Profiling the propagation of error from PPG to HRV features in a wearable physiological‐monitoring device

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.

Are ultra‐short heart rate variability features good surrogates of short‐term ones? State‐of‐the‐art review and recommendations

“…According to [3], HRV refers to the time series of the interval variation between consecutive heart beats and it can be analysed in time, frequency and non-linear domains [3,4]. Common HRV features extracted from HRV excerpts are reported in Table 1. HRV analysis can be performed on 24 h nominal recordings (referred as long-term HRV analysis), 5 min recordings (referred as short-term HRV analysis) or shorter recordings [3], which in this review is referred as ultra-short term HRV analysis.…”

Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach

“…Third, there are many methods for improving smartphone PPG accuracy [27,[29][30][31][32][33]. For example, adding a suitable bandpass filter for signal processing [28] or excluding data with RR intervals that differ more than a certain threshold [70] are simple and effective approaches to reduce noise.…”

“…Given that smartphones with in-built cameras have become a part of modern life, using them to access health information is an ideal alternative when ECGs or similar medical devices are not available [26]. In addition, there have been several reported techniques for increasing the accuracy of smartphone PPG, such as point-of-interest selection [27], bandpass filtering [28], adaptive signal thresholding [29], motion detection techniques [30][31][32], interpolation techniques [33], and signal decomposition methods [34][35][36]. Bioengineering studies indicate that the average HR [37] and HRV measured using smartphone PPG are comparable with those measured using gold standard ECGs [21,28,[38][39][40].Although it is a promising solution for practical data collection and has an accuracy that has been well proved in several experiments, using smartphone PPG to measure HRV has received limited research attention in applied disciplines such as medicine or psychology [41]; a possible explanation is the lack of robustness in practical scenarios.…”

Study on Environmental Parameters and Wearing Electrocardiogram

“…Thus, the method proposed here can be applied to various environments at home or in the workplace in the future for research and discussion. (9)(10)(11)(12)(13)(14) 2. Research Methods…”