Morelli Davide, Rossi Alessio, Cairo Massimo, Clifton David A
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX2 6DP, UK.
Biobeats Group LTD, 3 Fitzhardinge Street, London W1H 6EF, UK.
Sensors (Basel). 2019 Jul 18;19(14):3163. doi: 10.3390/s19143163.
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.
可穿戴式生理监测器越来越受欢迎，人们常在日常生活中佩戴，每周7天、每天24小时收集数据。在过去十年中，这些设备引起了科学界的关注，因为它们使我们能够自动提取有关用户生理状况的信息（例如心率、睡眠质量和身体活动），从而推断其健康状况。然而，可穿戴设备记录的数据最大的问题是在数据采集过程中由于外部刺激引起的运动和机械伪迹导致的缺失值。这些缺失数据可能会对心率（HR）反应的评估和心率变异性（HRV）的估计产生负面影响，进而可能提供有关个体健康状况的误导性见解。在本研究中，我们聚焦于心脏活动正常的健康受试者，通过使用吉尔伯特突发方法在从nsr2db PhysioNet数据集中获取的RR间期的五分钟时间窗口内随机引入缺失值，研究由运动伪迹引起的心跳之间时间间隔（RR间期）的缺失变化对HRV特征估计的影响。然后，我们通过对缺失值时间段进行插值来评估几种在存在缺失值情况下估计HRV的策略，涵盖文献中经常使用的一系列技术，通过线性、二次、三次和三次样条函数进行插值。我们从而将通过处理RR间期时间序列中的缺失数据获得的HRV特征与从无缺失值的相同数据中获得的HRV特征进行比较。最后，我们评估在时间（即心跳发生的时间戳）和持续时间（即心跳的持续时间）上使用插值方法的差异，以确定处理缺失RR间期的最佳方法。本研究的主要新发现是，与在持续时间上进行插值相比，在时间上对缺失数据进行插值会产生更可靠的HRV估计。因此，我们可以得出结论，随着缺失值数量的增加，在持续时间上进行插值会改变RR信号的功率谱，对HRV特征的估计产生负面影响。我们可以得出结论，在处理具有大量缺失值的数据（如可穿戴传感器的数据）时，在时间上进行插值是所考虑的方法中的最佳方法。
