Hurst Exponent-Based Nonlinear Analysis for the Identification of Arrhythmia-Affected Cardiac Systems

The stochastic nature of the human heart, a complex biological system, is evident from electrocardiogram (ECG) signals, which are weak, non-linear and non-stationary signals. These temporal variations of electromagnetic pulses emanated from the heart are instrumental in indicating the cardiac health. The Empirical Mode Decomposition (EMD) technique was employed in order to decompose a total of 64 ECG signal data of arrhythmic and normal subjects, obtained from widely used MIT-BIH databases, into a finite number of Intrinsic Mode Functions (IMFs). The rationale behind using this strategy was to extract non-linear features of ECG signals which are not explicitly expressed, while keeping the original signal unaltered. Following removal of non-stationary noises from the ECG signals by the Savitzky-Golay (SG) filter, popular non-linear parameter Hurst Exponent (H) was estimated for every IMF by employing the R/S technique. A distinct difference between H values of 1st IMFs between normal individuals and arrhythmia affected patients was identified. This observation was further validated through an age-based and gender-based analysis, which demonstrated a unique alteration pattern with age. The study showed 94.92% probability in detection of arrhythmia in a patient. Adopting this EMD-based procedure for ECG data analysis and disease prediction may assist in reducing our dependence on intuition-based diagnosis of ECG reports by medical practitioners and may provide novel insights into the functioning of the human heart which might help develop new biomedical strategies to combat cardiac disorders.