{
  "abstract": "Accurate detection of rapid eye movement (REM) sleep is critical for neurophysiological monitoring and early identification of sleep-related disorders. An entropy-driven adaptive knowledge distillation framework integrated with a lightweight Transformer was developed to support robust REM sleep detection from single-channel electroencephalogram (EEG) signals. By dynamically adjusting temperature scaling and distillation weighting based on teacher–student entropy, the proposed approach explicitly models uncertainty inherent in REM sleep. When evaluated under subject-independent cross-validation on three public datasets (Sleep-EDF, ISRUC-Sleep, and MASS), the proposed model outperformed CNN, LSTM, and standard Transformer baselines, achieving an average accuracy of 93.2% and an F1 score of 92.8%. Moreover, the distilled student model reduced training time and memory usage by approximately 30%, indicating potential suitability for future optimization toward real-time and wearable EEG applications. These results suggest that uncertainty-aware adaptive distillation may support accurate and computationally efficient REM sleep detection from single-channel EEG under subject-independent experimental conditions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Ji-Jer Huang"
    },
    {
      "name": "Shih-Chung Chen"
    },
    {
      "name": "Irawan Dwiwahyono"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3qe5bcyv3fgqy.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62389-3",
  "fulltext_url": null,
  "id": "3qe5bcyv3fgqy",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-62389-3",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7223459984",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62389-3_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/3qe5bcyv3fgqy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "10.1038/s41598-026-62389-3",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-62389-3"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|EEG and Brain-Computer Interfaces",
      "subfield": "neuroscience"
    }
  ],
  "title": "An adaptive transformer-based model with entropy-driven knowledge distillation for single-channel EEG REM sleep detection",
  "title_text": "An adaptive transformer-based model with entropy-driven knowledge distillation for single-channel EEG REM sleep detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:33Z",
  "url": "https://research.draconitic.com/paper/3qe5bcyv3fgqy",
  "venue": "Scientific Reports",
  "versions": []
}