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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.

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