Open AccessA new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification
Author(s) -
Tripathy R.K.,
Sharma L.N.,
Dandapat S.
Publication year - 2014
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2014.0080
Subject(s) - pattern recognition (psychology) , artificial intelligence , principal component analysis , computer science , sample entropy , entropy (arrow of time) , myocardial infarction , multivariate statistics , support vector machine , cardiology , medicine , machine learning , physics , quantum mechanics
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.