Abstract
Wearable physiological monitors have become increasingly popular, often worn during people’s daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.
Highlights
In the last two decades, the interest in the variation of the timing between beats (RR-intervals) of the cardiac cycle, called heart rate variability (HRV), has widely increased in the psycho-physiological research field
The main findings of this study is that the interpolation of missing values in RR-intervals timeseries on time produces more reliable HRV features estimations compared to interpolation on duration
By using this preprocessing approach, the quantification of the expected error on HRV features caused by a huge amount of missing values can support better estimations of users’ well-being, by assessing their HRV features
Summary
In the last two decades, the interest in the variation of the timing between beats (RR-intervals) of the cardiac cycle, called heart rate variability (HRV), has widely increased in the psycho-physiological research field. Thanks to the technological advancements of recent decades, it is possible to continuously record heart activity during peoples’ life via wrist-worn wearable devices equipped with heart rate sensors This innovation might have a great impact on the medical field because of the low cost of the devices and the possibility to obtain continuous passive measurements performed in an ecological setting, gaining an overview of the users’ health status by assessing HRV features during their daily life [2]. The inconsistent RR-interval data were handled by reconstructing the missing values using nearest-neighbour, linear, cubic spline and piecewise cubic Hermite interpolation methods [4,5] These methods can introduce changes in the reconstructed timeseries that could corrupt the signal spectrum [6], reducing the ability to estimate both time or frequency domains HRV features. HRV feature estimation that the interpolation on Duration suggested by all the previous studies
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