Several methods for automatic heartbeat classification have been developed, but few efforts have been devoted to the recognition of the small ECG changes occurring in healthy people as a response to stimuli. Herein, we describe a procedure for the extraction, selection and classification of features summarizing morphological ECG changes. The proposed procedure is composed by the following stages: 1) extraction of a set of heartbeat morphological features; 2) selection of a subset of features; 3) subject normalization 4) classification. The selection of a subset of features enabled us to summarize ECG changes with only three non redundant features. In addition we performed a comparison between four classificators: k-nearest-neighbors (k-NN), artificial neural networks (ANN), support vector machines (SVM) and naive Bayes classifiers (nB). In order to cope with the possible non linear separation problem, we evaluated two strategies: a subject factor normalization on feature space and the usage of kernel functions for classifiers. The results of the comparison recommended the usage of subject normalization, irrespectively from the classificator: with or without normalization we had the best performance of classification for the linear-SVM and ANN

Comparative study of morphological ECG features classificators: An application on athletes undergone to acute physical stress

LAURINO, MARCO;GEMIGNANI, ANGELO;LANDI, ALBERTO;PIAGGI, PAOLO;Menicucci D.
2011

Abstract

Several methods for automatic heartbeat classification have been developed, but few efforts have been devoted to the recognition of the small ECG changes occurring in healthy people as a response to stimuli. Herein, we describe a procedure for the extraction, selection and classification of features summarizing morphological ECG changes. The proposed procedure is composed by the following stages: 1) extraction of a set of heartbeat morphological features; 2) selection of a subset of features; 3) subject normalization 4) classification. The selection of a subset of features enabled us to summarize ECG changes with only three non redundant features. In addition we performed a comparison between four classificators: k-nearest-neighbors (k-NN), artificial neural networks (ANN), support vector machines (SVM) and naive Bayes classifiers (nB). In order to cope with the possible non linear separation problem, we evaluated two strategies: a subject factor normalization on feature space and the usage of kernel functions for classifiers. The results of the comparison recommended the usage of subject normalization, irrespectively from the classificator: with or without normalization we had the best performance of classification for the linear-SVM and ANN
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/202121
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