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  1. Artikel ; Online: Response to comment on "Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura Sleep Staging Algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography: A validation study of 96 participants and 421,045 epochs".

    Svensson, Thomas / Madhawa, Kaushalya / Nt, Hoang / Chung, Ung-Il / Svensson, Akiko Kishi

    Sleep medicine

    2024  Band 117, Seite(n) 217–218

    Mesh-Begriff(e) Humans ; Polysomnography ; Reproducibility of Results ; Sleep/physiology ; Algorithms
    Sprache Englisch
    Erscheinungsdatum 2024-03-14
    Erscheinungsland Netherlands
    Dokumenttyp Letter
    ZDB-ID 2012041-2
    ISSN 1878-5506 ; 1389-9457
    ISSN (online) 1878-5506
    ISSN 1389-9457
    DOI 10.1016/j.sleep.2024.03.014
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Verfügbar in ZB MED Köln/Königswinter

    Zs.A 5315: Hefte anzeigen Standort:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 635: Hefte anzeigen

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  2. Artikel ; Online: Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura sleep staging algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography: A validation study of 96 participants and 421,045 epochs.

    Svensson, Thomas / Madhawa, Kaushalya / Nt, Hoang / Chung, Ung-Il / Svensson, Akiko Kishi

    Sleep medicine

    2024  Band 115, Seite(n) 251–263

    Abstract: Purpose: To evaluate the validity and the reliability of the Oura Ring Generation 3 (Gen3) with Oura Sleep Staging Algorithm 2.0 (OSSA 2.0) through multi-night polysomnography (PSG).: Participants and methods: Participants were 96 generally healthy ... ...

    Abstract Purpose: To evaluate the validity and the reliability of the Oura Ring Generation 3 (Gen3) with Oura Sleep Staging Algorithm 2.0 (OSSA 2.0) through multi-night polysomnography (PSG).
    Participants and methods: Participants were 96 generally healthy Japanese men and women aged between 20 and 70 years contributing with 421,045 30-s epochs. Sleep scoring was performed according to American Academy of Sleep Medicine criteria. Each participant could contribute with a maximum of three polysomnography (PSG) nights. Within-participant means were created for each sleep measure and paired t-tests were used to compare equivalent measures obtained from the PSG and Oura Rings (non-dominant and dominant hand). Agreement between sleep measures were assessed using Bland-Altman plots. Interrater reliability for epoch accuracy was determined by prevalence-adjusted and bias-adjusted kappa (PABAK).
    Results: The Oura Ring did not significantly differ from PSG for the measures time in bed, total sleep time, sleep onset latency, sleep period time, wake after sleep onset, time spent in light sleep, and time spent in deep sleep. Oura Rings worn on the non-dominant- and dominant-hand underestimated sleep efficiency by 1.1 %-1.5 % and time spent in REM sleep by 4.1-5.6 min. The Oura Ring had a sensitivity of 94.4 %-94.5 %, specificity of 73.0 %-74.6 %, a predictive value for sleep of 95.9 %-96.1 %, a predictive value for wake of 66.6 %-67.0 %, and accuracy of 91.7 %-91.8 %. PABAK was 0.83-0.84 and reliability was 94.8 %. Sleep staging accuracy ranged between 75.5 % (light sleep) and 90.6 % (REM sleep).
    Conclusions: The Oura Ring Gen3 with OSSA 2.0 shows good agreement with PSG for global sleep measures and time spent in light and deep sleep.
    Mesh-Begriff(e) Male ; Humans ; Female ; Young Adult ; Adult ; Middle Aged ; Aged ; Polysomnography ; Actigraphy ; Reproducibility of Results ; Sleep ; Algorithms
    Sprache Englisch
    Erscheinungsdatum 2024-01-26
    Erscheinungsland Netherlands
    Dokumenttyp Journal Article
    ZDB-ID 2012041-2
    ISSN 1878-5506 ; 1389-9457
    ISSN (online) 1878-5506
    ISSN 1389-9457
    DOI 10.1016/j.sleep.2024.01.020
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Volltext online

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    Verfügbar in ZB MED Köln/Königswinter

    Zs.A 5315: Hefte anzeigen Standort:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 635: Hefte anzeigen

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  3. Buch ; Online: Graph Homomorphism Convolution

    NT, Hoang / Maehara, Takanori

    2020  

    Abstract: In this paper, we study the graph classification problem from the graph homomorphism perspective. We consider the homomorphisms from $F$ to $G$, where $G$ is a graph of interest (e.g. molecules or social networks) and $F$ belongs to some family of graphs ...

    Abstract In this paper, we study the graph classification problem from the graph homomorphism perspective. We consider the homomorphisms from $F$ to $G$, where $G$ is a graph of interest (e.g. molecules or social networks) and $F$ belongs to some family of graphs (e.g. paths or non-isomorphic trees). We show that graph homomorphism numbers provide a natural invariant (isomorphism invariant and $\mathcal{F}$-invariant) embedding maps which can be used for graph classification. Viewing the expressive power of a graph classifier by the $\mathcal{F}$-indistinguishable concept, we prove the universality property of graph homomorphism vectors in approximating $\mathcal{F}$-invariant functions. In practice, by choosing $\mathcal{F}$ whose elements have bounded tree-width, we show that the homomorphism method is efficient compared with other methods.

    Comment: 37th International Conference on Machine Learning (ICML 2020)
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Discrete Mathematics ; Mathematics - Combinatorics ; Statistics - Machine Learning
    Thema/Rubrik (Code) 511
    Erscheinungsdatum 2020-05-03
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Buch ; Online: A Simple Proof of the Universality of Invariant/Equivariant Graph Neural Networks

    Maehara, Takanori / NT, Hoang

    2019  

    Abstract: We present a simple proof for the universality of invariant and equivariant tensorized graph neural networks. Our approach considers a restricted intermediate hypothetical model named Graph Homomorphism Model to reach the universality conclusions ... ...

    Abstract We present a simple proof for the universality of invariant and equivariant tensorized graph neural networks. Our approach considers a restricted intermediate hypothetical model named Graph Homomorphism Model to reach the universality conclusions including an open case for higher-order output. We find that our proposed technique not only leads to simple proofs of the universality properties but also gives a natural explanation for the tensorization of the previously studied models. Finally, we give some remarks on the connection between our model and the continuous representation of graphs.
    Schlagwörter Computer Science - Machine Learning ; Statistics - Machine Learning
    Erscheinungsdatum 2019-10-09
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Buch ; Online: Revisiting Graph Neural Networks

    NT, Hoang / Maehara, Takanori

    All We Have is Low-Pass Filters

    2019  

    Abstract: Graph neural networks have become one of the most important techniques to solve machine learning problems on graph-structured data. Recent work on vertex classification proposed deep and distributed learning models to achieve high performance and ... ...

    Abstract Graph neural networks have become one of the most important techniques to solve machine learning problems on graph-structured data. Recent work on vertex classification proposed deep and distributed learning models to achieve high performance and scalability. However, we find that the feature vectors of benchmark datasets are already quite informative for the classification task, and the graph structure only provides a means to denoise the data. In this paper, we develop a theoretical framework based on graph signal processing for analyzing graph neural networks. Our results indicate that graph neural networks only perform low-pass filtering on feature vectors and do not have the non-linear manifold learning property. We further investigate their resilience to feature noise and propose some insights on GCN-based graph neural network design.

    Comment: 12 pages, 5 figures, 2 tables
    Schlagwörter Statistics - Machine Learning ; Computer Science - Information Theory ; Computer Science - Machine Learning ; Mathematics - Spectral Theory
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2019-05-23
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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