Application of artificial neural networks to identify the premature ventricular contraction (PVC) beats

Premature ventricular contraction (PVC) is a cardiac arrhythmia that can result in sudden death. Understanding and treatment of this disorder would be improved if patterns of electrical activation could be accurately identified and studied during its occurrence. In this paper, we shall review three feature extractions algorithms of the electrocardiogram (ECG) signal, Fourier transform, linear prediction coding (LPC) technique and principal component analysis (PCA) method, with aim of generating the most appropriate input vector for a neural classifier. The performance measures of the classifier rate, sensitivity and specificity of these algorithms will also be presented using as training and testing data sets from the MIT-BIH (Massachusetts Institute Technology Beth Israel Hospital) database.