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State Estimation of the Autonomic Nervous System from Heart Rate Variability Measurements

Marra, Lindsey (2021) State Estimation of the Autonomic Nervous System from Heart Rate Variability Measurements. Master's Thesis, University of Pittsburgh. (Unpublished)

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Heart rate variability (HRV) is an indicator of autonomic nervous system (ANS) activity. HRV is the time series of fluctuations in the intervals between subsequent heartbeats. An opportunity exists to establish real-time tracking of the ANS state based on real time measurements of HRV and predictions from a model of the ANS control of heart rate.
We propose an ANS state estimator. A computational model of the ANS implements a state estimator that is robust to the nonlinear and nonstationary nature of cardiac control. We use a three-state model of the ANS validated in humans, canines, and non-human primates by Champeroux et al. (2018). The state variables are the set of the probabilities that the ANS is in a particular state: [P(S1), P(S2), P(S3)]’. S1 is the state of parasympathetic predominance. S2 is the state of parasympathetic and sympathetic coactivation, and S3 is the state of parasympathetic withdrawal and sympathetic activation.
Other state estimation methods were rejected in favor of particle filtering, commonly used in robotics for navigational position estimation. The approach enables visualization of the ANS state based on HRV measurements that may be useful for diagnostic purposes.
Given that the model of ANS regulation of HRV has been validated in pharmacological studies, there is strong qualitative confidence in estimating the ANS state based on HRV
measurements. Using this model for long-term state estimation does not directly incorporate the time constants of the PNS and the SNS. Thus, the changes in states may be inaccurate by up to 5 seconds, which is the longest observable delay in ANS control of HRV. Future work to incorporate the dynamics of changes in the ANS state would improve the precision of estimation.
Establishing a state tracking system for the ANS that captures the nature of such a complex physiological system will enable investigation into how the ANS becomes dysregulated in anxiety, depression and PTSD, as well as chronic diseases that, while not mental illnesses, may exacerbate or be exacerbated by ANS dysregulation and mental illness.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Marra, Lindseymarralindseyj@gmail.comljm70
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairDallal, Ahmedahd12@pitt.eduahd12
Committee MemberMao, Zhi-Hongzhm4@pitt.eduzhm4
Committee MemberDickerson, Samueldickerson@pitt.edudickerson
Date: 13 June 2021
Date Type: Publication
Defense Date: 19 November 2020
Approval Date: 13 June 2021
Submission Date: 13 November 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 93
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: heart rate variability, autonomic nervous system, ptsd, depression, anxiety, diagnostics, allostasis, particle filter, state estimation, physiology, robotics
Date Deposited: 13 Jun 2021 18:37
Last Modified: 13 Jun 2021 18:37


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