Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device Davide Morelli 1,2,3 ✉ , Leonardo Bartoloni 1,2 , Michele Colombo 1,2 , David Plans 1,3 , David A. Clifton 4 1 BioBeats Group Ltd, London, UK 2 Dipartimento di Informatica, Università di Pisa, Pisa, Italy 3 Center for Digital Economy, University of Surrey, Guildford, UK 4 Department of Engineering Science, University of Oxford, Oxford, UK ✉ E-mail: davide@biobeats.com Published in Healthcare Technology Letters; Received on 24th May 2017; Revised on 18th July 2017; Accepted on 19th July 2017 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. 1. Introduction: Wearable physiological monitoring, exploiting devices with unobtrusive (often wrist-worn) packages, offers substantial promise for improving the care of patients in clinical settings, and for enabling individuals to better manage their own health. With recent advances in consumer markets, including devices such as fitness trackers and ‘smart watches’, the use of wearable monitors is becoming increasingly commonplace. However, very few of these devices penetrate into use at scale within either clinical settings or for permitting patients to track their own health outside clinical environments [1, 2], with only small numbers of studies that have been described in the literature [3–5]. A major obstacle to the use of wearable devices in such settings is the lack of characterisation of their ability to estimate clinically relevant physiological parameters, such as, in the case of cardiac applications, understanding the propagation of error from the photoplethysmogram (PPG) waveform acquired within the device, through to estimation of the heart rate (HR), and ultimately on to the heart-rate variability (HRV) features that are used to track the state of cardiac health of a patient. In this Letter, we investigate this propagation of error, with the aim of improving understanding of the accuracy of the estimated HRV features; we use as our experimental device a commonly used wearable consumer device, the Microsoft Band 2. The most commonly occurring mode of error in such wearable devices has previously been identified as being due to movement artefact [6], which is unsurprising given the sensitivity of the pulse oximetry process (which yields the PPG waveform from which HR and HRV parameters are estimated) to the small changes in light inten- sity that arise due to movement of the patient. However, while some studies have investigated how to mitigate the effects of motion arte- facts correcting the raw PPG signal [7–9], we could find a small amount of studies investigating the possibility to assess PPG reli- ability from accelerometer [10], and no study investigating the propagation of error from PPG to HRV features. This investigation provides the following contributions: (i) inves- tigation of a means of discriminating when the time series of pulsa- tile intervals (estimated from the PPG) should be discarded; (ii) estimate the expected error on subsequently derived HRV features as movement of the patient increases; and (iii) suggest a means of filtering to minimise the error of the HRV features. 1.1. Heart-rate variability: HRV is the beat-to-beat variability in R–R intervals (sometimes termed NN intervals or successive differences (SD) in the literature), where the latter refers to the time series obtained by identifying the duration between subsequent R-peaks in the electrocardiogram (ECG) waveform. An R–R interval t therefore corresponds to an estimate of instantaneous HR 60t −1 bpm (beats per minute). HRV features are descriptors that capture aspect of this variability in the time or frequency domain. Examples of the former include the following, which are defined for a window of data of duration t, where often t = 5 min: † AverageNN, defined as being the mean of the NN intervals occurring within the window [4, 11]; † SDNN, defined as being the standard deviation of the NN inter- vals occurring within the window [4, 11–13]; † RMSSD, the root-mean-square (RMS) of SD occurring within the window [4, 11, 13]; † pNN50, which is the proportion of the total number of NN inter- vals within the window that exceed 50 ms. That is, one first defines {NN50} as being that subset of {NN intervals} that exceed a dur- ation of 50 ms, and where pNN50 is |NN50|/|NNintervals|, where |·| is set cardinality [4, 11, 13]. Examples of frequency-domain HRV features include: † SVI, which is the sympathovagal index, defined as the LF:HF ratio, where LF is the total power in the low-frequency band (between 0.003 and 0.14 Hz), and where HF is the total power in the high-frequency band (between 0.15 and 0.40 Hz). The defin- ition of the boundaries of the LF and HF bands varies slightly between authors, being a heuristic definition [11, 12, 14]. For simplicity, we use the same name for HRV features cal- culated from R–R intervals (collected from the ECG sensor), and Healthcare Technology Letters, 2018, Vol. 5, Iss. 2, pp. 59–64 doi: 10.1049/htl.2017.0039 59 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/)