Profiling the propagation of error from PPG to HRV features in a wearable physiological‐monitoring device. Issue 2 (12th February 2018)

• Record Type: Journal Article
• Authors: Morelli, Davide; Bartoloni, Leonardo; Colombo, Michele; Plans, David; Clifton, David A.
• Is Part Of: Healthcare technology letters. Volume 5:Issue 2(2018)
• Page Start: 59
• Page End: 64
• Publication Date: 2018-02-12
• Subjects: time‐domain analysis -- patient monitoring -- biomedical equipment -- feature extraction -- body sensor networks -- photoplethysmography -- oximetry -- accelerometers -- acceleration measurement -- signal denoising -- medical signal processing; error propagation profiling -- PPG‐HRV features -- wearable physiological‐monitoring device -- consumer domain -- ambulatory patients -- heart‐rate sensor -- exemplar wearable wrist‐worn monitoring system -- photoplethysmogram -- pulse oximetry -- HR estimation -- HR variability features -- accelerometer sensor -- acquired signals -- noise -- substantial energy -- high‐frequency band -- clinical practice -- long‐duration windows -- time‐domain HRV features; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2017.0039
• Languages: English
• ISSNs: 2053-3713

Abstract

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.