Application of Empirical Mode Decomposition And Support Vector Machine Classifiers for Prediction of Sudden Cardiac Arrest in Susceptible Cases

Abstract

One of the primary causes of Sudden CardiacArrest (SCA) is Ventricular Fibrillation (V.F.). It is a serious arrhythmic condition wherein the heart beats chaotically rather than pumping blood. It is essential for the subject to get instant medical attention when ventricular fibrillation occurs. In this paper, a novel approach is presented where the Hilbert Huang Transform is used for prediction of such an event. The ECG signals are pre-processed for removal of noises, artifacts. The empirical mode decomposition (EMD), which is a part of Hilbert Huang Transform is applied to the de-noised ECG signal. Three features were extracted from the first IMF (Intrinsic Mode Function) obtained after EMD, viz; spectral entropy, lyapunov exponent and weighted frequency. Support Vector Machine (SVM) classifier was used to classify and predict the normal people and patients prone to sudden cardiac arrest, based on the extracted features from both the cases. By applying this technique, the results show that average classification accuracy of 95% was achieved in predicting an unknown signal, labelled as 'Normal' or 'Abnormal ECG signal'. A second SVM classifier was used for predicting the occurrence of V.F. in ECG signals prone to cardiac arrest within 15 minutes, which resulted in an accuracy of 55.5 %.