{
  "abstract": "Approximately 50% of the population in India is estimated to experience sleep-related disorders. Sleep deprivation is a prevalent condition that adversely impacts cognitive performance, neural functioning, and overall health. Electroencephalography (EEG) offers an objective means of capturing neural alterations associated with sleep loss, making it well-suited for automated detection frameworks. In this study, we explore the application of a Quantum Support Vector Machine and Hybrid Quantum Neural Networks to classify sleep-deprived and well-rested states using resting-state EEG signals. A comprehensive feature extraction pipeline is employed, incorporating spectral band power, band ratios, Hjorth parameters, and functional connectivity measures. These features are subsequently encoded into quantum states to construct a quantum kernel, which is then utilized for classification. Model performance is evaluated under both epoch-level and subject-level data partitioning schemes. The Hybrid Quantum Neural Network (HQNN) achieves the highest performance across both evaluation settings, attaining an accuracy of 96.88% at the epoch level and 81.25% at the subject level. The Quantum Support Vector Machine (QSVM) model achieves accuracies of 93.75% and 75.00% for epoch-level and subject-level evaluations, respectively. At subject-level and epoch-level evaluation, HQNN outperforms previously reported results (95.72% and 68.23%). Overall, these findings highlight the potential of quantum machine learning as a competitive approach for EEG-based sleep deprivation detection, with promising implications for real-world biomedical applications.",
  "abstract_shown": true,
  "affiliations": [
    "Maulana Abul Kalam Azad University of Technology"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "P. Sarma-Sarkar"
    },
    {
      "name": "R. Saini"
    },
    {
      "name": "P. P. Roy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6a3r705sn8tpf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.06.14.732153",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.06.14.732153v2.full",
  "id": "6a3r705sn8tpf",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.06.14.732153v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ]
  ],
  "openalex": "W7165206030",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.06.14.732153v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6a3r705sn8tpf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:43Z",
      "native_id": "10.64898/2026.06.14.732153",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.06.14.732153v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Quantum machine learning for detection of sleep deprivation from EEG signals",
  "title_text": "Quantum machine learning for detection of sleep deprivation from EEG signals",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:43Z",
  "url": "https://research.draconitic.com/paper/6a3r705sn8tpf",
  "venue": "bioRxiv",
  "versions": []
}