Volume 16, Issue 6 And S7 (Artificial Intelligence- In Press 2026)
J Research Health 2026, 16(6 And S7): 5-5 |
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Ahmadabadi M, Hosseini-Nejad H, Rajabipoor A. A Hierarchical ECG-Based Framework for Inter-Patient Detection of Multiple Cardiac Arrhythmias. , J Research Health. 2026; 16 (6 and S7) :5-5
URL: http://jrh.gmu.ac.ir/article-1-3204-en.html
URL: http://jrh.gmu.ac.ir/article-1-3204-en.html
1- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
2- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran ,hosseini_nezhad@kntu.ac.ir
3- University of Medical Sciences, Gilan, Iran
2- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran ,
3- University of Medical Sciences, Gilan, Iran
Abstract: (16 Views)
Background: Cardiac arrhythmias exhibit diverse electrophysiological characteristics, making their simultaneous detection from electrocardiogram (ECG) signals challenging. Moreover, the generalizability of automated detection methods to previously unseen patients is an important consideration for their practical application. This study proposes a hierarchical ECG-based framework for the inter-patient detection of five clinically relevant arrhythmias: ventricular tachycardia/ventricular fibrillation (VT/VF), atrial fibrillation (AF), premature ventricular contractions (PVC), premature atrial contractions (PAC), and Wolff-Parkinson-White (WPW) syndrome.
Methods: Rather than treating all arrhythmias as a single multiclass classification problem, the proposed framework sequentially analyzes the ECG using arrhythmia-specific characteristics. VT/VF detection is based on signal periodicity and complexity measures, AF is identified using heart rate variability and Poincaré-derived features, PVC and PAC are detected using RR-interval characteristics followed by wavelet-based discrimination, and WPW is identified from PR-interval and QRS-duration characteristics. The framework was evaluated using ECG recordings from four databases under a leave-one-subject-out inter-patient protocol.
Results: The VT/VF, AF, PVC/PAC detection, PVC-PAC discrimination, and WPW stages achieved accuracies of 98.90%, 97.06%, 98.64%, 97.73%, and 95.15%, respectively.
Conclusion: The proposed hierarchical framework achieved high inter-patient performance across all detection stages. These findings indicate that combining arrhythmia-specific ECG characteristics within a sequential framework can provide effective detection of different rhythm- and beat-level abnormalities in previously unseen subjects.
Methods: Rather than treating all arrhythmias as a single multiclass classification problem, the proposed framework sequentially analyzes the ECG using arrhythmia-specific characteristics. VT/VF detection is based on signal periodicity and complexity measures, AF is identified using heart rate variability and Poincaré-derived features, PVC and PAC are detected using RR-interval characteristics followed by wavelet-based discrimination, and WPW is identified from PR-interval and QRS-duration characteristics. The framework was evaluated using ECG recordings from four databases under a leave-one-subject-out inter-patient protocol.
Results: The VT/VF, AF, PVC/PAC detection, PVC-PAC discrimination, and WPW stages achieved accuracies of 98.90%, 97.06%, 98.64%, 97.73%, and 95.15%, respectively.
Conclusion: The proposed hierarchical framework achieved high inter-patient performance across all detection stages. These findings indicate that combining arrhythmia-specific ECG characteristics within a sequential framework can provide effective detection of different rhythm- and beat-level abnormalities in previously unseen subjects.
Keywords: Electrocardiogram (ECG), Cardiac arrhythmia, Inter-patient classification, Feature extraction, Hierarchical classification
Type of Study: Orginal Article |
Subject:
● Artificial Intelligence
Received: 2026/08/24 | Accepted: 2026/09/19 | Published: 2026/11/11
Received: 2026/08/24 | Accepted: 2026/09/19 | Published: 2026/11/11
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