Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features

Abstract

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.

Keywords

• electrocardiography
• diseases
• medical signal processing
• medical signal detection
• signal classification
• wavelet transforms
• muscle
• learning (artificial intelligence)
• support vector machines
• radial basis function networks
• automated heart ailment detection
• 12-lead ECG
• electrocardiography
• complex wavelet sub-band bi-spectrum features
• CWSB
• myocardial infarction
• heart muscle disease
• HMD
• bundle branch block
• BBB
• dual tree CW transform
• negative phase angle
• positive phase angle features
• extreme learning machine
• support vector machine
• SVM classifiers
• heart pathologies
• radial basis function kernel function
• cardiac disease detection