Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device
dc.contributor.author | Morelli, D | |
dc.contributor.author | Bartoloni, L | |
dc.contributor.author | Colombo, M | |
dc.contributor.author | Plans, D | |
dc.contributor.author | Clifton, DA | |
dc.date.accessioned | 2019-01-25T14:45:06Z | |
dc.date.issued | 2018-05-07 | |
dc.description.abstract | Wearable physiological monitors are becoming increasingly commonplace in the consumer domain, but in literature there exists no substantive studies of their performance when measuring the physiology of ambulatory patients. In this Letter, the authors investigate the reliability of the heart-rate (HR) sensor in an exemplar 'wearable' wrist-worn monitoring system (the Microsoft Band 2); their experiments quantify the propagation of error from (i) the photoplethysmogram (PPG) acquired by pulse oximetry, to (ii) estimation of HR, and (iii) subsequent calculation of HR variability (HRV) features. Their experiments confirm that motion artefacts account for the majority of this error, and show that the unreliable portions of HR data can be removed, using the accelerometer sensor from the wearable device. The experiments further show that acquired signals contain noise with substantial energy in the high-frequency band, and that this contributes to subsequent variability in standard HRV features often used in clinical practice. The authors finally show that the conventional use of long-duration windows of data is not needed to perform accurate estimation of time-domain HRV features. | en_GB |
dc.identifier.citation | Vol. 5, pp. 59 - 64 | en_GB |
dc.identifier.doi | 10.1049/htl.2017.0039 | |
dc.identifier.uri | http://hdl.handle.net/10871/35591 | |
dc.language.iso | en | en_GB |
dc.publisher | Institution of Engineering and Technology (IET) | en_GB |
dc.rights | © The Author(s) 2018. This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License (http:// creativecommons.org/licenses/by-nc/3.0/) | en_GB |
dc.subject | time-domain analysis | |
dc.subject | patient monitoring | |
dc.subject | biomedical equipment | |
dc.subject | feature extraction | |
dc.subject | body sensor networks | |
dc.subject | photoplethysmography | |
dc.subject | oximetry | |
dc.subject | accelerometers | |
dc.subject | acceleration measurement | |
dc.subject | signal denoising | |
dc.subject | medical signal processing | |
dc.title | Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-01-25T14:45:06Z | |
dc.description | This is the final version. Available on open access from IET via the DOI in this record | en_GB |
dc.identifier.journal | Healthcare Technology Letters | en_GB |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/ | en_GB |
dcterms.dateAccepted | 2017-07-19 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2017-07-19 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-01-25T14:43:13Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2019-01-25T14:45:09Z | |
refterms.panel | C | en_GB |
refterms.depositException | publishedGoldOA |
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Except where otherwise noted, this item's licence is described as © The Author(s) 2018. This is an open access article published by the IET under the
Creative Commons Attribution -NonCommercial License (http://
creativecommons.org/licenses/by-nc/3.0/)