A brief performance evaluation of ECG feature extraction techniques for artificial neural network based classification

Electrocardiogram is the most easily accessible bioelectric signal that provides the doctors with reasonably accurate data regarding the patient heart condition. Many of the cardiac problems are visible as distortions in the electrocardiogram (ECG). Normally ECG related diagnoses are carried out by the medical practitioners manually. The major task in diagnosing the heart condition is analyzing each heart beat and co-relating the distortions found therein with various heart diseases. 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. Once feature extraction is done, ANNs can be trained to classify the patterns reasonably accurately. 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. Transform feature extraction and morphological feature extraction schemes are mostly preferred. Discrete Fourier transform, principal component analysis, and discrete wavelet transform are the three transform schemes along with three other morphological feature extraction schemes are discussed and compared in this paper.