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dc.contributor.authorSvane, Jakob
dc.contributor.authorWiktorski, Tomasz
dc.contributor.authorØrn, Stein
dc.contributor.authorEftestøl, Trygve Christian
dc.identifier.citationSvane, J., Wiktorski, T., Stein, Ø. & Eftestøl, T.C. (2023) Recurrent Neural Networks for Artifact Correction in HRV Data During Physical Exercise. Nordic Machine Intelligence (NMI), 3 (1), 1-12.en_US
dc.description.abstractIn this paper, we propose the use of recurrent neural networks (RNNs) for artifact correction and analysis of heart rate variability (HRV) data. HRV can be a valuable metric for determining the function of the heart and the autonomic nervous system. When measured during exercise, motion artifacts present a significant challenge. Several methods for artifact correction have previously been proposed, none of them applying machine learning, and each presenting some limitations regarding an accurate representation of HRV metrics. RNNs offer the ability to capture patterns that might otherwise not be detected, yielding predictions where no prior physiological assumptions are needed. A hyperparameter search has been carried out to determine the best network configuration and the most important hyperparameters. The approach was tested on two extensive multi-subject data sets, one from a recreational bicycle race and the other from a laboratory experiment. The results demonstrate that RNNs outperform by order of magnitude existing methods with respect to the calculation of derived HRV metrics. However, they are not able to accurately fill in individual missing RR intervals in sequence. Future research should pursue improvements in the prediction of RR interval lengths and reduction in necessary training data.en_US
dc.publisherUniversity of Osloen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectnevrale nettverken_US
dc.titleRecurrent Neural Networks for Artifact Correction in HRV Data During Physical Exerciseen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2023 Author(s)en_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.source.journalNordic Machine Intelligence (NMI)en_US

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