Classification of ECG signal-based cardiac abnormalities using fluctuation-based dispersion entropy and first-order statistics

Abstrak

The heart is one of the most important organs in the human body. The presence of abnormalities in the heart can be fatal for a person. One way to detect heart abnormalities is an Electrocardiogram (EKG) signal examination. To facilitate the detection of ECG signal abnormalities, an automatic classification method is needed. Therefore, in this study, a method for classifying ECG signals using FdispEn (Fluctuation-based dispersion Entropy) and first-order statistics is proposed. FdispEn measures the uncertainty in the signal and is expected to be able to distinguish the physiological state of the ECG signal time series. In this study FdispEn and statistical computing were used as feature extraction of the ECG signal and combined with the Support Vector Machine (SVM) for the classification process of Normal ECG, AFIB (Atrial Fibrilation), and CHF (Congestive Heart Failure). The method proposed in this study generates an accuracy of 91.5%. The system proposed in this study is expected to assist in the clinical diagnosis of abnormalities in the heart.