A context-aware temporal modeling through unified multi-scale temporal encoding and hierarchical sequence learning for single-channel EEG sleep staging.
Overview
Abstract
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.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
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Data
Datasets cited
- physionet.org/
content/ , at PhysioNet; found in “Data Availability”sleep-edfx
Data Availability
The data underlying this study are publicly available from PhysioNet as part of the Sleep-EDF dataset at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 3 October 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 MeSH terms, 39 references.
Cite
This paper
Vakili, A. A., Jahanshiri, S., & Salimi-Badr, A. (2026). A context-aware temporal modeling through unified multi-scale temporal encoding and hierarchical sequence learning for single-channel EEG sleep staging. PloS one, 21(9), e0358241. https://
BibTeX
@article{vakili2026conte
author = {Vakili, Amir Ali and Jahanshiri, Salar and Salimi-Badr, Armin},
title = {{A context-aware temporal modeling through unified multi-scale temporal encoding and hierarchical sequence learning for single-channel EEG sleep staging}},
journal = {PloS one},
year = {2026},
month = sep,
volume = {21},
number = {9},
pages = {e0358241},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/
url = {https://
pmid = {42814731},
pmcid = {PMC13626375}
}
RIS
TY - JOUR
AU - Vakili, Amir Ali
AU - Jahanshiri, Salar
AU - Salimi-Badr, Armin
TI - A context-aware temporal modeling through unified multi-scale temporal encoding and hierarchical sequence learning for single-channel EEG sleep staging
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/
VL - 21
IS - 9
SP - e0358241
SN - 1932-6203
PB - PLOS
DO - 10.1371/
UR - https://
LA - en
ER -
CSL-JSON
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