SHOCKABLE VERSUS NONSHOCKABLE LIFE-THREATENING VENTRICULAR ARRHYTHMIAS USING DWT AND NONLINEAR FEATURES OF ECG SIGNALS

Shockable ventricular arrhythmias (VAs) such as ventricular tachycardia (VT) and ventricular fibrillation (VFib) are the life-threatening conditions requiring immediate attention. Cardiopulmonary resuscitation (CPR) and defibrillation are the significant immediate recommended treatments for these shockable arrhythmias to obtain the return of spontaneous circulation. However, accurate classification of these shockable VAs from nonshockable ones is the key step during defibrillation by automated external defibrillator (AED). Therefore, in this work, we have proposed a novel algorithm for an automated differentiation of shockable and nonshockable VAs from electrocardiogram (ECG) signal. The ECG signals are segmented into 5, 8 and 10 s. These segmented ECGs are subjected to four levels of discrete wavelet transformation (DWT). Various nonlinear features such as approximate entropy (E-a(x)), signal energy (Omega(x)), Fuzzy entropy (E-f(x)), Kolmogorov Sinai entropy (E-ks(x)), permutation entropy (E-p(x)), Renyi entropy (E-r(x)), sample entropy (E-s(x)), Shannon entropy (E-sh(x)), Tsallis entropy (E-t(x)), wavelet entropy (E-w(x)), fractal dimension (F-D(x)), Kolmogorov complexity (C-k(x)), largest Lyapunov exponent (E-LLE(x)), recurrence quantification analysis (RQA) parameters (RQ(i)(x)), Hurst exponent (H-x), activity entropy (E-ac(x)), Hjorth complexity (H-c(x)), Hjorth mobility (H-m(x)), modified multi scale entropy (E-mmsy(x)) and higher order statistics (HOS) bispectrum (Bi-i(x)) are obtained from the DWT coefficients. Later, these features are subjected to sequential forward feature selection (SFS) method and selected features are then ranked using seven ranking methods namely, Bhattacharyya distance, entropy, Fuzzy maximum relevancy and minimum redundancy (mRMR), receiver operating characteristic (ROC), Student's t-test, Wilcoxon and ReliefF. These ranked features are supplied independently into the k-Nearest Neighbor (kNN) classifier. Our proposed system achieved maximum accuracy, sensitivity and specificity of (i) 97.72%, 94.79% and 98.74% for 5 s, (ii) 98.34%, 95.49% and 99.14% for 8 s and (iii) 98.32%, 95.16% and 99.20% for 10 s of ECG segments using only ten features. The integration of the proposed algorithm with ECG acquisition systems in the intensive care units (ICUs) can help the clinicians to decipher the shockable and nonshockable life-threatening arrhythmias accurately. Hence, doctors can use the CPR or AED immediately and increase the chance of survival during shockable life-threatening arrhythmia intervals.