2026-10-09T01:13:28Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34035623 2026-09-30T17:36:10Z category_146 category_15 category_21 category_734 category_931 category_64 portal_5 item_type_3 month_year_09_2026

<p>Experimental hyperparameters.</p> Amir Ali Vakili (25145354) Salar Jahanshiri (25145357) Armin Salimi-Badr (25145360) Space Science Neuroscience Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer work proposes temposleep widely available signal weighted loss functions scale feature extraction lacking clearly defined hierarchical sequence learning enhancing contextual representation scale temporal encoding proposed framework achieves automatic sleep staging address data imbalance challenging n1 stage aware temporal modeling temporal modeling aware framework n1 stage sleep disorders data augmentation field modeling unified multi substantial improvement study focuses stacked layers sleepedf datasets results indicate range dependencies providing insights previous methods prediction behavior potential application particular emphasis overall accuracy limited receptive insufficient interpretability improving detection healthcare due global prevalence final predictions epoch chunks eeg signals eeg ), critical task class imbalance channel electroencephalography black boxes 46 %. <div><p>Automatic sleep staging is a critical task in healthcare due to the global prevalence of sleep disorders. This study focuses on single-channel electroencephalography (EEG), a practical and widely available signal for automatic sleep staging. Existing approaches face challenges such as class imbalance, limited receptive-field modeling, and insufficient interpretability. This work proposes TempoSleep, a context-aware framework for single-channel EEG sleep staging, with particular emphasis on improving detection of the N1 stage. Many prior models operate as black boxes with stacked layers, lacking clearly defined and interpretable feature extraction roles. TempoSleep combines compact multi-scale feature extraction with temporal modeling to capture both local and long-range dependencies. To address data imbalance, especially in the N1 stage, class-weighted loss functions and data augmentation are applied. EEG signals are segmented into sub-epoch chunks, and final predictions are obtained by averaging softmax probabilities across chunks, enhancing contextual representation and robustness. The proposed framework achieves an overall accuracy of 89.72% and a macro-average F1-score of 85.46%. Notably, it attains an F1-score of 61.7% for the challenging N1 stage, demonstrating a substantial improvement over previous methods on the SleepEDF datasets.These results indicate that the proposed approach effectively improves sleep staging performance while providing insights into its prediction behavior and supporting its potential application in automatic sleep staging.</p></div> 2026-09-30T17:35:55Z Dataset Dataset 10.1371/journal.pone.0358241.t002 https://figshare.com/articles/dataset/_p_Experimental_hyperparameters_p_/34035623 CC BY 4.0