Combination of Nonlinear Feature Extraction Techniques for ECG Arryhtmia Detection System

Fatin Ahmed Elhaj ； Arief R. Harris ； Tan Tian Swee ； Naomie Salim

《Research Journal of Applied Sciences, Engineering and Technology》 11卷9期 (2015/11) Pp. 1024-1029

並列摘要

Electrocardiogram (ECG) is a noninvasive method used to detect arrhythmias or heart abnormalities. It describes the electrical activity of the heart. Physicians are faced with difficulties in detecting irregular heartbeats due to the presence of noise and subtle changes in the signal amplitude and duration. Depending on human visual detection alone may lead to misdiagnosis or insignificant detection of cardiovascular diseases. A computer-aided diagnosis of the ECG will assist physicians to significantly detect the cardiovascular diseases. Non-linear method is useful to extract and capture hidden information in the ECG signal. In this study we present a combination of two nonlinear methods; Higher Order Statistics (HOS) cumulants and Independent Component Analysis (ICA), performed on the dynamics ECG signals for arrhythmia detection. The abnormal heartbeats focused in this study are Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC), Ventricular Premature Contraction (VPC) and Paced beat (P). The HOS and ICA features were fed to the Support Vector Machine (SVM) with kernel functions for automatic classification and neural network. In our study, we obtained the highest accuracy of 96.34% with Radial Basis Function (RBF) kernel and neural network.

並列關鍵字

ECG ； feature extraction ； HOS ； ICA ； supervised learning algorithm