Open AccessAbnormal ECG Classification using Empirical Mode Decomposition and Entropy
Author(s) -
Suci Aulia,
Sugondo Hadiyoso
Publication year - 2021
Publication title -
jurnal rekayasa elektrika
Language(s) - English
Resource type - Journals
eISSN - 2252-620X
pISSN - 1412-4785
DOI - 10.17529/jre.v17i3.22070
Subject(s) - pattern recognition (psychology) , left bundle branch block , artificial intelligence , hilbert–huang transform , feature extraction , support vector machine , entropy (arrow of time) , sample entropy , computer science , right bundle branch block , approximate entropy , atrial fibrillation , electrocardiography , cardiology , mathematics , medicine , computer vision , heart failure , physics , filter (signal processing) , quantum mechanics
Heart disease is one of the leading causes of death in the world. Early detection followed by therapy is one of the efforts to reduce the mortality rate of this disease. One of the leading medical instruments for diagnosing heart disorders is the electrocardiogram (ECG). The shape of the ECG signal represents normal or abnormal heart conditions. Some of the most common heart defects are atrial fibrillation and left bundle branch block. Detection or classification can be difficult if performed visually. Therefore in this study, we propose a method for the automatic classification of ECG signals. This method generally consists of feature extraction and classification. The feature extraction used is based on information theory, namely Fuzzy entropy and Shannon entropy, which is calculated on the decomposed signal. The simulated ECG signals are of three types: normal sinus rhythm, atrial fibrillation, and left bundle branch block. Support vector machine and k-Nearest Neighbor algorithms were employed for the validation performance of the proposed method. From the test results obtained, the highest accuracy is 81.1%. With specificity and sensitivity of 79.4% and 89.8%, respectively. It is hoped that this proposed method can be further developed to assist clinical diagnosis.