ORCID Identifier(s)

0000-0002-4844-7202

Graduation Semester and Year

2021

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Yingying Zhu

Abstract

Sleep disorders are common among adults and children; it has serious consequences on their heath, cognitive development and quality of life. However, some sleep disorders are challenging to diagnose and more challenging to treat. Practitioners often rely on AIH for OSA patients’ classification task, where considering one measurement could raise a risk of oversimplification. Studies show the correlation between sleep disorders, specifically OSA, and mental health. On the other side there are an increasing number of studies suggested evidence of a relationship between the dynamic properties of functional brain structure with the behaviors and cognition attributes. This novel work objective is to assist medical professionals in screening and treating sleep disorders and any related cognitive disorders as a consequence. In this work we consider the novel problem of detecting behavioral measurements based on the resting-state fNIRS time series data. Taking the full advantages of the fNIRS neuroimaging technique, which is described as an emerging and promising technology for wide range of applications and as a diagnosing tool. In addition, it is a useful neuroimaging technology for research on children's cognitive development without the limitation of fMRI. The foundation of the study is capturing the spatiotemporal dynamic features of the resting-state FC and fully use the captured dynamic information for the prediction problem. To do so, we applied a 4D tensor method to build the dynamic functional connectivity matrices. Then the tensor statistical model trained on dFC matrices for the classification task. The highest classification accuracy achieved was 74%.

Keywords

Resting-state functional near-infrared spectroscopy (fNIRS), Sleep apnea, Apnea-hypopnea index (AHI), Dynamic functional connectivity (dFC), Behavior rating inventory of executive function (BRIEF)

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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