Dataset Information

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

ABSTRACT: Application of ultra-short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people's daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.

SUBMITTER: Rossi A

PROVIDER: S-EPMC7764161 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Rossi Alessio A Pedreschi Dino D Clifton David A DA Morelli Davide D

Sensors (Basel, Switzerland) 20201211 24

Application of ultra-short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people's daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge probl ...[more]

PMID: 33322560

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Project description: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.

Project description:BackgroundIn-vivo high-resolution peripheral quantitative computed tomography (HR-pQCT) has high potential in scaphoid bone pathologies' scientific and clinical fields. The manufacturer's visual grading scale (VGS) classifies motion artifacts and divides scans into five quality grades ranging from grade 1 (good quality) to grade 5 (poor quality). This prospective study aimed to investigate the feasibility of the VGS and the influence of image quality on bone density and microarchitecture parameters for the scaphoid bone.MethodsWithin one year, twenty-two patients with scaphoid fractures received up to six scans of their fractured and contralateral wrist (each consisting of three stacks) using second-generation HR-pQCT (total 256 scans). Three experienced observers graded each stack following the visual grading system, and inter- and intraobserver variability were assessed. The contralateral uninjured scaphoids were then compared pairwise within each patient to high-quality grade 1 scans to determine the influence of image quality on density and microarchitecture parameters.ResultsInter- and intraobserver variability among the three observers significantly revealed fair to moderate agreement, P<0.001 and P<0.05, respectively. Bone volume (BV) fraction tended to increase with poorer image quality but did not exceed four percent. Trabecular bone mineral density (Tb.BMD) decreased with poorer image quality but did not exceed five percent. Trabecular number and trabecular thickness significantly increased by 15.5% and 6.8% at grade five (P<0.001), respectively, and trabecular separation significantly decreased by 13.7% at grade five (P<0.001).ConclusionsThis study revealed a considerable influence of motion on bone morphometry parameters of the scaphoid. Therefore, high image quality must be a central point in studies focusing on the histomorphometry of small objects. The high inter- and intraobserver variability limit the VGS. Future research may focus on other grading systems or automated techniques leading to more consistent and reproducible results. Currently, the use of microarchitectural analysis should be limited to cases without motion artefacts or, at most low graded motion artefacts.

Project description:Objectives:Remote photoplethysmography (rPPG) is a promising non-contact measurement technique for assessing numerous physiological parameters: pulse rate, pulse rate variability (PRV), respiratory rate, pulse wave velocity, blood saturation, blood pressure, etc. To justify its use in ultra-short-term (UST) PRV analysis, which is of great benefit for several healthcare applications, the agreement between rPPG- and PPG-derived UST-PRV metrics was studied. Approach:Three time-domain metrics-standard deviation of normal-to-normal (NN) intervals (SDNN), root mean square of successive NN interval differences (RMSSD), and the percentage of adjacent NN intervals that differ from each other by more than 50 ms (pNN50)-were extracted from 56 video recordings in a publicly available data set. The selected metrics were calculated on the basis of three groups of 10 s recordings and their average, two groups of 30 s recordings and their average, and a group of 60 s recordings taken from the full-length recordings and then compared with metrics derived from the corresponding reference (PPG) pulse waveform signals by using correlation and effect size parameters, and Bland-Altman plots. Main results:The results show there is stronger agreement as the recording length increases for SDNN and RMSSD, yet there is no significant change for pNN50. The agreement parameters reach r = 0.841 (p < 0.001), r = 0.529 (p < 0.001), and r = 0.657 (p < 0.001), estimated median bias -1.52, -2.28 ms and -1.95% and a small effect size for SDNN, RMSSD, and pNN50 derived from the 60 s recordings, respectively. Significance:Remote photoplethysmography-derived UST-PRV metrics manage to capture UST-PRV metrics derived from reference (PPG) recordings well. This feature is highly desirable in numerous applications for the assessment of one's health and well-being. In future research, the validity of rPPG-derived UST-PRV metrics compared to the gold standard electrocardiography recordings is to be assessed.

Dataset Information

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

Publications

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

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