Application of Higher Order Cumulants to ECG Signals for the Cardiac Health Diagnosis

Electrocardiogram (ECG) is the P-QRS-T wave which indicates the electrical activity of the heart. The subtle changes in the amplitude and duration of the ECG signal depict the cardiac abnormality. It is very difficult to decipher these minute changes by the naked eye. Hence, a computer-aided diagnosis system will help the physicians to monitor the cardiac health. The ECG is a nonlinear and non-stationary signal. Hence, the hidden information in the ECG signal can be extracted using nonlinear method. In this paper, we have automatically classified normal and abnormal beats using higher order spectra (HOS) cumulants of wavelet packet decomposition (WPD). The abnormal beats are ventricular premature contractions (VPC) and Atrial premature contractions (APC). These HOS cumulant features of the WPD are subjected to principal component analysis (PCA) to reduce the number of features to five. Finally these features were fed to the support vector machine (SVM) with kernel functions for automatic classification. In our work, we have obtained the highest accuracy of 98.4% sensitivity and specificity of 98.9% and 98.0% respectively with radial basis function (RBF) kernel function and Meyer's wavelet (dmey) function. Our system is ready clinically to run on large amount of data sets.