access icon openaccess A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification

A new measure for quantifying diagnostic information from a multilead electrocardiogram (MECG) is proposed. This diagnostic measure is based on principal component (PC) multivariate multiscale sample entropy (PMMSE). The PC analysis is used to reduce the dimension of the MECG data matrix. The multivariate multiscale sample entropy is evaluated over the PC matrix. The PMMSE values along each scale are used as a diagnostic feature vector. The performance of the proposed measure is evaluated using a least square support vector machine classifier for detection and classification of normal (healthy control) and different cardiovascular diseases such as cardiomyopathy, cardiac dysrhythmia, hypertrophy and myocardial infarction. The results show that the cardiac diseases are successfully detected and classified with an average accuracy of 90.34%. Comparison with some of the recently published methods shows improved performance of the proposed measure of cardiac disease classification.

Inspec keywords: medical diagnostic computing; principal component analysis; medical signal processing; support vector machines; electrocardiography; diseases

Other keywords: least square classifier; diagnostic feature vector; cardiovascular disease; principal component; PMMSE; support vector machine classifier; multilead electrocardiogram; myocardial infarction; hypertrophy; cardiac dysrhythmia; diagnostic information; MECG data matrix; cardiac disease classification; multivariate multiscale sample entropy

Subjects: Knowledge engineering techniques; Other topics in statistics; Probability theory, stochastic processes, and statistics; Electrodiagnostics and other electrical measurement techniques; Electrical activity in neurophysiological processes; Digital signal processing; Other topics in statistics; Bioelectric signals; Biology and medical computing

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