Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations

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.

Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

“…First of all, by using the Python hrv-analysis library ( ), 2.18 ± 1.29% of RR-intervals in the dataset were detected as ectopic beats (i.e., disturbance of the cardiac rhythm frequently related to the electrical conduction system of the heart) or missing values induced by motion artifacts. These missing values were reconstructed via quadratic interpolation applied on the time domain, i.e., the heartbeats timestamp, instead of the duration domain, i.e., the duration of the heartbeats, as suggested by Morelli et al [ 23 ].…”

“…The main problem of wrist-worn wearable devices is the artifacts induced by external stimuli that produce inconsistent IBI values. Usually, when IBI are recorded with gold standard technology (i.e., electrocardiography), the number of abnormal values is close to 1% [ 22 ], while, when it is recorded by these low cost instruments, is more than 10% [ 23 ]. Hence, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable values from wrist-worn wearable devices equipped with heart rate sensors avoiding misleading results [ 24 ].…”

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG

“…However, this lack of awareness can affect the signal quality, which thus must be expected to be lower in longer-term real-life recordings than under laboratory conditions. For this reason, the artifact detection and correction are crucial steps in data preprocessing, especially when it comes to ECG recordings over several hours [ 40 , 41 ].…”

“…However, according to more recent advances, piecewise cubic spline interpolation is the standard method. This also holds when it comes to the replacement of artifacts in the data series [ 41 , 46 ]. In addition, power spectral density estimates using the parametric autoregressive method depend on the model order.…”

Data Analytics and Applications of the Wearable Sensors in Healthcare: An Overview

“…The results showed that both set of features can reach high accuracy with support vector machine (SVM) classifier, but the new proposed variables can be easily interpreted and employed for better understanding of the alterations of biomechanical behavior in complex situations. In a study focusing on healthy subjects having normal heart activity, Morelli et al [32] investigated the effects of interpolation on time and duration with increasing missing values to assess the interpolation strategy for better results during the estimation of heart rate variability (HRV) features. The results concluded that interpolation in time is the most favorable method for producing better HRV features estimation as compared to interpolation on duration.…”