Closed-Loop Fuzzy Energy Regulation in Patients With Hypercortisolism via Inhibitory and Excitatory Intermittent Actuation.

Bayesian estimation closed-loop cortisol energy state fuzzy control hypercortisolism wearable

Journal

Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481

Informations de publication

Date de publication:
2021
Historique:
received: 22 04 2021
accepted: 28 06 2021
entrez: 26 8 2021
pubmed: 27 8 2021
medline: 27 8 2021
Statut: epublish

Résumé

Hypercortisolism or Cushing's disease, which corresponds to the excessive levels of cortisol hormone, is associated with tiredness and fatigue during the day and disturbed sleep at night. Our goal is to employ a wearable brain machine interface architecture to regulate one's energy levels in hypercortisolism. In the present simulation study, we generate multi-day cortisol profile data for ten subjects both in healthy and disease conditions. To relate an internal hidden cognitive energy state to one's cortisol secretion patterns, we employ a state-space model. Particularly, we consider circadian upper and lower bound envelopes on cortisol levels, and timings of hypothalamic pulsatile activity underlying cortisol secretions as continuous and binary observations, respectively. To estimate the hidden cognitive energy-related state, we use Bayesian filtering. In our proposed architecture, we infer one's cognitive energy-related state using wearable devices rather than monitoring the brain activity directly and close the loop utilizing fuzzy control. To model actuation in the real-time closed-loop architecture, we simulate two types of medications that result in increasing and decreasing the energy levels in the body. Finally, we close the loop using a knowledge-based control approach. The results on ten simulated profiles verify how the proposed architecture is able to track the energy state and regulate it using hypothetical medications. In a simulation study based on experimental data, we illustrate the feasibility of designing a wearable brain machine interface architecture for energy regulation in hypercortisolism. This simulation study is a first step toward the ultimate goal of managing hypercortisolism in real-world situations.

Identifiants

pubmed: 34434085
doi: 10.3389/fnins.2021.695975
pmc: PMC8381152
doi:

Types de publication

Journal Article

Langues

eng

Pagination

695975

Informations de copyright

Copyright © 2021 Fekri Azgomi, Hahn and Faghih.

Déclaration de conflit d'intérêts

RF is a co-inventor on a provisional patent that designs decoders for estimating energy based on cortisol observations. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Hamid Fekri Azgomi (H)

Computational Medicine Lab, Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.

Jin-Oh Hahn (JO)

Department of Mechanical Engineering, University of Maryland, College Park, MD, United States.

Rose T Faghih (RT)

Computational Medicine Lab, Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.

Classifications MeSH