The improvement of precise and fast methods for the automatic extraction of ECG characteristics is of paramount importance, particularly for the examination of long recordings. In this project we will implement a system that first extracts ECG characteristics and on the basis that we will find the location and amplitude of ECG signal details. ĮCG Feature Extraction plays an important role in the diagnosis of most heart diseases. A cardiac cycle in an ECG signal consists of the P-QRS-T waves.

This feature extraction scheme determines the amplitudes and intervals in the ECG signal for further analysis. The value of the amplitudes and the intervals of the segment P-QRS-T determines the functioning of the heart of each human being. Recently, numerous investigations and techniques have been developed to analyse the ECG signal. The proposed schemes were based mainly on fuzzy logic, artificial neural network (ANN), genetic algorithm (GA), vector support machines (SVM) and other signal analysis techniques. All these techniques and algorithms have their advantages and limitations. This paper discusses several techniques and transformations previously proposed in the literature to extract characteristics of an ECG signal. In addition, this paper also provides a comparative study of several methods proposed by researchers in extracting the ECG signal characteristic.