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Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.
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Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays
Abstract. The objective of this note is to provide the backgroundand basic tools to estimate the statistical error of deformation parametersthat are calculated from displacement fields retrieved from syntheticaperture radar (SAR) imagery or from location changes of position sensors inan array. We focus here specifically on sea ice drift and deformation. Inthe most general case, the uncertainties of divergence/convergence, shear,vorticity, and total deformation are dependent on errors in coordinatemeasurements, the size of the area and the time interval over which theseparameters are determined, as well as the velocity gradients within the boundary ofthe area. If displacements are calculated from sequences of SAR images, atracking error also has to be considered. Timing errors in position readingsare usually very small and can be neglected. We give examples for magnitudesof position and timing errors typical for buoys and SAR sensors, in thelatter case supplemented by magnitudes of the tracking error, and apply thederived equations on geometric shapes frequently used for derivingdeformation from SAR images and buoy arrays. Our case studies show that thesize of the area and the time interval for calculating deformationparameters have to be chosen within certain limits to make sure that theuncertainties are smaller than the magnitude of deformation parameters.
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- Award ID(s):
- 1722729
- PAR ID:
- 10391047
- Date Published:
- Journal Name:
- The Cryosphere
- Volume:
- 14
- Issue:
- 9
- ISSN:
- 1994-0424
- Page Range / eLocation ID:
- 2999 to 3016
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/tc-14-2999-2020
