Multi-Distance Dispersion Entropy for ECG Signal Classification

Abstract

Automatic detection of heartbeat is critical for early cardiovascular disease prevention and diagnosis. Traditional feature methodologies based on expert knowledge cannot abstract and represent multidimensional and multi-view information. Hence traditional research on heartbeat detection pattern recognition cannot produce adequate results. The proposed method in this research used Dispersion Entropy (DisEn) on Multidistance Signal Level Difference (MSLD) for feature extraction and Support Vector Machine (SVM) method for classifying the ECG signals. DisEn generates 20 DE values as feature vectors for each MSLD signal with a distance D of 1 to 20. The datasets used in this research were obtained from the MIT-BIH Arrhythmia database of ECG signals that consist of forty-five patients was captured using 200 [adu/mV] amplification and a sampling frequency of 360 Hz. The experiments result using 5-fold cross-validation revealed that at distance D= 1-15 had the highest accuracy of 91% to classify the ECG data into Normal Sinus Rhythm (NSR), Left Bundle Branch Block (LBBB), and Atrial Fibrillation (AFIB) from the MIT-BIH Arrhythmias database.