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

[摘要] 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.

[学科分类] 肠胃与肝脏病学

[关键词] electrocardiography;medical signal processing;support vector machines;principal component analysis;diseases;medical diagnostic computing;myocardial infarction;hypertrophy;cardiac dysrhythmia;cardiovascular disease;support vector machine classifier;least square classifier;diagnostic feature vector;MECG data matrix;PMMSE;multivariate multiscale sample entropy;principal component;cardiac disease classification;multilead electrocardiogram;diagnostic information