XABH-CNN-GRU: Explainable attention-based hybrid CNN-GRU model for accurate identification of common arrhythmias

Abstract

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.

Keywords

Arrhythmias / Cardiac / Classification / Deep learning / Electrocardiogram (ECG)

Ethical statement

All data used in this study were obtained from publicly available databases, and no human or animal subjects were directly involved in the study. Therefore, ethical approval was not required.

CRediT authorship contribution statement

Abduljabbar S. Ba Mahel: Resources, Writing – original draft, Software, Data curation, Formal analysis, Methodology, Visualization, Validation. Fahad Mushabbab G. Alotaibi: Writing – review & editing, Conceptualization, Investigation. Zenebe Markos Lonseko: Resources, Software, Visualization. Ni-Ni Rao: Supervision, Funding acquisition, Formal analysis, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant No. 62271127, the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China, and the West China Hospital of Sichuan University under Grants No. ZYGX2022YGRH011 and No. HXDZ22005, the Natural Science Foundation of Sichuan, China under Grant No. 23NSFSC0627 and Sichuan Provincial Key Laboratory Fund for Ultra Sound Cardioelectrophysiology and Biomechanics, China under Grant No. 2023KFKT01.

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