Detection of Ventricular Fibrillation Based on Time Domain Analysis

This study proposes feature extraction using Hilbert transforms and phase space reconstruction to detect ventricular fibrillation (VF) and normal sinus rhythm (NSR) from ECG episodes. We implemented three pre-processing steps to extract features from ECG episodes. In the first step, we use Hilbert transforms to extract peaks. In the second step, we use statistical methods and extract 4 features from the peaks. In the final step, we extract 4 features using statistical methods based on the Euclidean distance between the origin (0, 0) and the peaks after the peaks are plotted in a two dimensional phase space diagram. We applied the 8 features as inputs to a neural network with weighted fuzzy membership functions (NEWFM), and recorded sensitivity, specificity, and accuracy performances of 76.37%, 89.18%, and 86.63%, respectively.