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LightSleepX: A Lightweight, Inception-Based Dual-Modal Network for Sleep Staging — arXiv:2609.19062

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.19062
License (as archived): CC0-1.0
Archived: 2026-10-15T15:05:00.064Z
SHA-256 of the archived text: 2542aa0358db3ce71269e69a5a85a278b39e1a9a7f5fb493bd043fda6d738f73
Stable URL of this page: https://metraipolis.com/documento/dc_ee9e4b59bbce

How to cite

LightSleepX: A Lightweight, Inception-Based Dual-Modal Network for Sleep Staging — arXiv:2609.19062 — arXiv (en), https://arxiv.org/abs/2609.19062 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ee9e4b59bbce (Metraipolis archive dc_ee9e4b59bbce, sha256 2542aa0358db3ce71269e69a5a85a278b39e1a9a7f5fb493bd043fda6d738f73)

BibTeX

@misc{dcee9e4b59bbce,
  title = {LightSleepX: A Lightweight, Inception-Based Dual-Modal Network for Sleep Staging — arXiv:2609.19062},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.19062},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ee9e4b59bbce; sha256 2542aa0358db3ce71269e69a5a85a278b39e1a9a7f5fb493bd043fda6d738f73},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.19062. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automatic sleep staging is fundamental to personal health monitoring, yet many existing approaches are ill-suited for real-world applications. Traditional pipelines often rely on hand-crafted features or shallow machine learning models that struggle to generalize, while state-of-the-art deep learning methods, though accurate, are computationally heavy and impractical for resource-constrained environments. This paper introduces LightSleepX, a lightweight framework designed to deliver robust sleep analysis in resource-constrained environments. LightSleepX combines an Inception-style architecture with depthwise separable convolutions and Multi-scale Enhanced Attention for efficient multi-modal EEG/EOG feature extraction, and a Mamba encoder for rule-free long-range temporal modeling. On public benchmark datasets, LightSleepX achieves 85.9% accuracy and a 0.803 macro-F1 score on Sleep-EDF-20, and 81.8% accuracy and a 0.796 macro-F1 score on the cross-subject ISRUC-S3 dataset. With 0.049M parameters and 195.9 MFLOPs, the framework targets practical local deployment where computational cost and privacy are central constraints.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-17 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.19062 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T14:08:19.172Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).