Open AccessAutomated detection of heart ailments from 12‐lead ECG using complex wavelet sub‐band bi‐spectrum features
Author(s) -
Tripathy Rajesh Kumar,
Dandapat Samarendra
Publication year - 2017
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.2016.0089
Subject(s) - support vector machine , artificial intelligence , pattern recognition (psychology) , computer science , radial basis function kernel , right bundle branch block , wavelet transform , left bundle branch block , qrs complex , radial basis function , wavelet , electrocardiography , artificial neural network , cardiology , medicine , kernel method , heart failure
The complex wavelet sub‐band bi‐spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12‐lead ECG. The dual tree CW transform of 12‐lead ECG produces CW coefficients at different sub‐bands. The higher‐order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12‐lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12‐lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12‐lead ECG‐based cardiac disease detection techniques.