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