Open AccessAutomated detection of myocardial infarction from ECG signal using variational mode decomposition based analysis
Author(s) -
Kapfo Ato,
Dandapat Samarendra,
Kumar Bora Prabin
Publication year - 2020
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.2020.0015
Subject(s) - pattern recognition (psychology) , principal component analysis , computer science , support vector machine , artificial intelligence , classifier (uml) , redundancy (engineering) , energy (signal processing) , algorithm , mathematics , statistics , operating system
In this Letter, the authors propose a variational mode decomposition method for quantifying diagnostic information of myocardial infarction (MI) from the electrocardiogram (ECG) signal. The multiscale mode energy and principal component (PC) of multiscale covariance matrices are used as features. The mode energies determine the strength of the mode, and the PCs provide the representation of the ECG signal with less redundancy. K‐nearest neighbour and support vector machine classifier are utilised to assess the performance of the extracted features for the detection and classification of MI and normal (healthy control). The proposed method achieved a specificity of 99.88%, sensitivity of 99.90%, and accuracy of 99.88%. Experimental results demonstrate that the proposed method with the multiscale mode energy and PC features achieved better output compared to the previously published work.