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All Journal Bulletin of Electrical Engineering and Informatics
Anggreni Matondang, Nia
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Electrical & Electronics Engineering

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Analysis of arrhythmia detection and classification using electrocardiogram signals with decision tree algorithm Marpaung, Putri Juniarti; Anggreni Matondang, Nia; Margaretta Siregar, Rince; Ester Novelia Siahaan, Angelica; Dharma, Abdi; Turnip, Arjon; Turnip, Mardi
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10317

Abstract

Heart disease remains the primary cause of death globally, with arrhythmia diagnosis often limited by restricted access to medical personnel and the complexity of electrocardiogram (ECG) interpretation. Accurate arrhythmia classification is essential to prevent cardiovascular complications. The proposed method successfully categorized classify ECG signals into five categories: normal, abnormal, potentially arrhythmia, moderate arrhythmia risk, and highly potentially arrhythmia. Data were collected from 30 subjects under three activity scenarios: sitting, walking, and running. The proposed model achieved an accuracy of 99.4%, demonstrating strong potential for real-time monitoring applications. Performance evaluation was conducted using accuracy, precision, recall, and F1-score for each class. Although the dataset size remains relatively small, the findings highlight the effectiveness of decision tree as an efficient and interpretable classification method. Future research will involve validation using large-scale public databases like the arrhythmia database at MIT-BIH and comparisons with advanced methods including convolutional neural network (CNN), transformer-based models, and explainable artificial intelligent (XAI) frameworks.
Co-Authors Abdi Dharma Arjon Turnip Ester Novelia Siahaan, Angelica Mardi Turnip, Mardi Margaretta Siregar, Rince Marpaung, Putri Juniarti