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dc.contributor.authorCoutts, LV
dc.contributor.authorPlans, D
dc.contributor.authorBrown, AW
dc.contributor.authorCollomosse, J
dc.date.accessioned2020-11-02T10:43:43Z
dc.date.issued2020-10-31
dc.description.abstractThe ubiquity and commoditisation of wearable biosensors (fitness bands) has led to a deluge of personal healthcare data, but with limited analytics typically fed back to the user. The feasibility of feeding back more complex, seemingly unrelated measures to users was investigated, by assessing whether increased levels of stress, anxiety and depression (factors known to affect cardiac function) and general health measures could be accurately predicted using heart rate variability (HRV) data from wrist wearables alone. Levels of stress, anxiety, depression and general health were evaluated from subjective questionnaires completed on a weekly or twice-weekly basis by 652 participants. These scores were then converted into binary levels (either above or below a set threshold) for each health measure and used as tags to train Deep Neural Networks (LSTMs) to classify each health measure using HRV data alone. Three data input types were investigated: time domain, frequency domain and typical HRV measures. For mental health measures, classification accuracies of up to 83% and 73% were achieved, with five and two minute HRV data streams respectively, showing improved predictive capability and potential future wearable use for tracking stress and well-being.en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.identifier.citationArticle 103610en_GB
dc.identifier.doi10.1016/j.jbi.2020.103610
dc.identifier.grantnumberEP/P03196X/2en_GB
dc.identifier.urihttp://hdl.handle.net/10871/123453
dc.language.isoenen_GB
dc.publisherElsevieren_GB
dc.rights.embargoreasonUnder embargo until 31 October 2021 in compliance with publisher policyen_GB
dc.rights© 2020. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/en_GB
dc.subjectMachine learningen_GB
dc.subjectLSTMen_GB
dc.subjectHeart rate variabilityen_GB
dc.subjectMental healthen_GB
dc.subjectWearablesen_GB
dc.titleDeep learning with wearable based heart rate variability for prediction of mental and general healthen_GB
dc.typeArticleen_GB
dc.date.available2020-11-02T10:43:43Z
dc.identifier.issn1532-0464
exeter.article-number103610en_GB
dc.descriptionThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recorden_GB
dc.descriptionData Availability: The data collected in this study resides in a secure network and access to data for further analysis would require further ethics approval due to the data containing sensitive participant information, but may be available upon request.en_GB
dc.identifier.journalJournal of Biomedical Informaticsen_GB
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_GB
dcterms.dateAccepted2020-10-25
exeter.funder::Engineering and Physical Sciences Research Council (EPSRC)en_GB
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2020-10-25
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2020-11-02T10:41:35Z
refterms.versionFCDAM
refterms.panelCen_GB


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© 2020. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's licence is described as © 2020. This version is made available under the CC-BY-NC-ND 4.0 license: https://creativecommons.org/licenses/by-nc-nd/4.0/