Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach

Cardiovascular diseases remain the leading cause of death in developed countries. This study introduces deep learning ensembles for arrhythmia detection and atrial fibrillation (AF) recurrence prediction from electrocardiogram scans, supported by explainable artificial intelligence (XAI) methods. Validation used two datasets: Guangdong Provincial People's Hospital, China (Dataset G, 1172 patients, 71.4 ± 6.3 years, 66% women, 20.5% with arrhythmia) and Liverpool Heart and Chest Hospital, UK (L, 909 patients, 60.5 ± 10.71 years, 33% women, 29.7% with arrhythmia). Our ensembles outperformed individual and voting models with the area under the receiver operating characteristic curve (ROC-AUC): 0.980 (95%CI: 0.956-0.998, p = 0.03) for Dataset G, 0.799 (95%CI: 0.737-0.856, p = 0.07) for Dataset L. The models trained on combined training sets achieved ROC-AUC: 0.980 (95%CI: 0.952-1.0) and 0.800 (95% CI: 0.739-0.861) for the G and L test sets. Precision-recall AUC for AF recurrence was 0.765 (95%CI: 0.669-0.849) for ensembles vs. 0.737 (95%CI: 0.648-0.821) for individual models. XAI enhanced interpretability for clinical applications.

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Publication Details

Journal
npj Digital Medicine
Published
2025-11-06
DOI
https://doi.org/10.1038/s41746-025-01932-4
Primary Topic
ECG Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach

Jacek Kawa, Damian Kucharski, Hanadi Aldosari, Lü Fu et al.
npj Digital Medicine
ECG Monitoring and Analysis
article

Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach

Jacek Kawa, Damian Kucharski, Hanadi Aldosari, Lü Fu, Jakub Nalepa, Weidong Lin, Agata M. Wijata, Frans Coenen, Dhiraj Gupta, Yalin Zheng, Yumei Xue, Gregory Lip, Arkadiusz Paweł Czerwiński
article en

Abstract

Cardiovascular diseases remain the leading cause of death in developed countries. This study introduces deep learning ensembles for arrhythmia detection and atrial fibrillation (AF) recurrence prediction from electrocardiogram scans, supported by explainable artificial intelligence (XAI) methods. Validation used two datasets: Guangdong Provincial People's Hospital, China (Dataset G, 1172 patients, 71.4 ± 6.3 years, 66% women, 20.5% with arrhythmia) and Liverpool Heart and Chest Hospital, UK (L, 909 patients, 60.5 ± 10.71 years, 33% women, 29.7% with arrhythmia). Our ensembles outperformed individual and voting models with the area under the receiver operating characteristic curve (ROC-AUC): 0.980 (95%CI: 0.956-0.998, p = 0.03) for Dataset G, 0.799 (95%CI: 0.737-0.856, p = 0.07) for Dataset L. The models trained on combined training sets achieved ROC-AUC: 0.980 (95%CI: 0.952-1.0) and 0.800 (95% CI: 0.739-0.861) for the G and L test sets. Precision-recall AUC for AF recurrence was 0.765 (95%CI: 0.669-0.849) for ensembles vs. 0.737 (95%CI: 0.648-0.821) for individual models. XAI enhanced interpretability for clinical applications.

npj Digital MedicineVol. 8(1)
Silesian University of Technology (PL), Medical University of Białystok (PL), University of Liverpool (GB), Soegijapranata Catholic University (ID), Liverpool Heart and Chest Hospital (GB), Institute of Medical Technology and Equipment (PL), Guangdong Academy of Medical Sciences (CN), Guangdong Provincial People's Hospital (CN), Liverpool John Moores University (GB), Aalborg University (DK)
Silesian University of Technology, Infrastruktura PL-Grid, Academic Computer Centre Cyfronet, AGH University of Science and Technology
Openalex Percentile: Top 50%
ECG Monitoring and Analysis
DOIOpen Access PDFOpenAlex
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