Abstract: Electrocardiogram is an important tool in diagnosing the condition of the heart. Extracting the information from the Electrocardiograph is an important task in determining the variations of the electrical activity of the heart. ECG feature extraction plays a major significant role in diagnosing the most of the cardiac diseases. One among the major cardiac diseases is arrhythmia which is abrupt and abnormal heart beat. In case of arrhythmia heart doesn’t pump sufficient blood required for the human body and sudden cardiac death may happen and this can even damage vital organs such as brain, heart, etc. of the body positions. So it is very much needed to determine conditions of arrhythmia and should take necessary measure before the patient reaches some serious condition. Hence in order to find out arrhythmia ECG signal should be analyzed. Analyzing the ECG signal manually is a tedious process so number of researches has been done recently for analyzing the ECG signal based on fuzzy logic method, artificial neural networks, genetic algorithms and support vector machines and using other analysis techniques. This proposed paper discusses different ECG analysis techniques and provides comparative study of various methods of such techniques proposed by researchers in the previous articles. as both an instruction set and as a template into which you can type your own text.
Keywords: Discrete wavelets transform, ECG signal, Feature extraction, QRS complex detection.