MuRAt-CAP-Net: A novel multi-input residual attention network for automated detection of A-phases and subtypes in cyclic alternating patterns
Article
Yaman, Suleyman, Güler, Hasan, Sengur, Abdulkadir, Hafeez-Baig, Abdul and Acharya, U. Rajendra. 2025. "MuRAt-CAP-Net: A novel multi-input residual attention network for automated detection of A-phases and subtypes in cyclic alternating patterns." Biomedical Signal Processing and Control. 110 (Part A). https://doi.org/10.1016/j.bspc.2025.108221
| Article Title | MuRAt-CAP-Net: A novel multi-input residual attention network for automated detection of A-phases and subtypes in cyclic alternating patterns |
|---|---|
| ERA Journal ID | 3391 |
| Article Category | Article |
| Authors | Yaman, Suleyman, Güler, Hasan, Sengur, Abdulkadir, Hafeez-Baig, Abdul and Acharya, U. Rajendra |
| Journal Title | Biomedical Signal Processing and Control |
| Journal Citation | 110 (Part A) |
| Article Number | 108221 |
| Number of Pages | 20 |
| Year | 2025 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 1746-8094 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bspc.2025.108221 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1746809425007323 |
| Abstract | Cyclic Alternating Pattern (CAP) is an essential biomarker for evaluating sleep microstructure, analyzing sleep stability and detecting various sleep disorders. The manual scoring of the CAP A-phase and its subtypes (A1, A2, A3) is a time-consuming, complex and expert-dependent. In this study, we propose a novel deep learning model named the multi-input residual attention CAP network (MuRAt-CAP-Net) for the automated detection of CAP A-phase and its subtypes. MuRAt-CAP-Net, with its multi-input architecture, simultaneously processes signals from four EEG channels (C4-P4, F4-C4, Fp2-F4, P4-O2) originating from different cortical areas. Additionally, the integrated attention mechanisms enable the model to focus on the most relevant features. The performance of MuRAt-CAP-Net was evaluated on both balanced and imbalanced datasets using a 5-fold cross-validation strategy. For A-phase classification, the model achieved an accuracy of 81.26 % and an F1-score of 81.13 % on the balanced dataset, while achieving 83.68 % accuracy and 88.38 % F1-score on the imbalanced dataset. For subtype classification, the model achieved an accuracy of 83.34 % and an F1-score of 83.38 % on the balanced dataset, and 87.31 % accuracy and 84.64 % F1-score on the imbalanced dataset. Compared to state-of-the-art methods, MuRAt-CAP-Net demonstrated superior performance in the detection of CAP A-phases and their subtypes. Furthermore, to enhance interpretability, Grad-CAM was applied to visualize the temporal and spectral focus of the MuRAt-CAP-Net’s decisions, revealing physiologically consistent patterns and supporting the clinical reliability of the model. Additionally, this study provides a comprehensive analysis of the impact of different EEG channel combinations, input window durations, and attention mechanisms on model performance. |
| Keywords | Attention mechanism; Cyclic alternating pattern; Sleep; Electroencephalogram; Multi-input deep learning |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Firat University, Turkey |
| School of Mathematics, Physics and Computing | |
| School of Business |
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