International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-3, July 2012 5 A Unified Approach of ECG Signal Analysis Rajiv Ranjan, V. K. Giri Abstract: The bio-potentials generated by the muscles of the heart result in an electrical signal called electrocardiogram (ECG). It is one of the most important physiological parameter, which is being extensively used for knowing the state of the cardiac patients. Feature extraction of ECG is most essential task in the manual and automated ECG analysis for use in instruments like ECG monitors, Holter tape recorders and scanners, ambulatory ECG recorders and analysers. Recently, artificial intelligent tools such as neural networks, genetic algorithms, fuzzy systems, and expert systems have frequently been reported for detection and diagnostic tasks. This paper, therefore, is an attempt to review the work done by the different researchers in the area of ECG signal processing, analysis and interpretation during last five decades. Keywords: Arrhythmia, ECG analysis, ECG interpretation, Noise removal, Expert system, Artificial intelligence, Feature extraction. I. INTRODUCTION Normally, the frequency range of an ECG signal is of 0.05–100 Hz and its dynamic range of 1–10 mV. The ECG signal is characterized by five peaks and valleys labelled by the letters P, Q, R, S, T as shown in fig1. In some cases (especially in infants) we may also find another peak called U. The performance of ECG analyzing system depends mainly on the accurate and reliable detection of the QRS complex, as well as T and P waves. The P-wave represents the activation of the upper chambers of the heart, the atria, while the QRS complex and T-wave represent the excitation of the ventricles or the lower chamber of the heart. The detection of the QRS complex is the most important task in automatic ECG signal analysis. Once the QRS complex has been identified a more detailed examination of ECG signal including the heart rate, the ST segment etc. can be performed. In the normal sinus rhythm (normal state of the heart) the P-Rinterval is in the range of 0.12 to 0.2 seconds as shown in fig 1. The QRS interval is from 0.04 to 0.12 seconds. Manuscript received on July, 2012 Rajiv Ranjan, Associate Professor, Department of Electrical & Electronics Engineering, Dronacharya Group of Institutions, Gr. Noida U.P. India . Dr. V.K. Giri, Professor, Electrical Engineering Department, MMM Engineering College, Gorakhpur, U.P. India. Figure 1.The normal ECG signal. The Q-T interval is less than 0.42 seconds and the normal rate of the heart is from 60 to 100 beats per minute. So, from the recorded shape of the ECG, we can say whether the heart activity is normal or abnormal. The electrocardiogram is a graphic recording or display of the time variant voltages produced by the myocardium during the cardiac cycle. The P-, QRS- and T-waves reflect the rhythmic electrical depolarization and repolarization of the myocardium associated with the contractions of the atria and ventricles. This ECG is used clinically in diagnosing various abnormalities and conditions associated with the heart. The normal value of heart beat lies in the range of 60 to 100 beats/minute. A slower rate than this is called bradycardia (slow heart rate) and a higher rate is called tachycardia (fast heart rate). If the cycles are not evenly spaced, an arrhythmia may be indicated. If the P-R interval is greater than 0.2 seconds, it may suggest blockage of the AV node. The horizontal segment of this waveform preceding the P-wave is designated as the baseline or the isopotential line. The P-wave represents depolarization of the atrial musculature. The QRS complex is the combined result of the repolarization of the atria and depolarization of the ventricles, which occur almost simultaneously. The T- wave is the wave of ventricular repolarization, where as the U-wave, if present is generally believed to be the result of after potentials in the ventricular muscle. So, the duration amplitude and morphology of the QRS complex is useful in diagnosing cardiac arrhythmias, conduction abnormalities, ventricular hypertrophy, myocardial infection and other disease states. Various abnormalities and their characteristic features are listed in table 1. II. RESEARCH WORK & LITERATURE REVIEW AT A GLANCE Although the first attempt to automate ECG analysis by digital computer was made as early as in 1956 by Pipberger and his group, but the first industrial ECG processing system came in the market during seventies. Since then many investigative and commercial minicomputer-based and microcomputer based system have become common in