{
  "abstract": "BACKGROUND: Traditional sleep metrics from polysomnography are often limited by the hypnogram, in which each 30-s epoch is classified into one of the five sleep stages. This oversimplifies sleep stage continuity and fragmented sleep, typical in sleep-disordered breathing (SDB). Deep learning offers more dynamic sleep modelling with a higher temporal resolution by providing probabilities for each sleep stage, known as hypnodensities. RESEARCH QUESTION AND METHODS: We investigated whether high-frequency (1/s) hypnodensities captured from nocturnal fingertip photoplethysmography (PPG) recordings better correlate with patient-reported outcomes in SDB than traditional metrics. RESULTS: Among 2,280 patients (65% male, mean age 62 years, mean apnoea-hypopnoea index (AHI) 23.5/h), we found that several PPG-derived hypnodensity metrics correlate with self-reported daytime fatigue and sleepiness. The level of correlation was better or similar compared to traditional hypnogram-based sleep metrics derived from manually scored polysomnography. Specifically, PPG-based hypnodensities for N1 and N3 sleep showed a stronger association (p < 0.005) with fatigue and sleepiness than manually scored N1 and N3 sleep. Multivariable logistic regression (adjusted for age, sex, body mass index, AHI, and total sleep time below 90% blood oxygen saturation) revealed significant associations (p < 0.01) between multiple PPG-based hypnodensity metrics and daytime sleepiness. CONCLUSION: These findings suggest that PPG-based hypnodensities improve the characterisation of sleep quality and better predict patient-reported outcomes in SDB patients. Future uses may include tracking sleep quality with fingertip pulse oximeters outside of sleep centres, potentially over multiple nights due to their simplicity.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Matias Rusanen"
    },
    {
      "name": "Viateur Tuyinsenge"
    },
    {
      "name": "Sandrine Muller"
    },
    {
      "name": "Renaud Tamisier"
    },
    {
      "name": "Sébastien Baillieul"
    },
    {
      "name": "Sami Myllymaa"
    },
    {
      "name": "Samu Kainulainen"
    },
    {
      "name": "Timo Leppänen"
    },
    {
      "name": "Jean-Louis Pepin"
    },
    {
      "name": "Sébastien Bailly"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/54k25qhc4nj8b.bib",
  "cited_by": 0,
  "doi": "10.1080/25310429.2026.2683326",
  "fulltext_url": null,
  "id": "54k25qhc4nj8b",
  "issns": [
    "2531-0429",
    "2531-0437"
  ],
  "landing_url": "https://hal.science/hal-05744213v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "openalex",
      "2737|Obstructive Sleep Apnea Research"
    ]
  ],
  "openalex": "W7164501527",
  "pdf_url": "https://www.tandfonline.com/doi/pdf/10.1080/25310429.2026.2683326?needAccess=true",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-12",
  "publisher": "Taylor & Francis",
  "ris": "https://research.draconitic.com/paper/54k25qhc4nj8b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "hal-05744213",
      "source": "hal",
      "url": "https://hal.science/hal-05744213v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2737|Obstructive Sleep Apnea Research",
      "subfield": "physiology"
    }
  ],
  "title": "Hypnodensity sleep metrics from fingertip photoplethysmography are better associated with daytime sleepiness and fatigue than traditional metrics from polysomnography",
  "title_text": "Hypnodensity sleep metrics from fingertip photoplethysmography are better associated with daytime sleepiness and fatigue than traditional metrics from polysomnography",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:33Z",
  "url": "https://research.draconitic.com/paper/54k25qhc4nj8b",
  "venue": "Pulmonology",
  "versions": []
}