XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias
Arrhythmias stand out for having irregular cardiac rhythms, and the fast diagnosis of arrhythmias holds significant clinical importance due to its potential to mitigate adverse health outcomes. Despite the progress in this field, existing research efforts have encountered limitations, necessitating...
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KeAi Communications Co., Ltd.
2025-09-01
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| Series: | Journal of Electronic Science and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674862X25000230 |
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| author | Abduljabbar S. Ba Mahel Fahad Mushabbab G. Alotaibi Zenebe Markos Lonseko Ni-Ni Rao |
| author_facet | Abduljabbar S. Ba Mahel Fahad Mushabbab G. Alotaibi Zenebe Markos Lonseko Ni-Ni Rao |
| author_sort | Abduljabbar S. Ba Mahel |
| collection | DOAJ |
| description | Arrhythmias stand out for having irregular cardiac rhythms, and the fast diagnosis of arrhythmias holds significant clinical importance due to its potential to mitigate adverse health outcomes. Despite the progress in this field, existing research efforts have encountered limitations, necessitating innovative approaches to address diagnostic challenges effectively. The primary objective of this research is to propose an innovative classification methodology for distinguishing five distinct arrhythmia classes: Atrial premature beat (A), normal (N), ventricular premature beat (V), right bundle branch block (R), and left bundle branch block (L). The proposed methodology involves constructing a hybrid model that incorporates an attention mechanism, utilizing electrocardiogram (ECG) data from an open-source repository. Additionally, we have incorporated an explainability feature into the model, allowing for the interpretation and explanation of its predictions. This model is designed to capitalize on the unique features of arrhythmic patterns and enhance classification metrics. Innovative techniques employed within the methodology are detailed to elucidate the rationale behind their selection and their anticipated contributions to improved model performance. Findings from this study underscore the superiority of the proposed classification model over existing methodologies. Quantitative analysis demonstrates its outstanding performance. The approach, outperforming existing methods, achieves high levels of accuracy (99.16%), specificity (99.79%), recall (99.2 %), precision (99.20%), F1-measure (99.16 %), and AUC (99.92%). This research advances medical diagnostics by integrating advanced machine-learning techniques to enhance arrhythmia detection. |
| format | Article |
| id | doaj-art-d7268e36107241ea98f39bcc108bea5b |
| institution | Kabale University |
| issn | 2666-223X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Electronic Science and Technology |
| spelling | doaj-art-d7268e36107241ea98f39bcc108bea5b2025-08-20T03:45:11ZengKeAi Communications Co., Ltd.Journal of Electronic Science and Technology2666-223X2025-09-0123310032210.1016/j.jnlest.2025.100322XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmiasAbduljabbar S. Ba Mahel0Fahad Mushabbab G. Alotaibi1Zenebe Markos Lonseko2Ni-Ni Rao3School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, ChinaSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, 11433, Saudi ArabiaSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, ChinaSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Corresponding author.Arrhythmias stand out for having irregular cardiac rhythms, and the fast diagnosis of arrhythmias holds significant clinical importance due to its potential to mitigate adverse health outcomes. Despite the progress in this field, existing research efforts have encountered limitations, necessitating innovative approaches to address diagnostic challenges effectively. The primary objective of this research is to propose an innovative classification methodology for distinguishing five distinct arrhythmia classes: Atrial premature beat (A), normal (N), ventricular premature beat (V), right bundle branch block (R), and left bundle branch block (L). The proposed methodology involves constructing a hybrid model that incorporates an attention mechanism, utilizing electrocardiogram (ECG) data from an open-source repository. Additionally, we have incorporated an explainability feature into the model, allowing for the interpretation and explanation of its predictions. This model is designed to capitalize on the unique features of arrhythmic patterns and enhance classification metrics. Innovative techniques employed within the methodology are detailed to elucidate the rationale behind their selection and their anticipated contributions to improved model performance. Findings from this study underscore the superiority of the proposed classification model over existing methodologies. Quantitative analysis demonstrates its outstanding performance. The approach, outperforming existing methods, achieves high levels of accuracy (99.16%), specificity (99.79%), recall (99.2 %), precision (99.20%), F1-measure (99.16 %), and AUC (99.92%). This research advances medical diagnostics by integrating advanced machine-learning techniques to enhance arrhythmia detection.http://www.sciencedirect.com/science/article/pii/S1674862X25000230ArrhythmiasCardiacClassificationDeep learningElectrocardiogram (ECG) |
| spellingShingle | Abduljabbar S. Ba Mahel Fahad Mushabbab G. Alotaibi Zenebe Markos Lonseko Ni-Ni Rao XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias Journal of Electronic Science and Technology Arrhythmias Cardiac Classification Deep learning Electrocardiogram (ECG) |
| title | XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias |
| title_full | XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias |
| title_fullStr | XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias |
| title_full_unstemmed | XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias |
| title_short | XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias |
| title_sort | xabh cnn gru explainable attention based hybrid cnn gru model for accurate identification of common arrhythmias |
| topic | Arrhythmias Cardiac Classification Deep learning Electrocardiogram (ECG) |
| url | http://www.sciencedirect.com/science/article/pii/S1674862X25000230 |
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