Feature Recognition Of ECG Signal And Applicationin Diagnosis Of Cardiovascular Disease

Along with the change of citizen’s lifestyle, the pace of work and diet rules, heartdisease has become a high-incident ailment in modern social conditions. Hence, earlyrecognition, accurate diagnosis and timely treatment are very important to decreasethe mortality of cardiovascular disease. With the improvement of electrocardiogramsand hundreds years usage of it, electrocardiograms has been irreplaceable in detectionof cardiovascular disease. This simple, rapid and cheap technology has high values onclinical application. However, detects still exist. For instance, the proficiency ofoperator, the site of electrodes and several other reasons would affect the accuracy ofdetection results, which may lead to attaining different results of the same person. Sothe professional doctors with rich clinical experiences are needed, because of thedetection differences, the various kinds of pathological electrocardiograms, the greatvariability and the difficulty for the judgment. Then, for cardiovascular diseases suchas valvulopathy and mild ventricular hypertrophy, the results of electrocardiogramusually have no changes. And for cardiovascular diseases such as cardiovascularsclerosis and Parts of atrial fibrillation (AF), the results of electrocardiogram can onlyused as an indicator of auxiliary diagnosis. Although the result of electrocardiogramwould change along with the detection time and the site of electrodes, same internalchanges have a common biological characteristic. Hence, the bioinformaticsprocessing on numerous ECG data would increase the accuracy and the ability ofidentification of ECG, and the study of ECG automatic classification methods becomemore and more important.In our study, we aimed to build an ECG normalized method for ECG dataattained from different persons at various times, by extracting the characteristics ofthe weak electrical signals in the heart movements, which were cycle balanceddynamic signals. The major feature of this method was that it could normalize theECG data of the same people, no matter healthy or not the person was, and the ECG data of a person was never same as others. The results showed that,①The ECG dataof healthy persons were stable in a period of time, which could be used asphysiological signals for identification, through the examination using the ECG dataobtained from PTB database.②The ECG date of most people except some seriousheart unstable situation, could maintain dynamically stable in a certain period of time,through the identification test of persons suffering cardiovascular diseases using theECG data got from QT database.③Our study showed that ECG normalizedalgorithm was a process of ECG signal data standardization. ECG normalizedalgorithm was an important step in the extracting of ECG signal characteristics, whichhad a strong supporting role for ECG identification algorithm.④We deemed thatECG signals could be used as a tool for identification because of its complex anddynamic unique characteristics, which may maintain a high accuracy within a certainlimits. Before large-scale usage, a large sample validation still needed.⑤The studyshowed the complexity of the biological characteristics of ECG. Establishment of themethod describe above would provide an accurate template of the biologicalcharacteristics based on clinical samples.⑥Tests prove: CVB3/MKP strains withmyocardial toxicity and found CVB3/MKP、CVB3/Macocy and CVB3/HM138916strains of myocarditis caused by extremely close genetic relationship. To furtherimprove the CVB3/MKP virus evolution and phylogenetic relationship provides thenecessary basis for the development.Based on the ECG normalization algorithm, the article investigated theidentification of single cycle normalization ECG signal and all kinds of aspects ofECG fitting. It also adapted the method which analyses the ECG database about PTBand QT. The recognition accuracy of52healthy participants in the PTB database canachieve the100%. The recognition accuracy of91participants in the QT database canachieve90.1%. This research explores the PTB database which includes the ECGabout50MI patients and52health control participants in order to classify the singlecycle normalization ECG signal. During the experiment, we found that the moreeffective classifiers, including Bayesian network, multilayer perceptron, random forest classifier. According to the experiment, it demonstrates that the random forestclassifier is more excellent and we use the method of limiting decision tree to avoidthe over fitting phenomenon. We will use this ECG classification method in the fieldsof diagnosing MI, information assurance and classifying the diseases. It also can beused for monitoring personal health information and reducing the potential diseaserisk.In this paper, the main contributions and innovative work is as follows: the paperstarts from the formation mechanism and structural characteristics ofelectrocardiogram (ECG), majors in the key technologies of ECG normalization, andput forward some new ideas and algorithms:(1) Using ECG and other equipment foracquiring a large number of clinical ECG and constructing a database. We conducteda comprehensive analysis of foreign ECG database, for example PTB, MIT-BIH,AFPDB, SVDB, TWADB and so on. ECG standardization relies on the database ofECG signals, selecting the appropriate library can test out the advantages anddisadvantages of the algorithm. If ECG images of various disease are single lead topartial algorithm.(2) Achieve the ECG normalization method and feature extractiontechnology based on the standardization of electrocardiogram, the detectionalgorithm were respectively used to measure QRS waves in ECG signals, extract thesignal voltage amplitude, the different time period signal feature points such as RQ,RS, PQ, PS eigenvalues.(3) Using multiple classifiers classify and analysis methodssuch as PCA. The advantage of this approach is that a combination of relevant clinicaldiagnostic techniques can be more in line with the human heart electrophysiologicalcharacteristics, effectively save storage space, further optimize characteristics of eachgroup, and reduce the useless information. Experimental results showed that canachieve high recognition rate.(4) The accuracy of the automatic classification methodabout ECG can achieve over95%and the running time is just from several seconds toten or over ten seconds. This algorithmic increases the ECG classification speed andaccuracy. It also can diagnose the heart diseases quickly and accurately. This methodis of great importance of diagnosing and practical value.(5) The test demonstrated ECG normalization algorithm is a necessary step to achieve ECG standardization, isan important part of the ECG feature extraction, and has a strong supporting role foridentification of the ECG.(6) The ECG normalization mainly uses biometrictechnology, removing the human error factor of ECGmeasurement.Through normalization of ECG data, the ECG data of different tested persons would not be changed because of various factors.Biometric identification technology will become a new technology with greatprospects in21stcentury. As the development and application of this technology,everybody will enjoy the convenience of biological identification technology.Everyone can complete the requirements of biological identification and diseasesclassification via ECG testing, during a physical examination or when it is necessary.