CN103345600A - Electrocardiosignal data processing method - Google Patents
Electrocardiosignal data processing method Download PDFInfo
- Publication number
- CN103345600A CN103345600A CN201310253575XA CN201310253575A CN103345600A CN 103345600 A CN103345600 A CN 103345600A CN 201310253575X A CN201310253575X A CN 201310253575XA CN 201310253575 A CN201310253575 A CN 201310253575A CN 103345600 A CN103345600 A CN 103345600A
- Authority
- CN
- China
- Prior art keywords
- electrocardiosignal
- monocycle
- signal data
- similarity
- ecg signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The invention discloses an electrocardiosignal data processing method. The electrocardiosignal data processing method comprises the following steps of (1) collecting data of electrocardiosignals; (2) carrying out preprocessing on the collected data of the electrocardiosignals, wherein baseline drift removal processing and de-noising processing are carried out on the collected electrocardiosignals; (3) decomposing the electrocardiosignals into single-cycle electrocardiosignal sets; (4) carrying out characteristic extraction, carrying out signal classification according to single-cycle electrocardiosignals after normalization and the level of similarity of curve signals, and choosing electrocardiosignals in the maximum class as a template; (5) detecting signal input, carrying out similarity comparison between the electrocardiosignals and a central database template to determine the identity. According to the single-leading electrocardiosignal data processing method, the mode of characteristic curve matching is adopted to carry out curve similarity comparative studies on electrocardiosignal sequences, and complex procedures for extracting characteristic points are avoided.
Description
[technical field]
The present invention relates to a kind of technology of identification, relate in particular to a kind of electrocardiosignal that utilizes and carry out the data processing, and then the method for identification identity.
[background technology]
Along with computer network and development of electronic technology, a kind of new auth method occurred and replaced traditional password and password---biological identity recognizing technology.Biological identity recognizing technology refers to utilize human body biological characteristics or behavioural characteristic to carry out a kind of technology of authentication.
Automatic recognition system based on biological characteristic has identical substantially principle of work and the course of work.At first be to gather sample, these samples can be the images of fingerprint, people's face etc.; Next is to carry out feature extraction, and the uniqueness that has according to sample and unique feature are that it distributes a feature code with a kind of algorithm, and this feature code is deposited in database.At last when needs carry out identity authentication to someone, will deposit this person's the feature code of database in certain characteristic matching algorithm again and identified person's feature is complementary, thereby find out its identity.
Electrocardiosignal of the prior art carries out the method for identification, electrocardiosignal satisfies the pacing items of bio-identification, it is relative constant that normal person's Electrocardiographic PQRST waveform kept in the regular hour, even pressure, exercise heart rate change, but it is stable that the QRS waveform still keeps, and so just guaranteed the stability of individual ecg characteristics.But existing each a plurality of unique point of leading that adopts 12 electrocardiosignals that lead based on the electrocardiosignal identity recognizing technology, as having uniqueness and distinctive identification characteristic information, the electrocardiosignal that the Technology Need collection 12 of this identification is led, signals collecting is more loaded down with trivial details, only be applicable to through the professional training hospital doctor and implement, be difficult to be applied in family and the individual medical monitoring.Simultaneously, because the characteristic information of gathering is discrete data, calculation of complex and differentiation accuracy are not high.
[summary of the invention]
For solving the problems of the technologies described above, the present invention proposes a kind of ecg signal data disposal route, and this electrocardiosignal sequence is carried out itself the similarity comparison of characteristic curve:
A kind of ecg signal data disposal route, this method may further comprise the steps:
(a) gather ecg signal data;
(b) ecg signal data that collects is carried out pre-service;
(c) feature extraction will be decomposed into monocycle electrocardiosignal group through pretreated electrocardiosignal, and general's monocycle electrocardiosignal wherein is as a proper vector;
(d) electrocardiosignal is carried out categorizing selection, make up the identification proper vector with this;
(e) similarity is relatively confirmed identity.
In above-mentioned characteristic extraction step, described concrete steps are:
Detect electrocardiosignal QRS complex, determine R wave-wave peak dot;
Be the separatrix with the crest, electrocardiosignal is decomposed into the monocycle signal group;
With each monocycle signal as a proper vector.
Preferably, taking each proper vector is the normalized of [0,1] at transverse axis (time shaft) and the enterprising line range of the longitudinal axis (voltage axis) simultaneously.
Preferably, above-mentioned steps also is included in carries out the cubic spline interpolation algorithm on the transverse axis, and interpolation is spaced apart X=[0:0.01:1].
Preferably, in characteristic extraction step, further comprise and to classify through the monocycle electrocardiosignal after the normalized, and select monocycle electrocardiosignal in the maximum classification as set of eigenvectors.
Preferably, described similarity may further comprise the steps relatively before:
The monocycle electrocardiosignal that extracts in the maximum classification is stored in central database as the original electrocardiographicdigital signal as the set of eigenvectors of sample objects, stores template base into;
Choose in the monocycle electrocardiosignal classification, the highest k the normalization cardiac electrical cycle of similarity stores template base into as individual's representation signal.
After the detection signal input, carry out similarity relatively with the centre data library template, determine one's identity.
Preferably, adopt the characteristic curve matching way, the similarity of monocycle electrocardiosignal sequence being carried out itself curve compares.
Advantage of the present invention is:
1, can utilize family portable equipment to carry out ecg signal acquiring, enlarge user scope, can only be finished by medical institutions such as hospitals and no longer be confined to existing ecg signal acquiring;
2, the present invention adopts the mode of characteristic curve coupling, and the complicated processes of having avoided ecg signal data to extract makes to calculate and oversimplifies;
3, it is not high to have got rid of the signal data accuracy that causes because of factors such as motion, mood, heart diseases, can effectively improve the accuracy of identification.
[description of drawings]
Fig. 1 is an embodiment center electric signal data sampling system architecture synoptic diagram;
Fig. 2 is the process flow diagram of an embodiment center electric signal data sampling;
Fig. 3 is original ecg signal data figure among the embodiment;
Fig. 4 is normalization electrocardio monocycle signal data plot among the embodiment;
Fig. 5 is the monocycle ecg signal data figure after normalization is integrated among the embodiment.
[embodiment]
Below in conjunction with Figure of description 1-5, the technical scheme that the present invention relates to done further elaborating.
Fig. 1 and Fig. 2 show present embodiment center electric signal data sampling system architecture synoptic diagram and process flow diagram respectively, specifically may further comprise the steps:
Step S10 gathers electrocardiosignal.In the present embodiment, adopt the electrocardiosignal that singly leads to carry out identification, lead or portable cardiac collecting device that other differences are led several comprising medical 12.
Because existing each a plurality of unique point of leading that adopts medical 12 electrocardiosignals that lead based on the electrocardiosignal identity recognizing technology, signals collecting is more loaded down with trivial details, and range of application is less, so only need with one of them electrocardiogram (ECG) data that leads in the present embodiment, can gather the data of different objects, different time sections, make the electrocardiosignal identification no longer be confined to medical institutions such as hospital, can utilize family portable electrocardiogram acquisition equipment, tele-medicine and individual medical treatment, gather and upload data voluntarily by the user, enlarged range of application.
Step S20 carries out pre-service to the electrocardiogram (ECG) data of gathering.
The electrocardiosignal pre-service mainly is to carry out filtering, in the present embodiment, can adopt the fertile husband's bandpass filter of Bart of 0.5-45Hz to carry out denoising, and adopt Wavelet Transformation Algorithm to remove baseline wander.
Step S30, the normalized of ecg signal data.
Be illustrated in figure 3 as original ecg signal data figure in the present embodiment, described the original electrocardiographicdigital signal data that collects carried out normalized, concrete steps are:
Determine the monocycle electrocardiosignal and with this as a proper vector.In the present embodiment, Figure 4 shows that normalization electrocardio monocycle signal data plot in the present embodiment, wherein, determine that the step of monocycle signal in the electrocardiosignal is:
Detecting the QRS ripple in the electrocardiosignal, and the position of definite R wave-wave peak dot, is the separatrix with R wave-wave peak dot, and electrocardiosignal is decomposed into monocycle electrocardiosignal group, with each monocycle electrocardiosignal as a proper vector.
Be [0 at transverse axis (time shaft) and the enterprising line range of the longitudinal axis (voltage axis) simultaneously to each proper vector, 1] normalized, and carrying out cubic spline interpolation at transverse axis handles, interpolation is set at X=[0:0.01:1 at interval], like this, make and adopt unified sample frequency in the same information handling system.
Be illustrated in figure 5 as the electrocardio exemplary plot after normalization is integrated, be the separatrix with the R ripple, electrocardiosignal has kept all features of complete cycle signal after normalization, and amplitude difference disappears, electrocardiosignal after the reorganization still can well extract the information of P ripple or R ripple.
Step S40 sets up electrocardiosignal masterplate database.
In the present embodiment, as shown in Figure 3, the top data are training set data, and the bottom is divided into the test set data, and the data that two parts are collected are as the original electrocardiographicdigital signal data, and the top data store in the ecg signal data template base.
As shown in Figure 4, the bottom is divided into normalization monocycle electrocardio recall signal.For the original electrocardiographicdigital signal data that retrieves, by the monocycle confidence electric signal that obtains after the normalized, carry out the signal classification according to the similarity degree between the curve signal, adopt processings of classifying of the method for cluster analysis, choose in the classification of maximum electrocardiosignal as individual's identification electrocardiosignal masterplate.By different objects being carried out the ecg signal data sampling, set up the electrocardiosignal template database.
Among the step S40, also can adopt intermediate value with all electrocardiosignals in the maximum classification as standard vector, calculate and standard vector between difference degree (for example Euclidean distance), and the nearest 10 groups of vectors of selected distance standard vector are as the representative electrocardiosignal template of sample objects.
Step S50, similarity relatively.
Whether the proper vector of the electrocardiosignal that relatively collects is similar to the template in the electrocardiosignal template database, if then enter step S60; If not, then finish.
In the present embodiment, after the input of detection electrocardiosignal, take the characteristic curve matching way, carry out similarity relatively with the centre data library template.
Step S60: described similarity compares, with the sample objects corresponding with the template base data of retrieve data similarity degree maximum, as the owner of retrieval electrocardiogram (ECG) data.
Need to prove that above-described the specific embodiment of the present invention does not constitute the restriction to protection domain of the present invention.Other various corresponding changes and distortion that any technical conceive according to the present invention has been done all should be included within the protection domain of claim of the present invention.
Claims (7)
1. an ecg signal data disposal route is characterized in that, may further comprise the steps:
(a) gather ecg signal data;
(b) ecg signal data that collects is carried out pre-service;
(c) feature extraction will be decomposed into monocycle electrocardiosignal group through pretreated electrocardiosignal, and general's monocycle electrocardiosignal wherein is as a proper vector;
(d) electrocardiosignal is carried out categorizing selection, make up the identification proper vector with this;
(e) similarity is relatively confirmed identity.
2. ecg signal data disposal route as claimed in claim 1 is characterized in that, described characteristic extraction step is specially:
Detect electrocardiosignal QRS complex, determine R wave-wave peak dot;
Be the separatrix with the crest, electrocardiosignal is decomposed into the monocycle signal group;
With each monocycle signal as a proper vector.
3. ecg signal data disposal route as claimed in claim 2 is characterized in that, also comprises step:
Upward each proper vector is carried out the normalized that scope is [0,1] at transverse axis (time shaft) and the longitudinal axis (voltage axis) simultaneously.
4. ecg signal data disposal route as claimed in claim 3 is characterized in that, also is included in to carry out the cubic spline interpolation processing on the transverse axis, and interpolation is spaced apart X=[0:0.01:1].
5. as claim 2 or 3 described ecg signal data disposal routes, it is characterized in that, described characteristic extraction step further comprises classifies the monocycle electrocardiosignal after normalized, and selects monocycle electrocardiosignal in the maximum classification as set of eigenvectors.
6. ecg signal data disposal route as claimed in claim 1 is characterized in that, described similarity may further comprise the steps relatively before:
The monocycle electrocardiosignal that extracts in the maximum classification is stored in central database as the original electrocardiographicdigital signal as the set of eigenvectors of sample objects, stores template base into;
Choose in the monocycle electrocardiosignal classification, the highest k the normalization cardiac electrical cycle of similarity stores template base into as individual's representation signal;
After the detection signal input, carry out similarity relatively with the centre data library template.
7. ecg signal data disposal route as claimed in claim 6 is characterized in that,
Adopt the characteristic curve matching way, the similarity of monocycle electrocardiosignal sequence being carried out itself curve compares.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310253575.XA CN103345600B (en) | 2013-06-24 | 2013-06-24 | A kind of ecg signal data processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310253575.XA CN103345600B (en) | 2013-06-24 | 2013-06-24 | A kind of ecg signal data processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103345600A true CN103345600A (en) | 2013-10-09 |
| CN103345600B CN103345600B (en) | 2017-06-13 |
Family
ID=49280395
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310253575.XA Active CN103345600B (en) | 2013-06-24 | 2013-06-24 | A kind of ecg signal data processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103345600B (en) |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103610457A (en) * | 2013-11-19 | 2014-03-05 | 深圳先进技术研究院 | Electrocardiosignal processing method and electrocardiosignal processing system |
| CN103714281A (en) * | 2013-12-12 | 2014-04-09 | 深圳先进技术研究院 | Identity recognition method based on electrocardiosignals |
| CN103800003A (en) * | 2014-01-21 | 2014-05-21 | 杭州电子科技大学 | ECG detection method and detector |
| CN104102915A (en) * | 2014-07-01 | 2014-10-15 | 清华大学深圳研究生院 | Multiple-template matching identity recognition method based on ECG (Electrocardiogram) under electrocardiogram abnormality state |
| CN104398252A (en) * | 2014-11-05 | 2015-03-11 | 深圳先进技术研究院 | Electrocardiogram signal processing method and device |
| CN104573458A (en) * | 2014-12-30 | 2015-04-29 | 深圳先进技术研究院 | Identity recognition method, device and system based on electrocardiogram signals |
| CN105373719A (en) * | 2014-09-01 | 2016-03-02 | 三星电子株式会社 | User authentication method and apparatus based on electrocardiogram (ecg) signal |
| CN105469037A (en) * | 2015-11-19 | 2016-04-06 | 中科院微电子研究所昆山分所 | Identity identification method and identity identification system based on electrocardiosignals |
| CN106650685A (en) * | 2016-12-29 | 2017-05-10 | 深圳先进技术研究院 | Electrocardiogram signal-based identity identification method and apparatus |
| CN103886204B (en) * | 2014-03-24 | 2017-10-17 | 宇龙计算机通信科技(深圳)有限公司 | Data storage device and date storage method |
| CN107590508A (en) * | 2017-08-25 | 2018-01-16 | 漫迪医疗仪器（上海）有限公司 | A kind of mcg-signalses adaptive processing method and system |
| CN107736888A (en) * | 2017-11-27 | 2018-02-27 | 中山大学 | A kind of method and system that real-time cardiac monitoring is carried out using wearable ecg equipment |
| WO2018152724A1 (en) * | 2017-02-23 | 2018-08-30 | 深圳先进技术研究院 | Identity recognition method, device and system |
| CN108875344A (en) * | 2018-06-25 | 2018-11-23 | Oppo（重庆）智能科技有限公司 | A kind of electronic equipment control method, electronic appliance controlling device and electronic equipment |
| CN109044347A (en) * | 2018-07-11 | 2018-12-21 | 上海夏先机电科技发展有限公司 | Identify electrocardio wave image junctional escape beat method, apparatus, system and electronic equipment |
| CN109222963A (en) * | 2018-11-21 | 2019-01-18 | 燕山大学 | A kind of anomalous ecg method for identifying and classifying based on convolutional neural networks |
| CN109492659A (en) * | 2018-09-25 | 2019-03-19 | 维灵（杭州）信息技术有限公司 | A kind of method of calculated curve similarity |
| CN109893119A (en) * | 2019-03-24 | 2019-06-18 | 浙江好络维医疗技术有限公司 | A kind of P wave recognition positioning method based on multi-lead clustering |
| CN110123289A (en) * | 2019-04-08 | 2019-08-16 | 清华大学深圳研究生院 | A kind of biometric discrimination method and relevant apparatus based on pulse wave |
| CN110169767A (en) * | 2019-07-08 | 2019-08-27 | 河北大学 | A kind of search method of electrocardiosignal |
| CN110263520A (en) * | 2019-06-21 | 2019-09-20 | 深圳市奋达智能技术有限公司 | A kind of auth method and system |
| CN110269625A (en) * | 2019-05-31 | 2019-09-24 | 杭州电子科技大学 | A kind of electrocardio authentication method and system of novel multiple features fusion |
| CN112370065A (en) * | 2020-10-21 | 2021-02-19 | 华南理工大学 | Identity recognition method, system and medium based on electrocardiosignal dynamics characteristics |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101773394A (en) * | 2010-01-06 | 2010-07-14 | 中国航天员科研训练中心 | Identification method and identification system using identification method |
| CN102274029A (en) * | 2011-05-25 | 2011-12-14 | 中国科学院深圳先进技术研究院 | Identity recognition method and system |
| CN102805620A (en) * | 2012-08-16 | 2012-12-05 | 深圳市邦健电子有限公司 | Method and device for detecting atrial fibrillation |
| CN102938055A (en) * | 2012-10-09 | 2013-02-20 | 哈尔滨工程大学 | Hand bone identification system |
-
2013
- 2013-06-24 CN CN201310253575.XA patent/CN103345600B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101773394A (en) * | 2010-01-06 | 2010-07-14 | 中国航天员科研训练中心 | Identification method and identification system using identification method |
| CN102274029A (en) * | 2011-05-25 | 2011-12-14 | 中国科学院深圳先进技术研究院 | Identity recognition method and system |
| CN102805620A (en) * | 2012-08-16 | 2012-12-05 | 深圳市邦健电子有限公司 | Method and device for detecting atrial fibrillation |
| CN102938055A (en) * | 2012-10-09 | 2013-02-20 | 哈尔滨工程大学 | Hand bone identification system |
Non-Patent Citations (1)
| Title |
|---|
| 李巍巍 等: "应用主成分分析法的ECG身份识别方法", 《苏州科技学院学报（自然科学版）》 * |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103610457A (en) * | 2013-11-19 | 2014-03-05 | 深圳先进技术研究院 | Electrocardiosignal processing method and electrocardiosignal processing system |
| CN103714281A (en) * | 2013-12-12 | 2014-04-09 | 深圳先进技术研究院 | Identity recognition method based on electrocardiosignals |
| CN103714281B (en) * | 2013-12-12 | 2017-03-29 | 深圳先进技术研究院 | A kind of personal identification method based on electrocardiosignal |
| CN103800003A (en) * | 2014-01-21 | 2014-05-21 | 杭州电子科技大学 | ECG detection method and detector |
| CN103886204B (en) * | 2014-03-24 | 2017-10-17 | 宇龙计算机通信科技(深圳)有限公司 | Data storage device and date storage method |
| CN104102915A (en) * | 2014-07-01 | 2014-10-15 | 清华大学深圳研究生院 | Multiple-template matching identity recognition method based on ECG (Electrocardiogram) under electrocardiogram abnormality state |
| CN104102915B (en) * | 2014-07-01 | 2019-02-22 | 清华大学深圳研究生院 | Personal identification method based on ECG multi-template matching under a kind of anomalous ecg state |
| US10572637B2 (en) | 2014-09-01 | 2020-02-25 | Samsung Electronics Co., Ltd. | User authentication method and apparatus based on electrocardiogram (ECG) signal |
| CN105373719A (en) * | 2014-09-01 | 2016-03-02 | 三星电子株式会社 | User authentication method and apparatus based on electrocardiogram (ecg) signal |
| CN104398252A (en) * | 2014-11-05 | 2015-03-11 | 深圳先进技术研究院 | Electrocardiogram signal processing method and device |
| CN104573458B (en) * | 2014-12-30 | 2017-05-31 | 深圳先进技术研究院 | A kind of personal identification method based on electrocardiosignal, apparatus and system |
| CN104573458A (en) * | 2014-12-30 | 2015-04-29 | 深圳先进技术研究院 | Identity recognition method, device and system based on electrocardiogram signals |
| CN105469037A (en) * | 2015-11-19 | 2016-04-06 | 中科院微电子研究所昆山分所 | Identity identification method and identity identification system based on electrocardiosignals |
| CN106650685A (en) * | 2016-12-29 | 2017-05-10 | 深圳先进技术研究院 | Electrocardiogram signal-based identity identification method and apparatus |
| CN106650685B (en) * | 2016-12-29 | 2020-06-05 | 深圳先进技术研究院 | Identity recognition method and device based on electrocardiogram signal |
| WO2018152724A1 (en) * | 2017-02-23 | 2018-08-30 | 深圳先进技术研究院 | Identity recognition method, device and system |
| CN107590508A (en) * | 2017-08-25 | 2018-01-16 | 漫迪医疗仪器（上海）有限公司 | A kind of mcg-signalses adaptive processing method and system |
| CN107736888A (en) * | 2017-11-27 | 2018-02-27 | 中山大学 | A kind of method and system that real-time cardiac monitoring is carried out using wearable ecg equipment |
| CN108875344A (en) * | 2018-06-25 | 2018-11-23 | Oppo（重庆）智能科技有限公司 | A kind of electronic equipment control method, electronic appliance controlling device and electronic equipment |
| CN109044347A (en) * | 2018-07-11 | 2018-12-21 | 上海夏先机电科技发展有限公司 | Identify electrocardio wave image junctional escape beat method, apparatus, system and electronic equipment |
| CN109492659A (en) * | 2018-09-25 | 2019-03-19 | 维灵（杭州）信息技术有限公司 | A kind of method of calculated curve similarity |
| CN109492659B (en) * | 2018-09-25 | 2021-10-01 | 维灵（杭州）信息技术有限公司 | Method for calculating curve similarity for electrocardio and electroencephalogram waveform comparison |
| CN109222963A (en) * | 2018-11-21 | 2019-01-18 | 燕山大学 | A kind of anomalous ecg method for identifying and classifying based on convolutional neural networks |
| CN109893119A (en) * | 2019-03-24 | 2019-06-18 | 浙江好络维医疗技术有限公司 | A kind of P wave recognition positioning method based on multi-lead clustering |
| CN109893119B (en) * | 2019-03-24 | 2022-01-04 | 浙江好络维医疗技术有限公司 | P wave identification and positioning method based on multi-lead clustering analysis |
| CN110123289A (en) * | 2019-04-08 | 2019-08-16 | 清华大学深圳研究生院 | A kind of biometric discrimination method and relevant apparatus based on pulse wave |
| CN110269625A (en) * | 2019-05-31 | 2019-09-24 | 杭州电子科技大学 | A kind of electrocardio authentication method and system of novel multiple features fusion |
| CN110269625B (en) * | 2019-05-31 | 2022-02-11 | 杭州电子科技大学 | Novel multi-feature fusion electrocardio authentication method and system |
| CN110263520A (en) * | 2019-06-21 | 2019-09-20 | 深圳市奋达智能技术有限公司 | A kind of auth method and system |
| CN110169767A (en) * | 2019-07-08 | 2019-08-27 | 河北大学 | A kind of search method of electrocardiosignal |
| CN110169767B (en) * | 2019-07-08 | 2021-09-21 | 河北大学 | Retrieval method of electrocardiosignals |
| CN112370065A (en) * | 2020-10-21 | 2021-02-19 | 华南理工大学 | Identity recognition method, system and medium based on electrocardiosignal dynamics characteristics |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103345600B (en) | 2017-06-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103345600A (en) | Electrocardiosignal data processing method | |
| CN103714281B (en) | A kind of personal identification method based on electrocardiosignal | |
| CN106473750B (en) | Personal identification method based on photoplethysmographic optimal period waveform | |
| CN104102915B (en) | Personal identification method based on ECG multi-template matching under a kind of anomalous ecg state | |
| Fang et al. | Human identification by quantifying similarity and dissimilarity in electrocardiogram phase space | |
| CN101773394B (en) | Identification method and identification system using identification method | |
| CN110226921B (en) | Electrocardiosignal detection and classification method and device, electronic equipment and storage medium | |
| CN109645980A (en) | A kind of rhythm abnormality classification method based on depth migration study | |
| Shen et al. | The PLR-DTW method for ECG based biometric identification | |
| CN109044340B (en) | Electrocardiogram data classification method and device, electronic equipment and storage medium | |
| CN115120248B (en) | Histogram-based adaptive threshold R peak detection and heart rhythm classification method and device | |
| CN109222964A (en) | atrial fibrillation detection device and storage medium | |
| Jaafar et al. | Acceleration plethysmogram based biometric identification | |
| Xie et al. | Intelligent analysis of premature ventricular contraction based on features and random forest | |
| Kuila et al. | Feature extraction of electrocardiogram signal using machine learning classification | |
| CN114239649B (en) | Identity recognition method for discovering and recognizing new user by photoelectric volume pulse wave signal of wearable device | |
| Liu et al. | A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks | |
| CN114638272A (en) | Identity recognition method and device based on fingertip pulse wave signals | |
| Li et al. | A novel abnormal ECG beats detection method | |
| CN116028914B (en) | Intelligent finger ring identity authentication method and system | |
| Biran et al. | Automatic qrs detection and segmentation using short time fourier transform and feature fusion | |
| She et al. | A precise ambulatory ECG arrhythmia intelligent analysis algorithm based on support vector machine classifiers | |
| Hameed et al. | Electrocardiogram ECG For Human Identification Based on Machine Learning Techniques and deep learning | |
| Tantawi et al. | On the use of the electrocardiogram for biometrie authentication | |
| Saini et al. | Detection of QRS-complex using K-nearest neighbour algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20131009 Assignee: Guangdong Xinshen Electronic Technology Co.,Ltd. Assignor: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES Contract record no.: X2023980039226 Denomination of invention: A Method for Processing ECG Signal Data Granted publication date: 20170613 License type: Common License Record date: 20230808 |
|
| EE01 | Entry into force of recordation of patent licensing contract |