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All Journal International Journal of Artificial Intelligence Research Indonesian Journal on Software Engineering (IJSE) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Riset Informatika Journal of Applied Data Sciences Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Journal Medical Informatics Technology SWAGATI: Journal of Community Service
Nita Merlina
Universitas Nusa Mandiri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health Social Sciences Other

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ARRHYTHMIA DETECTION FROM IMBALANCED ECG DATA USING CNN, FOCAL LOSS, AND RR INTERVAL FEATURES WITH INTER-PATIENT EVALUATION Ilham Maulana; Nita Merlina
Jurnal Riset Informatika Vol. 8 No. 4 (2026): September 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i4.514

Abstract

Detection of arrhythmias from electrocardiogram (ECG) signals is crucial for early diagnosis of heart rhythm disorders. However, the imbalanced distribution of arrhythmia classes poses challenges for classification models. This study proposes a Convolutional Neural Network (CNN) model combined with Focal Loss and RR features to address class imbalance using an inter-patient evaluation approach. The dataset includes three classes: N (Normal), SVEB (Supraventricular Ectopic Beats), and VEB (Ventricular Ectopic Beats), divided into training (DS1), validation (DS1-Val), and testing (DS2) sets. The proposed CNN + Focal Loss + RR model achieved an accuracy of 95.75% and an F1-score of 95.19%. Additionally, Explainable AI (XAI) using LIME is applied to improve model interpretability by identifying important features influencing predictions. The results demonstrate that the proposed approach provides accurate and interpretable arrhythmia detection, supporting reliable medical decision-making. However, the model's performance on the minority SVEB class remains limited, with a recall of only 34.24%, indicating that SVEB beats are frequently misclassified as Normal due to morphological similarities and severe class imbalance. This limitation highlights the need for further improvements in minority-class detection for clinical applicability.
Co-Authors Agus Subekti Arfhan Prasetio Arfhan Prasetyo Arfita Adikvika Dea Amelia Eni Heni Hermaliani Esron Nainggolan Farisya Setiadi Faruq Aziz Filda Angellia Herdian Tohir Ida Zuniarti Ilham Maulana Isti Kharoh Marcella Irsictia Mayangky, Nissa Almira Mutmainah Putri Nurmalasari Nurmalasari Rahmadya Trias Handayanto Rani Risdiawati Ridan Nurfalah Riyan Latifahul Hasanah Siswahyudianto Sri Hadianti Sulistyowati, Daning Nur