Intracranial electroencephalography (iEEG) provides superior diagnostic and research benefits over non-invasive EEG in terms of spatial resolution and the level of electrophysiological detail. Post-operative Computed Tomography (CT) scans provide the precision in electrode localization required for clinical purposes; however, to use this data for basic sleep research the challenge lies in identifying the precise locations of the implanted electrodes’ recording sites in terms of neuroanatomical regions as well as reliable scoring of their sleep data without the aid of facial electrodes. While existing methods can be combined to determine their exact locations in three-dimensional space, they fail to identify the functionally relevant gray matter areas that lie closest to them, especially if the points lie in the white matter. We introduce an iterative sphere inflation algorithm in conjunction with a unified pipeline to detect the exact as well as nearest regions of interest for these recording sites. Next, for sleep scoring purposes, we establish differences observed in alpha band activity between wakefulness and rapid eye movement (REM) sleep in frontal and temporal regions of iEEG patients. Lastly, we implement an automated sleep scoring method relying on the variations in alpha and delta bands power during sleep which can be applied to large sets of iEEG data recorded without accompanying electrooculogram (EOG) and electromyogram (EMG) electrodes available across labs for use in studies pertaining to neural dynamics during sleep. / M.S. / Patients with epilepsy (a neurological disorder characterized by seizures) who do not respond to medication often undergo invasive monitoring of their brains’ electrical activity using intracranial electroencephalography (iEEG). iEEG requires a surgery in which electrodes are inserted directly into the patient’s brain for better measurements. While they are monitored, these patients offer a unique opportunity for research studies that investigate the role of sleep in various learning, memory mechanisms and other health-related areas. This is because the direct contact of the electrodes with the brain tissue provides far superior quality and resolution of brain activity data in comparison to non-invasive cap-based EEG that healthy subjects wear over their scalp. However, in order to derive meaningful conclusions from these invasive recordings, we must first know the exact areas of the brain from which each site records the electrical data. We must then be able to identify which stage of sleep the patient is in at any given point in time, to be able to successfully correlate specific sleep stage-related activity with our research objectives; these patients often lack the facial electrodes used for standard sleep scoring procedures. To solve the first problem, we present an electrode localization method along with an algorithm to determine which neighboring regions contribute most to a given site’s recorded data. For the second problem, we first establish a difference in the behavior of alpha waves in the brain between wakefulness and rapid eye movement (REM) sleep. Lastly, we present an automated method to classify sleep data into different stages based on the variation in alpha waves and delta waves found during sleep.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/103752 |
| Date | 01 June 2021 |
| Creators | Jain, Sparsh |
| Contributors | Biomedical Engineering and Mechanics, Vijayan, Sujith, Buczynski, Matthew, Gregus, Ann |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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