Arrhythmia Classification And Person Identification Based ECG Signals

Electrocardiogram(ECG)signals are the reflection of the electrophysiological activity of the human heart.Since the invention of electrocardiograph in the early 20 th century,people have been working on the research of heart disease through ECG.With the development of information technology,automatic analysis of ECG signals by computer and artificial intelligence has been widely used.However,due to the limitations of the ECG data acquisition environment and possible errors in the diagnosis by doctors,there are some wrongly labelled samples in the training set,which makes the classification of ECG become a weak supervised learning problem,the classifier be seriously damaged,and the classification accuracy be significantly reduced.Aiming at this problem,this paper proposes a cross validation based method to identify the mislabelled samples in the training set,thus improving the classification accuracy and achieving satisfactory results.Besides,because the ECG signals are relatively stable and very hard to steal and forge,their application is no longer limited to the diagnosis of heart disease,but extends to the field of identity recognition,too.Although a lot of related works have been done,the identification of ECG with exercise is still to be further studied.The robustness of exercise limits the practical application of ECG identification.In order to solve this problem,this paper establishes an exercise ECG database and discusses the feasibility of ECG identification with exercise.The first chapter introduces the background and significance of this study,and reviews the development of ECG automatic analysis and ECG identification.In the second chapter,the generation mechanism and waveform characteristics of ECG signals are introduced in detail.Then the lead system of electrocardiograph and the database widely used in ECG researches are introduced.In the third chapter,the main flow of ECG signals processing is analysed.Wavelet transform,median filter and notch filter are introduced in ECG preprocessing.Differential threshold method and Hilbert transform method are introduced in ECG waveform detection.Time domain features,morphological features and frequency domain features are introduced in ECG feature extraction.Finally,support vector machine,decision tree,linear discriminant analysis,naive bayes and knearest neighbour algorithm are introduced in the classification algorithm.The fourth chapter discusses the problem of mislabelled training samples in ECG automatic classification.To solve this problem,this paper proposes a method combining multiple machine learning algorithms based on cross validation to identify the mislabelled training samples.The proposed method is verified experimentally on the MIT-BIH arrhythmia database that has been artificially added with mislabelled noise.The results show that most of the classification performance indicators are significantly improved after removing the mislabelled training samples by the proposed method.Among them,as the overall classification accuracy for representation,if the mislabelled samples existed in the training set is less than 20%,the classification accuracy can be increased to the same level as there is no mislabelled sample in the training set with the help of the proposed method.If the proportion of mislabelled samples is 30%,the classification accuracy is slightly lower than the case of no mislabelled sample in the training set.If the proportion meets 40%,the classification accuracy is still much higher than the circumstance without filtering.In chapter 5,in view of the difficulties caused by the change of motion state in ECG identification,this paper discusses the feasibility of ECG identification with exercise by using the ECG identification database collected by our research group.This chapter first introduces the detailed information of the exercise ECG database and analyses the decisive factor of ECG identification with exercise is to extract the features that keep relatively stable before and after exercise.Then the feature extraction and selection methods,which are relatively mature in the current ECG identification field,are verified on the exercise ECG database.The experimental results show that the ECG identification methods proposed in the existing literatures have achieved good results in the static state,but the situation of the change of the motion state needs to be further studied.