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.
2011-01-01

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
9781457716751
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/202121
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