State-space Decoders for Wearable Healthcare Applications

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dc.contributor.advisorFaghih, Rose T.
dc.contributor.committeeMemberContreras-Vidal, Jose L.
dc.contributor.committeeMemberHan, Zhu
dc.contributor.committeeMemberFrancis, Joseph T.
dc.contributor.committeeMemberNguyen, Hien Van
dc.creatorWickramasuriya, Dilranjan Sunimal
dc.creator.orcid0000-0002-2998-095X
dc.date.accessioned2021-08-12T19:38:29Z
dc.date.createdDecember 2020
dc.date.issued2020-12
dc.date.submittedDecember 2020
dc.date.updated2021-08-12T19:38:30Z
dc.description.abstractHomeostatic processes govern multiple latent state variables within the human body. While many of these states remain largely unobserved, they frequently give rise to bioelectric and biochemical phenomena that can be measured. The measured signals provide a window into estimating the unobserved states. In a number of instances, the observed electrical and chemical phenomena are pulsatile or impulse-like in nature. This dissertation describes state-space methods for estimating latent variables tied to changes in skin conductance, heart rate and cortisol secretion – all of which have a characteristic point process nature. In the first two sections, state-space methods are developed for estimating sympathetic arousal from skin conductance and heart rate features. Estimation involves Bayesian filtering applied within an expectation-maximization framework. Results are provided on experiments involving different types of mental stressors and Pavlovian fear conditioning. The results agree with general expectations with high arousal levels typically occurring during stressors and lower values occurring during relaxation. General agreement with expectations is also found with different trial averages in fear conditioning. Skin conductance-based estimates are also validated with blood flow signals in the brain in a separate experiment. In the third section, state-space methods are developed to estimate energy production from blood cortisol measurements. The methods are applied to simulated and experimental data from patients suffering from Cushing's disease, chronic fatigue syndrome and fibromyalgia syndrome. The results help shed light on why patients with hypercortisolism may experience daytime fatigue and nighttime sleeping difficulties. Circadian-like behavior is also seen with higher energy estimates occurring towards morning awakening and lower values at bedtime. In the final section, machine learning methods are used for state-space estimation. Traditional Bayesian filtering methods do not have the ability to permit external influences such as domain knowledge or labels to affect the state estimates. We develop a hybrid estimator that enables this possibility and apply it to both skin conductance-based arousal estimation and cortisol-related energy estimation. The hybrid estimator permits the enforcement of circadian rhythms on to the state estimates and the customization of the level to which the external influence is permitted to affect them.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Wickramasuriya, Dilranjan S., and Rose T. Faghih. "A marked point process filtering approach for tracking sympathetic arousal from skin conductance." IEEE Access 8 (2020): 68499-68513.; Wickramasuriya, Dilranjan S., and Rose T. Faghih. "A Bayesian filtering approach for tracking arousal from binary and continuous skin conductance features." IEEE Transactions on Biomedical Engineering 67, no. 6 (2019): 1749-1760.; Wickramasuriya, Dilranjan S., Md Amin, and Rose T. Faghih. "Skin conductance as a viable alternative for closing the deep brain stimulation loop in neuropsychiatric disorders." Frontiers in neuroscience 13 (2019): 780.; Wickramasuriya, Dilranjan S., Chaoxian Qi, and Rose T. Faghih. "A state-space approach for detecting stress from electrodermal activity." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3562-3567. IEEE, 2018.; Wickramasuriya, Dilranjan S., and Rose T. Faghih. "A mixed filter algorithm for sympathetic arousal tracking from skin conductance and heart rate measurements in Pavlovian fear conditioning." PloS one 15, no. 4 (2020): e0231659.; Wickramasuriya, Dilranjan S., and Rose T. Faghih. "A novel filter for tracking real-world cognitive stress using multi-time-scale point process observations." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 599-602. IEEE, 2019.; Wickramasuriya, Dilranjan S., and Rose T. Faghih. "A cortisol-based energy decoder for investigation of fatigue in hypercortisolism." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 11-14. IEEE, 2019.
dc.identifier.urihttps://hdl.handle.net/10657/8083
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectState-space estimation, point processes, skin conductance, cortisol, machine learning
dc.titleState-space Decoders for Wearable Healthcare Applications
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2022-12-01
local.embargo.terms2022-12-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical and Computer Engineering, Department of
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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