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  1. Article ; 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  Volume 117, Page(s) 217–218

    MeSH term(s) Humans ; Polysomnography ; Reproducibility of Results ; Sleep/physiology ; Algorithms
    Language English
    Publishing date 2024-03-14
    Publishing country Netherlands
    Document type 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
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5315: Show issues Location:
    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: Show issues

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  2. Article ; 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  Volume 115, Page(s) 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 term(s) Male ; Humans ; Female ; Young Adult ; Adult ; Middle Aged ; Aged ; Polysomnography ; Actigraphy ; Reproducibility of Results ; Sleep ; Algorithms
    Language English
    Publishing date 2024-01-26
    Publishing country Netherlands
    Document type 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
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5315: Show issues Location:
    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: Show issues

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  3. Article ; Online: Active Learning for Node Classification: An Evaluation.

    Madhawa, Kaushalya / Murata, Tsuyoshi

    Entropy (Basel, Switzerland)

    2020  Volume 22, Issue 10

    Abstract: Current breakthroughs in the field of machine learning are fueled by the deployment of deep neural network models. Deep neural networks models are notorious for their dependence on large amounts of labeled data for training them. Active learning is being ...

    Abstract Current breakthroughs in the field of machine learning are fueled by the deployment of deep neural network models. Deep neural networks models are notorious for their dependence on large amounts of labeled data for training them. Active learning is being used as a solution to train classification models with less labeled instances by selecting only the most informative instances for labeling. This is especially important when the labeled data are scarce or the labeling process is expensive. In this paper, we study the application of active learning on attributed graphs. In this setting, the data instances are represented as nodes of an attributed graph. Graph neural networks achieve the current state-of-the-art classification performance on attributed graphs. The performance of graph neural networks relies on the careful tuning of their hyperparameters, usually performed using a validation set, an additional set of labeled instances. In label scarce problems, it is realistic to use all labeled instances for training the model. In this setting, we perform a fair comparison of the existing active learning algorithms proposed for graph neural networks as well as other data types such as images and text. With empirical results, we demonstrate that state-of-the-art active learning algorithms designed for other data types do not perform well on graph-structured data. We study the problem within the framework of the exploration-vs.-exploitation trade-off and propose a new count-based exploration term. With empirical evidence on multiple benchmark graphs, we highlight the importance of complementing uncertainty-based active learning models with an exploration term.
    Language English
    Publishing date 2020-10-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e22101164
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: GraphNVP

    Madhawa, Kaushalya / Ishiguro, Katushiko / Nakago, Kosuke / Abe, Motoki

    An Invertible Flow Model for Generating Molecular Graphs

    2019  

    Abstract: We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model. We decompose the generation of a graph into two steps: generation of (i) an adjacency tensor and (ii) node attributes. This decomposition yields the exact ...

    Abstract We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model. We decompose the generation of a graph into two steps: generation of (i) an adjacency tensor and (ii) node attributes. This decomposition yields the exact likelihood maximization on graph-structured data, combined with two novel reversible flows. We empirically demonstrate that our model efficiently generates valid molecular graphs with almost no duplicated molecules. In addition, we observe that the learned latent space can be used to generate molecules with desired chemical properties.

    Comment: 12 pages, 7 figures
    Keywords Statistics - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Publishing date 2019-05-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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