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  1. AU="Katori, Machiko"
  2. AU="Richter, Susanna"
  3. AU="Oladipo, Aishat T"
  4. AU="Arango, Alissa"
  5. AU=Manjili Rose H AU=Manjili Rose H
  6. AU=Chen Hongtao
  7. AU="Soto Alsar, Javier"
  8. AU="Eric Woode"
  9. AU="Zybina O"
  10. AU="Reynolds, Cecil R"
  11. AU="Shahidul Khan"
  12. AU="Vasisth, Rashi"
  13. AU="Raju Mandal"
  14. AU="Owen, Noel L"
  15. AU=Liu Xiaolei
  16. AU="Fırıncıoğluları, Ali"
  17. AU="Piepel, Christiane"
  18. AU="Saremi, Saeid"
  19. AU="Dunxian She"

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  1. Artikel ; Online: The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes.

    Katori, Machiko / Shi, Shoi / Ode, Koji L / Tomita, Yasuhiro / Ueda, Hiroki R

    Proceedings of the National Academy of Sciences of the United States of America

    2022  Band 119, Heft 12, Seite(n) e2116729119

    Abstract: SignificanceHuman sleep phenotypes are diversified by genetic and environmental factors, and a quantitative classification of sleep phenotypes would lead to the advancement of biomedical mechanisms underlying human sleep diversity. To achieve that, a ... ...

    Abstract SignificanceHuman sleep phenotypes are diversified by genetic and environmental factors, and a quantitative classification of sleep phenotypes would lead to the advancement of biomedical mechanisms underlying human sleep diversity. To achieve that, a pipeline of data analysis, including a state-of-the-art sleep/wake classification algorithm, the uniform manifold approximation and projection (UMAP) dimension reduction method, and the density-based spatial clustering of applications with noise (DBSCAN) clustering method, was applied to the 100,000-arm acceleration dataset. This revealed 16 clusters, including seven different insomnia-like phenotypes. This kind of quantitative pipeline of sleep analysis is expected to promote data-based diagnosis of sleep disorders and psychiatric disorders that tend to be complicated by sleep disorders.
    Mesh-Begriff(e) Acceleration ; Biological Specimen Banks ; Humans ; Phenotype ; Sleep ; Sleep Wake Disorders ; United Kingdom
    Sprache Englisch
    Erscheinungsdatum 2022-03-18
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2116729119
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration.

    Ode, Koji L / Shi, Shoi / Katori, Machiko / Mitsui, Kentaro / Takanashi, Shin / Oguchi, Ryo / Aoki, Daisuke / Ueda, Hiroki R

    iScience

    2022  Band 25, Heft 2, Seite(n) 103727

    Abstract: Arm acceleration data have been used to measure sleep-wake rhythmicity. Although several methods have been developed for the accurate classification of sleep-wake episodes, a method with both high sensitivity and specificity has not been fully ... ...

    Abstract Arm acceleration data have been used to measure sleep-wake rhythmicity. Although several methods have been developed for the accurate classification of sleep-wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep-wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep-wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.
    Sprache Englisch
    Erscheinungsdatum 2022-01-01
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2021.103727
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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