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The human heart is a complex system exhibiting stochastic nature, asreflected in electrocardiogram (ECG) signals. ECG signal is a weak,non-stationary, and nonlinear signal, which indicates the health of a heart interms of temporal variations of electromagnetic pulses from the heart. Abnormalfluctuations in ECG signal invokes the possibility of various cardiovasculardisorders, which is diagnosed through intuitive analysis of the ECG reports bythe medical practitioners. This could be made fast, accurate, and simple byimposing advanced nonlinear tools on the recorded ECG signals. In this paper, awell-known nonlinear technique, i.e., Empirical Mode Decomposition (EMD) methodis adopted to extract the hidden information in the recorded ECG signal. Here,we try to explore the human heart as a dynamic model and perform EMD on ECGreports distinguishing arrhythmia from normal data obtained from the widelyused MIT-BIH database. EMD essentially involves the decomposition of the signalinto a finite number of Intrinsic Mode Functions (IMFs), keeping its originalproperties unaltered. For analysis, we use the powerful Savitzky-Golay (SG)filter for removing non-stationary noises from the ECG signals. The popularnonlinear parameter Hurst Exponent (H) is estimated for every IMF by R/Stechnique. We identified a distinct margin of the H of 1st IMFs in between thenormal and the arrhythmia affected patients. Our model confirms with 94.92%certainty the chances of occurrence of arrhythmia disease in patients bydiagnosing ECG signals without performing other expensive and time-consumingtechniques such as Holter test, echocardiogram, and stress test.

Nonlinear and statistical analysis of ECG signals from Arrhythmia affected cardiac system through the EMD process