Performance Analysis of Feature Extraction Schemes for Artificial Neural Network Based ECG Classification

Many of the cardiac problems are visible as distortions in the electrocardiogram (ECG). Since the abnormal heart beats can occur randomly it becomes very tedious and time-consuming to analyze say a 24 hour ECG signal, as it may contain hundreds of thousands of heart beats. Hence it is desired to automate the entire process of heart beat classification and preferably diagnose it accurately In this paper the authors have focused on the various schemes for extracting the useful features of the ECG signals for use with artificial neural networks. Arrhythmia is one such type of abnormality detectable by an ECG signal. The three classes of ECG signals are Normal, Fusion and Premature Ventricular Contraction (PVC). The task of an ANN based system is to correctly identify the three classes, most importantly the PVC type, this being a fatal cardiac condition. Discrete Fourier Transform, Principal Component Analysis, and Discrete Wavelet Transform and Discrete Cosine Transform are the four schemes discussed and compared in this paper. For comparison the statistical techniques like linear discriminant analysis and tree clustering are also evaluated.