ECG SIGNAL CLASSIFICATION BASED ON THE HEART RATE USING ADAPTIVE THRESHOLD

Abstract

This project describes recognition and analysis of the Electrocardiogram (ECG). Identification of ECG is closely related to the classification of the patient's diagnosis; in this project we developed an easy, efficient and robust algorithm for the analysis of electrocardiogram signals. The technique used in this algorithm is based on the use of Moving Average Filters and Adaptive Thresholding for RST complex detection. Duration and amplitudes of all peaks were detected and R-R interval was calculated to determine the heart rate. The heart rate used as an input to decision role to classify the ECG signal into normal or abnormal. The project was implemented using Matlab. The results have been very satisfying, acceptable and obtained high accuracy. This system will serve as the support decision tool for the doctors and especially for the cardiologists.