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Signal analysis of sleep electrooculogram

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posted on 2021-06-08, 12:21 authored by Peyman Shokrollahi
Measures of sleep physiology, not obvious to the human eye, may provide important clues to disease states, and responses to therapy. A significant amount of eye movement data is not attended to clinically in routine sleep studies because these data are too long, about six to eight hours in duration, and they are also mixed with many unknown artifacts usually produced from EEG signals or other activities. This research describes how eye movements were different in depressed patients who used antidepressant medications, compared to those who did not. The goal is to track antidepressant medications effects on sleep eye movements. Clinically used SSRIs such as Prozac (Fluoxetine), Celexa (Citalopram), Zoloft (Sertraline), the SNRI Effexor (Venlafaxine) have been considered in this study to assess the possible connections between eye movements recorded during sleep and serotonin activities. The novelty of this research is in the assessment of sleep eye movement, in order to track the antidepressant medications' effect on the brain through EOG channels. EOG analysis is valuable because it is a noninvasive method, and the following research is looking for findings that are invisible to the eyes of professional clinicians. This thesis focuses on quantifying sleep eye movements, with two techniques: autoregressive modeling and wavelet analysis. The eye movement detection software (EMDS) with more than 1500 lines was developed for detecting sleep eye movements. AR coefficients were derived from the sleep eye movements of the patients who were exposed to antidepressant medications, and those who were not, and then they are classified by means of linear discriminant analysis. also for wavelet analysis, discrete wavelet coefficients have been used for classifying sleep eye movements of the patients who were exposed to medication and those who were not.





Master of Applied Science


Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type


Thesis Advisor

Sridhar Krishnan Kristiina McConville