Paper Info
Title
Third-order tensor based analysis of multilead ECG for classification of myocardial infarction.
Abstract
Electrocardiogram (ECG) feature extraction and classification are challenging tasks for correct diagnosis of cardiac diseases. Conventional feature extraction methods were based on evaluating instances from a 2-D multilead ECG (MECG) data matrix. In this article, we proposed a novel method for detection and localization of myocardial infarction (MI) from the reduced MECG tensor. A third-order tensor structure was employed to represent the MECG data in three dimensions (leads x beats x samples). The higher order singular value decomposition exploits intra-beat, inter-beat and inter-lead correlations of wavelet transformed MECG tensor. The mode-n singular values (MSVs) and the normalized multiscale wavelet energy (NMWE) of each subband tensor were considered as the mode features for detection and localization of MI. The support vector machine was used as the classifying technique. Datasets from the PTB database that comprise of healthy and different MI cases were considered for evaluation purpose. Experimental results showed that the proposed method assured a detection accuracy of 95.30% with sensitivity and specificity of 94.6% and 96.0%, respectively. The MSV and the NMWE features from the third-order MECG tensor were tested to be accurate in detecting and localizing MI. (C) 2016 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2017
10.1016/j.bspc.2016.07.007
Biomedical Signal Processing and Control
Keywords
Field
DocType
Multilead ECG,Myocardial infarction,Higher-order SVD,Mode-n singular values,Support vector machine
Singular value decomposition,Singular value,Normalization (statistics),Pattern recognition,Tensor,Third order,Support vector machine,Feature extraction,Artificial intelligence,Mathematics,Wavelet
Journal
Volume
ISSN
Citations 
31
1746-8094
8
PageRank 
References 
Authors
0.70
16
2
Name
Order
Citations
PageRank
Padhy, S.1163.29
Samarendra Dandapat2215.08