Multimodal vs Unimodal Estimation of Sympathetic-Driven Arousal States
Name
Article_6_Sandya_Sub_Emery_Brown_Multi_Unimodal_paper.pdf
Description
Accepted version
Size
709.77 KB
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Unknown
Checksum (MD5)
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Author(s)
Subramanian, Sandya
Brown, Emery Neal
Barbieri, Riccardo
Date Issued
2020
Journal
Computing in Cardiology
Publisher
Computing in Cardiology
Citation
Subramanian, Sandya, Brown, Emery and Barbieri, Riccardo. 2020. "Multimodal vs Unimodal Estimation of Sympathetic-Driven Arousal States." Computing in Cardiology, 47.
Version
Author's final manuscript
Abstract
Estimation of sympathetic-driven arousal state (SDAS) traditionally consists of computing frequency-based measures of heart rate variability. However, in the presence of confounds such as breathing frequency, these measures can incorrectly estimate the underlying SDAS. In this work, we present an example of such a case during a three-stage paced breathing task. Using a state space framework, we demonstrate that a unimodal model that relies solely on these frequency-based heart rate variability measures overestimates SDAS during the slowest breathing stage and underestimates it in subsequent stages. On the other hand, a multimodal model with both time and frequency domain heart rate variability observations as well as electrodermal activity information provides a more realistic estimate of SDAS throughout the task. This suggests that multimodal estimation of SDAS is more accurate and robust than unimodal estimation.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.22489/CINC.2020.290
