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    Deep Neural Network Architecture Search for Wearable Heart Rate Estimations.

    Ray, Daniel, Collins, Tim ORCID logoORCID: https://orcid.org/0000-0003-2841-1947 and Ponnapalli, Prasad (2021) Deep Neural Network Architecture Search for Wearable Heart Rate Estimations. Studies in health technology and informatics, 281. pp. 1106-1107. ISSN 0926-9630

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    Abstract

    Extracting accurate heart rate estimations from wrist-worn photoplethysmography (PPG) devices is challenging due to the signal containing artifacts from several sources. Deep Learning approaches have shown very promising results outperforming classical methods with improvements of 21% and 31% on two state-of-the-art datasets. This paper provides an analysis of several data-driven methods for creating deep neural network architectures with hopes of further improvements.

    Item Type: Article
    Peer-reviewed: Yes
    Date Deposited: 18 Oct 2021 08:30
    Publisher: IOP Press
    Additional Information: This is an Open Access article published in IOP Press.
    Divisions: Faculties > Science and Engineering
    Subject terms: Artifacts, Heart Rate, Algorithms, Signal Processing, Computer-Assisted, Wearable Electronic Devices, Neural Networks, Computer, Artifacts, Heart Rate, Algorithms, Signal Processing, Computer-Assisted, Wearable Electronic Devices, Neural Networks, Computer, deep neural networks, heart rate, network architecture search, photoplethysmography, wearables, Algorithms, Artifacts, Heart Rate, Neural Networks, Computer, Signal Processing, Computer-Assisted, Wearable Electronic Devices, 0807 Library and Information Studies, 1117 Public Health and Health Services, Medical Informatics
    URI: https://e-space.mmu.ac.uk/id/eprint/627922
    DOI: https://doi.org/10.3233/shti210366
    ISSN 0926-9630
    e-ISSN 1879-8365

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