Abstract: This paper presents the design of a fully integrated Electrocardiogram (ECG) Signal Processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial points. Those techniques are robust to any variations in the ECG signal with high sensitivity and precision. Two databases of the heart signal recordings from the MIT PhysioNet and the American Heart Association were used as a validation set to evaluate the performance of the processor. The early prediction of ventricular arrhythmia will improve the quality of life by alerting the patients before the critical condition. This is achieved by analyzing the ECG segment that precedes the onset of ventricular tachycardia/ventricular fibrillation condition. In order to achieve high detection accuracy with low power consumption, a multi-scaled product algorithm and soft-threshold algorithm are efficiently exploited in our ECG detector implementation.
Keywords: Adaptive techniques, classification, Electrocardiogra-phy (ECG), feature extraction, low power, ventricular arrhythmia.