Classification of ECG using some novel features

Abstract

Heart diseases are frequent reasons of death. Hence, there is always a need to develop systems that can provide prior indication about the state of the heart. This is also required because medical facilities may not be uniform everywhere. In such situation certain innovative approaches using certain signal processing techniques can provide considerable support. As a follow up to such possibilities, system for automatic recognition of cardiac arrhythmias has become necessary and important for diagnosis of cardiac abnormalities. Several algorithms have been proposed to classify cardiac arrhythmias in the literature; however, many of them fail to perform optimally. Here, we have proposed a method for ECG arrhythmia classification using Artificial Neural Network (ANN) and a novel feature set. Fast Fourier Transform is used for pre-processing the ECG recordings. Linear Prediction Coefficients (LPC) and Principal Component Analysis (PCA) are used for extracting some features and then Multi-Layer Perceptron (MLP) ANN performs the classification.