الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Heart Rate Variability(HRV) signals, derived from an ECG signal, are strongly related to the activity of the autonomous nervous system (ANS). HRV is usually investigated as RR variability, since the R wave is far easier to detect due to its peaked shape. The classical methods based on autocorrelation, thresholds or derivatives, time domain methods and frequency domain methods give a coarse quantification of the variability, without distinguishing between short-term and long-term fluctuations.The objective of this work is for computer based classification of Electrocardiogram (ECG) arrhythmias with a focus on less computational time and better accuracy. As an initial stride in this direction, the present study is based on two methods namely : time domain analysis and decompositional analysis techniques to yield a hybrid method. Moreover, the analysis concerns with the overall heart beat different waveforms and intervals of ECG complex to derive and determine the feature ,of a disease. Results show that the introduced hybrid method is very effective and efficient for fast computation of ECG analysis and disease feature extraction.