low frequency power, nLF=LF/ (TP ?VLF) * 100%, and nHF is normalized radiofrequency component, nHF=HF/ (TP ?VLF) * 100%, LF/HF is the ratio of lowfrequency component and radiofrequency component;
Described nonlinear parameter comprises SD1, SD2, α
_{1}and α
_{2}; Described SD2 is the scatterplot region the longest distance between two points of scatterplot in X=Y direction, and SD1 is the distance perpendicular to the longest pointtopoint transmission in scatterplot region in X=Y direction, described α
_{1}for HRV curve removes the slope of trend fluction analysis first (1st～11 points) fitting a straight line, described α
_{2}for HRV curve removes the slope of trend fluction analysis second portion (the 12nd point is to last point) fitting a straight line.
Step 6, selection characteristic parameter.According to the characteristic parameter collection of the greedy search expansion that can recall and CfsSubsetEval attribute appraisal procedure acquisition heart rate variability analysis.1, selecting a heart rate variability parameter is initial attribute collection R1, the scoring of calculating current property set; 2, the heart rate variability analysis parameter that increases property set, obtains second property set R
_{2}, the scoring of calculating second property set; 3, repeating step 2, obtain i (i is positive integer) property set R
_{i}; 4, compare property set R
_{i}with R
_{i1}score, if the inferior R of N continuous (N is positive integer)
_{i}scoring is higher than R
_{i1}, get back to step 3; Otherwise calculate, finish, property set Ri is characteristic parameter collection.92 sample sets of take are experimental example, and the characteristic parameter collection getting in said process is, tranquillization state parameter: pNN50, LF, HF, TP, α
_{1}; Deep breathing state data: RMSSD, pNN50, VLF, LF, α
_{1}; Wa Er Salva state parameter: pNN50, TP, pVLF, nLF, nHF; The state of standing parameter: Mean.
Step 7, model are set up.Initialization sample space is that the distribution probability of n sample is q by 92 even distribution and constitutions of sample
_{n0}, through training for the first time, obtain Weak Classifier h
_{1}, the accuracy rate of classification results is α
_{1}, then revise sample space, the weight of the correct sample that reduces to classify: q
_{r1}=A
_{r}q
_{r0}, r is the sample number of correct classification, the weight that Ar is r sample, Ar<1; The weight of the sample of extend assortment mistake: q
_{w1}=A
_{w}q
_{w0}, the sample number that w is misclassification, the weight that Aw is w sample, Aw>1; And
obtain second sample space X1, through training for the second time, obtain Weak Classifier h
_{2}, the accuracy rate of classification results is α
_{2}, through t repetition training, obtain t Weak Classifier h
_{i}, the accuracy rate of classification results is α
_{τ}, described hi={hii=1,2,3 ..., t}, t is positive integer, the expression formula of described Degree of Depression quantitative appraisement model is:
Wherein, h
_{t}represent t Weak Classifier, α
_{t}the weight that represents t Weak Classifier; p
_{i}weight for each grader of calculating according to the accuracy rate of grader.
Select the detailed process of characteristic parameter and model foundation as shown in Figure 4.
Step 8, model application.Data input model, obtains diagnostic result.Data are inputted to grader H, obtain the classification results of data.
Step 9, building database.Data management, can complete according to keywords retrieve data, by particular community sorting data with by particular community garbled data.The conveniently management to all data in system of data base, practicality and the operability of increase system.
Step 10, model correction.Proofread and correct the test result of current data, and result is saved in data base.Model tuning can increase the training sample of perinatal stage depression model, can enrich and set up the sample size that model is used, and increases the feature of different brackets data in model, is conducive to improve the accuracy rate of category of model.
Implementation process has designed three groups of contrast tests, first group adopts 54 pregnant and lyingin women's sample data as the training sample set of perinatal stage depression model, its accuracy rate of the model obtaining is 63.2%, second group adopts 71 pregnant and lyingin women's sample data as the training sample set of perinatal stage depression model, its accuracy rate of the model obtaining is 78.87%, the 3rd group adopts 92 pregnant and lyingin women's sample data as the training sample set of perinatal stage depression model, and its accuracy rate of the model obtaining is 82.5%.As can be seen here, if continue to increase model training collection in followup test, the accuracy rate of model can continue to improve.
The evaluating system of a kind of depression based on heart rate variance analyzing method disclosed by the invention and method, a kind of new research method is proposed in perinatal stage depression diagnosis and detection field, the method energy science, objective assessment pregnant and lyingin women's depressive state grade, can effectively assist and clinical pregnant and lyingin women be carried out to the examination of perinatal stage depression, prevention, discovery perinatal stage depression are had to very large meaning, can effectively reduce the clinical onset rate of perinatal stage depression, reduce the injury that depression causes Perinatal Women and family thereof, there is generalization and Clinical practicability.
Abovedescribed embodiment is preferably embodiment of the present invention; but embodiments of the present invention are not restricted to the described embodiments; other any do not deviate from change, the modification done under spirit of the present invention and principle, substitutes, combination, simplify; all should be equivalent substitute mode, within being included in protection scope of the present invention.
Claims (6)
1. the evaluating system of the depression based on heart rate variance analyzing method, it is characterized in that, comprise data preservation module, peak detection block, Data correction module, obtains heart rate variability curve module, HRV analysis module, select characteristic parameter module, model building module, model application module;
Data are preserved module for recording test process pregnant and lyingin women's electrocardiosignal and pulse wave signal, and preserve into file with binary data form; Described test process comprises: quiescent condition, deep breathing state, Valsalva maneuver and standing activities one of four states, each state recording duration 2 minutes; Described quiescent condition refers to that tester sits quietly 2 minutes, keeps eupnea; Described deep breathing state be take 5 second aspiratory action exhale action in 5 second be a breathing cycle, repeat 12 times; Described Valsalva maneuver is with deeply airbreathing, holds one's breath for 15 seconds, and then forced expiration loosens 15 seconds is an action cycle, repeats four times; Described standing state refers to that tester becomes erectposition from seat, keeps standing state 2 minutes, and eupnea, avoids body action;
Peak detection block is for obtaining the peak point sequence of electrocardiosignal and pulse wave signal, and described peak point refers to the peak of each heart beat cycle electrocardiosignal and pulse wave signal waveform, and the interval of adjacent peak point is a heart beat cycle;
The data of Data correction module for proofreading and correct peak value point sequence, remove nonhole cardiac signal; Described nonhole cardiac signal comprises:
(1) the short RR interval producing due to ectopic beat and follow Long RR interval thereafter;
(2) when peak value detects, due to the undetected Long RR interval causing;
(3) during due to artifact or peak value detection, detect the too low ghost peak producing of threshold value; The process that Data correction module is carried out Data correction is: while there is continuously 3 hole IBIs, a hole IBI in the middle of choosing is as the NN data of interval;
Obtain heart rate variability curve module for calculating hole heartbeat NN interval series, and the NN interval series obtaining is copied into duration is the NN interval series of 10 minutes, the duration of each NN interval of take in sequence is the longitudinal axis, and take the time that NN interval occurs is transverse axis, obtains heart rate variability linearity curve;
HRV analysis module, for calculating the parameter of heart rate variability linearity curve, comprises time domain parameter, frequency domain parameter and nonlinear parameter;
Select characteristic parameter module for calculating the characteristic parameter of all HRV parameters under 4 kinds of test modes, described characteristic parameter refers to the parameter of selecting through attribute appraisal procedure and best prioritization criteria iterative algorithm;
Model building module carries out category of model training for use characteristic parameter, based on AdaBoost algorithm, obtains can be used in the perinatal stage depression disaggregated model of diagnosis perinatal stage depression grade;
Model application module, for tester's data input perinatal stage depression disaggregated model being detected, obtains current tester's depression grade;
Data are preserved module and according to data acquisition time, are divided four sections of preservations by the electrocardiosignal of collection and pulse wave signal, and the corresponding test mode of every segment data, as the data input of peak detection block; Peak detection block is obtained peak point sequence, and the input using it as Data correction module; Data correction module is removed nonhole cardiac signal in peak point sequence and is obtained hole heartbeat NN interval series, as the input of heart rate variability linearity curve; Obtaining heart rate variability curve module, hole heartbeat NN interval series is copied into duration is the NN interval series of 10 minutes, and the duration of each NN interval of take in sequence is the longitudinal axis, and take the time that NN interval occurs is transverse axis, obtains heart rate variability linearity curve; HRV analysis module carries out timedomain analysis, frequencydomain analysis and nonlinear analysis to HRV curve, obtains HRV parameter; Select characteristic parameter module to assess the HRV parameter under four test modes, select characteristic parameter; Model building module use characteristic parameter is carried out category of model training, based on AdaBoost algorithm, obtains can be used in the perinatal stage depression disaggregated model of diagnosis perinatal stage depression grade; Model application module is tester's data input perinatal stage depression disaggregated model to be detected, obtains current tester's depression grade.
2. the evaluating system of the depression based on heart rate variance analyzing method according to claim 1, is characterized in that, also comprises with lower module: building database module, model correcting module;
Building database module is for management to all data, can complete according to keywords retrieve data, by particular community sorting data with by particular community garbled data;
Model correcting module is used for proofreading and correct the test result of current data, and result is saved in data base, the abundant sample size of setting up model use.
3. the evaluating system of the depression based on heart rate variance analyzing method according to claim 1, it is characterized in that, described HRV analysis module, comprises time domain parameter computing unit, nonlinear parameter computing unit, timefrequency converting unit and frequency domain parameter computing unit;
Time domain parameter computing unit for according to the NN interval series of HRV curve, calculates following five time domain parameter: MEAN, SDNN, RMSSD, pNN50 and SDSD, and described MEAN is all hole NN average of interval; Described SDNN is all hole NN standard deviation of interval, and described RMSSD is the rootmeansquare of adjacent NN interval difference, and described pNN50 is the ratios of the above adjacent NN interval difference of 50 ms intervals, and described SDSD is the standard deviation of adjacent NN between interval;
The data object of nonlinear parameter computing unit is HRV curve, for calculating following nonlinear parameter: SD1, SD2, α
_{1}and α
_{2}; Described SD2 is the scatterplot of the HRV curve the longest distance between two points in scatterplot region in X=Y direction, and described SD1 is the distance perpendicular to the longest pointtopoint transmission in scatterplot region in X=Y direction, described α
_{1}for HRV curve removes the slope of trend fluction analysis first fitting a straight line, described α
_{2}for HRV curve removes the slope of trend fluction analysis second portion fitting a straight line; , described first refers to 1st～11 points, described second portion refers to that the 12nd point is to last point;
Timefrequency converting unit is for resampling to HRV curve, then section length and overlapped data length are set, every segment data is carried out to windowing process, described window function is Hamming window, calculate the power spectrum of every segment data, the power spectrum of each segment data is integrated into the power spectrum of all HRV curves;
Frequency domain parameter computing unit is used for calculating frequency domain parameter, and described frequency domain parameter comprises: VLF, LF, HF, TP, pVLF, pLF, pHF, nLF, nHF and LF/HF; Described VLF is the power of extremely low frequency composition 0.0033～0.04Hz, and described LF is the power of lowfrequency component 0.04～0.15Hz; Described HF is the power of radiofrequency component 0.15～0.4Hz; Described TP is general power, described pVLF is the percentage ratio of heart rate variability linearity curve extremely low frequency composition, described pLF is the percentage ratio of heart rate variability linearity curve lowfrequency component, described pHF is the percentage ratio of heart rate variability linearity curve radiofrequency component, described nLF is normalized low frequency power, described nHF is normalized radiofrequency component, and described LF/HF is the ratio of lowfrequency component and radiofrequency component.
4. an appraisal procedure for the evaluating system of the depression based on heart rate variance analyzing method described in claim 1, is characterized in that, comprises the following steps:
Step 1, data are preserved; Record electrocardiosignal and the pulse wave signal of pregnant and lyingin women in test process, and preserve into file with binary data form; Described test process comprises: quiescent condition, deep breathing state, Valsalva maneuver and standing activities one of four states, each state recording duration 2 minutes; Described quiescent condition refers to that tester sits quietly 2 minutes, keeps eupnea; Described deep breathing state be take 5 second aspiratory action exhale action in 5 second be a breathing cycle, repeat 12 times; Described Valsalva maneuver is with deeply airbreathing, holds one's breath for 15 seconds, and then forced expiration loosens 15 seconds is an action cycle, repeats four times; Described standing state refers to that tester becomes erectposition from seat, keeps standing state 2 minutes, and eupnea, avoids body action;
Step 2, peak value detect; Obtain the peak point sequence of electrocardiosignal and pulse wave signal, described peak point refers to the peak of each heart beat cycle electrocardiosignal and pulse wave signal waveform, and the interval of adjacent peak point is a heart beat cycle;
Step 3, Data correction; Proofread and correct the data of peak value point sequence, remove nonhole cardiac signal; Described nonhole cardiac signal comprises:
(1) the short RR interval producing due to ectopic beat and follow Long RR interval thereafter;
(2) when peak value detects, due to the undetected Long RR interval causing;
(3) during due to artifact or peak value detection, detect the too low ghost peak producing of threshold value; The process that Data correction module is carried out Data correction is: while there is continuously 3 hole IBIs, a hole IBI in the middle of choosing is as the NN data of interval;
Step 4, obtain heart rate variability linearity curve; The hole heartbeat NN interval series of the electrocardiosignal after calculation correction and pulse wave signal, and the NN interval series data Replica obtaining is become to duration is the NN interval series of 10 minutes, the duration of each NN interval of take in sequence is the longitudinal axis, take the time that NN interval occurs is transverse axis, obtains heart rate variability linearity curve;
Step 5, HRV analyze; The parameter of calculating heart rate variability linearity curve, the parameter of described heart rate variability linearity curve comprises time domain parameter, frequency domain parameter and nonlinear parameter;
Step 6, selection characteristic parameter; Select the characteristic parameter of all HRV parameters under 4 kinds of test modes, described characteristic parameter refers to the parameter of selecting through attribute appraisal procedure and best prioritization criteria iterative algorithm;
Step 7, model are set up; Use characteristic parameter is carried out category of model training, based on AdaBoost algorithm, obtains can be used in the perinatal stage depression disaggregated model of diagnosis perinatal stage depression grade;
Step 8, model application; To tester's data input perinatal stage depression disaggregated model be detected, obtain current tester's depression grade.
5. appraisal procedure according to claim 4, is characterized in that, further comprising the steps of:
Steps A, building database; Management to all data, can complete according to keywords retrieve data, by particular community sorting data with by particular community garbled data;
Step B, model correction; Proofread and correct the test result of current data, and result is saved in data base, the abundant sample size of setting up model use.
6. an appraisal procedure claimed in claim 4, is characterized in that, in step 5, the method for calculating the parameter of described heart rate variability linearity curve comprises the following steps:
Step 51, time domain parameter calculate; According to the NN interval series of HRV curve, calculate following five time domain parameter: MEAN, SDNN, RMSSD, pNN50 and SDSD, described MEAN is all hole NN average of interval, described SDNN is all hole NN standard deviation of interval, described RMSSD is the rootmeansquare of adjacent NN interval difference, described pNN50 is the ratio of the above adjacent NN interval difference of 50 ms intervals, and described SDSD is the standard deviation of adjacent NN between interval;
Step 52, nonlinear parameter calculate; Calculate following nonlinear parameter: SD1, SD2, α
_{1}and α
_{2}; Described SD2 is the scatterplot of the HRV curve the longest distance between two points in scatterplot region in X=Y direction, and described SD1 is the distance perpendicular to the longest pointtopoint transmission in scatterplot region in X=Y direction, described α
_{1}for HRV curve removes the slope of trend fluction analysis first fitting a straight line, described α
_{2}for HRV curve removes the slope of trend fluction analysis second portion fitting a straight line; Described first refers to 1st～11 points, and described second portion refers to that the 12nd point is to last point;
Step 53, timefrequency conversion; HRV curve is resampled, then section length and overlapped data length are set, every segment data is carried out to windowing process, described window function is Hamming window, calculate the power spectrum of every segment data, the power spectrum of each segment data is integrated into the power spectrum of all HRV curves;
Step 54, frequency domain parameter calculate; Calculate frequency domain parameter, described frequency domain parameter comprises: VLF, LF, HF, TP, pVLF, pLF, pHF, nLF, nHF and LF/HF; Described VLF is the power of extremely low frequency composition 0.0033～0.04Hz, and described LF is the power of lowfrequency component 0.04～0.15Hz; Described HF is the power of radiofrequency component 0.15～0.4Hz; Described TP is general power, described pVLF is the percentage ratio of heart rate variability linearity curve extremely low frequency composition, described pLF is the percentage ratio of heart rate variability linearity curve lowfrequency component, described pHF is the percentage ratio of heart rate variability linearity curve radiofrequency component, described nLF is normalized low frequency power, described nHF is normalized radiofrequency component, and described LF/HF is the ratio of lowfrequency component and radiofrequency component.
Priority Applications (1)
Application Number  Priority Date  Filing Date  Title 

CN201410334899.0A CN104127194B (en)  20140714  20140714  A kind of evaluating system of the depression based on heart rate variance analyzing method 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

CN201410334899.0A CN104127194B (en)  20140714  20140714  A kind of evaluating system of the depression based on heart rate variance analyzing method 
Publications (2)
Publication Number  Publication Date 

CN104127194A true CN104127194A (en)  20141105 
CN104127194B CN104127194B (en)  20160504 
Family
ID=51800236
Family Applications (1)
Application Number  Title  Priority Date  Filing Date 

CN201410334899.0A CN104127194B (en)  20140714  20140714  A kind of evaluating system of the depression based on heart rate variance analyzing method 
Country Status (1)
Country  Link 

CN (1)  CN104127194B (en) 
Cited By (18)
Publication number  Priority date  Publication date  Assignee  Title 

CN104523263A (en) *  20141223  20150422  华南理工大学  Mobile internet based pregnant and lyingin woman health surveillance system 
CN104983434A (en) *  20150617  20151021  重庆邮电大学  Multiparameter mental stress assessment method based on analytical hierarchy method and device 
CN105022929A (en) *  20150807  20151104  北京环度智慧智能技术研究所有限公司  Cognition accuracy analysis method for personality trait value test 
CN105147248A (en) *  20150730  20151216  华南理工大学  Physiological informationbased depressive disorder evaluation system and evaluation method thereof 
CN105938520A (en) *  20160415  20160914  深圳市德力凯医疗设备股份有限公司  Data processing method and system for foaming test check 
CN105997019A (en) *  20160509  20161012  鲍崇智  A body sensor networkbased multidimensional heartbeat information synchronous collection method and system 
CN106037705A (en) *  20160523  20161026  韩剑  Data detection method and system based on wearable equipment 
CN106073765A (en) *  20160527  20161109  成都信汇聚源科技有限公司  Die suddenly in a kind of miniature holter devices the implementation method of risk profile 
CN106175742A (en) *  20160719  20161207  北京心量科技有限公司  A kind of heart sign acquisition methods and device 
CN106326644A (en) *  20160816  20170111  沈阳东软熙康医疗系统有限公司  Method and device for calculating HRV (heart rate variability) parameters and fatigue indexes 
CN106419937A (en) *  20160912  20170222  南京邮电大学  Mental stress analysis system based on heart sound HRV theory 
CN107088061A (en) *  20170504  20170825  太原理工大学  A kind of HRV online analysis systems and its method based on Shewhart control figures 
CN108366763A (en) *  20150615  20180803  Medibio有限公司  Method and system for assessing the state of mind 
CN108577804A (en) *  20180202  20180928  西北工业大学  A kind of BCG signal analysis methods and system towards hypertensive patient's monitoring 
CN108652613A (en) *  20170330  20181016  深圳市理邦精密仪器股份有限公司  The method and device that signal timefrequency figure generates 
CN109077714A (en) *  20180705  20181225  广州视源电子科技股份有限公司  Signal recognition method, device, equipment and storage medium 
CN109394197A (en) *  20181127  20190301  中山大学  A kind of heart rate variability measurement method, device and equipment based on time frequency analysis 
WO2019100561A1 (en) *  20171127  20190531  乐普（北京）医疗器械股份有限公司  Artificial intelligencebased interference recognition method for electrocardiogram 
Citations (3)
Publication number  Priority date  Publication date  Assignee  Title 

CN101337103A (en) *  20080828  20090107  北京阳光易德科技发展有限公司  Pressure and emotion adjustment method, system and a physiology signal acquisition device 
CN101642369A (en) *  20080804  20100210  南京大学  Autonomic nervous function biological feedback method and system 
WO2012156427A1 (en) *  20110518  20121122  Alain Gilles Muzet  System and method for determining sleep and sleep stages of a person 

2014
 20140714 CN CN201410334899.0A patent/CN104127194B/en active IP Right Grant
Patent Citations (3)
Publication number  Priority date  Publication date  Assignee  Title 

CN101642369A (en) *  20080804  20100210  南京大学  Autonomic nervous function biological feedback method and system 
CN101337103A (en) *  20080828  20090107  北京阳光易德科技发展有限公司  Pressure and emotion adjustment method, system and a physiology signal acquisition device 
WO2012156427A1 (en) *  20110518  20121122  Alain Gilles Muzet  System and method for determining sleep and sleep stages of a person 
NonPatent Citations (3)
Title 

CARMILLA M.M.LICHT ET.AL: "Association between anxiety disorders and Heart Rate Variability in the Netherlands Study of Depression and Anxiety(NESDA)", 《PSYCHOSOMATIC MEDICINE》 * 
JOANNA LEWEK ET.AL: "Clinical and electrocardiographic covariates of deceleration capacity in patients with STsegment elevation myocardial infarction", 《CARDIOLOGY JOURNAL》 * 
别怀玺 等: "心率变异分析在精神疾病领域中的应用", 《医学综述》 * 
Cited By (24)
Publication number  Priority date  Publication date  Assignee  Title 

CN104523263A (en) *  20141223  20150422  华南理工大学  Mobile internet based pregnant and lyingin woman health surveillance system 
CN108366763A (en) *  20150615  20180803  Medibio有限公司  Method and system for assessing the state of mind 
US10912508B2 (en)  20150615  20210209  Medibio Limited  Method and system for assessing mental state 
CN104983434A (en) *  20150617  20151021  重庆邮电大学  Multiparameter mental stress assessment method based on analytical hierarchy method and device 
CN104983434B (en) *  20150617  20180501  重庆邮电大学  Multiparameter psychological pressure appraisal procedure and device based on analytic hierarchy process (AHP) 
WO2017016086A1 (en) *  20150730  20170202  华南理工大学  Depression evaluating system and method based on physiological information 
CN105147248A (en) *  20150730  20151216  华南理工大学  Physiological informationbased depressive disorder evaluation system and evaluation method thereof 
CN105022929A (en) *  20150807  20151104  北京环度智慧智能技术研究所有限公司  Cognition accuracy analysis method for personality trait value test 
CN105938520A (en) *  20160415  20160914  深圳市德力凯医疗设备股份有限公司  Data processing method and system for foaming test check 
CN105938520B (en) *  20160415  20180731  深圳市德力凯医疗设备股份有限公司  A kind of data processing method and system of frothing test inspection 
CN105997019A (en) *  20160509  20161012  鲍崇智  A body sensor networkbased multidimensional heartbeat information synchronous collection method and system 
CN105997019B (en) *  20160509  20190219  鲍崇智  Multidimensional heartbeat message synchronous collection method and system based on bodysensing net 
CN106037705A (en) *  20160523  20161026  韩剑  Data detection method and system based on wearable equipment 
CN106073765A (en) *  20160527  20161109  成都信汇聚源科技有限公司  Die suddenly in a kind of miniature holter devices the implementation method of risk profile 
CN106175742A (en) *  20160719  20161207  北京心量科技有限公司  A kind of heart sign acquisition methods and device 
CN106326644A (en) *  20160816  20170111  沈阳东软熙康医疗系统有限公司  Method and device for calculating HRV (heart rate variability) parameters and fatigue indexes 
CN106419937A (en) *  20160912  20170222  南京邮电大学  Mental stress analysis system based on heart sound HRV theory 
CN108652613B (en) *  20170330  20201103  深圳市理邦精密仪器股份有限公司  Method and device for generating signal timefrequency diagram 
CN108652613A (en) *  20170330  20181016  深圳市理邦精密仪器股份有限公司  The method and device that signal timefrequency figure generates 
CN107088061A (en) *  20170504  20170825  太原理工大学  A kind of HRV online analysis systems and its method based on Shewhart control figures 
WO2019100561A1 (en) *  20171127  20190531  乐普（北京）医疗器械股份有限公司  Artificial intelligencebased interference recognition method for electrocardiogram 
CN108577804A (en) *  20180202  20180928  西北工业大学  A kind of BCG signal analysis methods and system towards hypertensive patient's monitoring 
CN109077714A (en) *  20180705  20181225  广州视源电子科技股份有限公司  Signal recognition method, device, equipment and storage medium 
CN109394197A (en) *  20181127  20190301  中山大学  A kind of heart rate variability measurement method, device and equipment based on time frequency analysis 
Also Published As
Publication number  Publication date 

CN104127194B (en)  20160504 
Similar Documents
Publication  Publication Date  Title 

Neilson  Fetal electrocardiogram (ECG) for fetal monitoring during labour  
Clifford et al.  Noninvasive fetal ECG analysis  
von Rosenberg et al.  Resolving ambiguities in the LF/HF ratio: LFHF scatter plots for the categorization of mental and physical stress from HRV  
Xiao et al.  Sleep stages classification based on heart rate variability and random forest  
CN107951485B (en)  Ambulatory ECG analysis method and apparatus based on artificial intelligence self study  
CN107495962B (en)  Sleep automatic staging method for singlelead electroencephalogram  
CN104523266B (en)  A kind of electrocardiosignal automatic classification method  
Martis et al.  Application of higher order statistics for atrial arrhythmia classification  
Silva et al.  Noninvasive fetal ECG: the PhysioNet/computing in cardiology challenge 2013  
Valenza et al.  Pointprocess nonlinear models with laguerre and volterra expansions: Instantaneous assessment of heartbeat dynamics  
WO2017016086A1 (en)  Depression evaluating system and method based on physiological information  
CN106108889B (en)  Electrocardiogram classification method based on deep learning algorithm  
Acharya et al.  Heart rate analysis in normal subjects of various age groups  
Yang  Multiscale recurrence quantification analysis of spatial cardiac vectorcardiogram signals  
Cahill et al.  Association and prediction of neonatal acidemia  
Bagby et al.  Are there subtypes of alexithymia?  
RU2678364C2 (en)  Method and system for estimating brain concussion  
CN103083013B (en)  Electrocardio signal QRS complex wave detection method based on morphology and wavelet transform  
EP1179319B1 (en)  Apparatus to detect acute cardiac syndromes in specified groups of patients using ECG  
US6580944B1 (en)  Method and apparatus for diagnosing sleep breathing disorders while a patient in awake  
Inbar et al.  On surface EMG spectral characterization and its application to diagnostic classification  
David et al.  An estimate of fetal autonomic state by timefrequency analysis of fetal heart rate variability  
Karvounis et al.  An automated methodology for fetal heart rate extraction from the abdominal electrocardiogram  
Behar et al.  A practical guide to noninvasive foetal electrocardiogram extraction and analysis  
Karpagachelvi et al.  ECG feature extraction techniquesa survey approach 
Legal Events
Date  Code  Title  Description 

PB01  Publication  
C06  Publication  
SE01  Entry into force of request for substantive examination  
C10  Entry into substantive examination  
GR01  Patent grant  
C14  Grant of patent or utility model 