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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.

Original publication

DOI

10.1049/htl.2017.0039

Type

Journal article

Journal

Healthc Technol Lett

Publication Date

04/2018

Volume

5

Pages

59 - 64

Keywords

HR estimation, HR variability features, PPG-HRV features, acceleration measurement, accelerometer sensor, accelerometers, acquired signals, ambulatory patients, biomedical equipment, body sensor networks, clinical practice, consumer domain, error propagation profiling, exemplar wearable wrist-worn monitoring system, feature extraction, heart-rate sensor, high-frequency band, long-duration windows, medical signal processing, noise, oximetry, patient monitoring, photoplethysmogram, photoplethysmography, pulse oximetry, signal denoising, substantial energy, time-domain HRV features, time-domain analysis, wearable physiological-monitoring device