Analysis of Coronary Heart Diseases by Kinetic Features: Applying Variational Mode Decomposition to ECG Signals and Classification Using Machine Learning Algorithms

Authors : Fırat Orhanbulucu, Fatma Latifoğlu, Ayşegül Güven, Semra İçer, Aigul Zhusupova

Pages : 133-137

View : 37 | Download : 30

Publication Date : 2024-12-22

Article Type : Research Paper

Abstract :This study presents an approach for the diagnosis of myocardial infarction (MI) and other coronary heart diseases using 12-lead electrocardiogram (ECG) signals. In the presented approach, 12-lead ECG signals recordings of MI types (STEMI-NSTEMI), other heart diseases (OHD) and healthy control (HC) participants, who presented to the Emergency Department of Erciyes University Hospital for heart disease, were used. In the first stage, the noise-cleaned ECG signals were decomposed into subbands by applying the Variational Mode Decomposition (VMD) method and kinetic features were obtained, and the ones that would positively affect the performance of the classifiers were determined by Chi-square test. In the classification stage, these features were evaluated by Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) algorithms, and AUC, Accuracy, and Negative Predictive Value ratios were obtained. Classification procedures were performed for HC-OHD, HC-MI (NSTEMI+STEMI), and STEMI-NSTEMI-OHD groups. When evaluated in terms of AUC, rates that can be considered successful (80% and above) were obtained. The findings of this research may contribute to the systems that can be developed for the rapid and accurate diagnosis of coronary heart diseases from ECG signals, which can be difficult to interpret manually.
Keywords : Koroner kalp hastalığı, 12 derivasyonlu elektrokardiyogram (EKG) sinyali, Kinetik özellikler, Varyasyonel mod ayrıştırma, Makine öğrenimi algoritmaları