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Abstract
Cardiac arrhythmias are alternations that affect the electrical system of the heart muscle, that reduce the pumping efficiency. More than four million people suffer from Cardiac arrhythmias in Egypt. So, the early detection of arrhythmias is a very important task specially for the critically Ill patients. In general, ventricular arrhythmias are the most serious and in fact can be life threatening in some cases. So, in our thesis, we consider two types of ventricular arrhythmias which are: ventricular tachycardia (VT) and ventricular fibrillation (VF). We proposed three different techniques to extract features from the ECG signal which are Fast Fourier Transform (FFT), Autoregressive Modeling (AR), and Principal Component Analysis (PCA). Then three different classifiers are used in the classification: Artificial Neural Networks, and two types of statistical classifiers which are Minimum Distance Classifier, and Bayes Minimum Distance Classifier.