Date of Award
2018
Degree Type
Thesis
Degree Name
Master of Science in Electrical Engineering (MSEE)
Department
Electrical and Computer Engineering
First Advisor
Walter Besio
Abstract
High frequency oscillations (HFOs) have become a predominant topic in neurology research in recent years as they are considered markers of epileptic activity, potential indicators of seizure onset zone (SOZ), and possibly hint at an oncoming seizure in epileptic patients. These oscillatory signals are frequently seen in intracranial electroencephalography (iEEG) but have proven difficult to record from conventional scalp EEG electrodes. However, with the advent of tripolar concentric ring electrodes (TCREs) it is becoming easier to record HFOs without the need of a surgical procedure. While several different methods have been proposed for detection of HFOs in iEEG signals, no single method has been identified as a best detector and it is unknown how any of these methods will perform on the activity recorded from TCREs (tEEG). In this study, a novel detection design is derived and evaluated on real and simulated tEEG and compared to two of the more popular HFO detection routines designed for iEEG. Subsequently, an estimate of SOZ based strictly on HFO event occurrence is made on a few patients and compared to the markings of a clinician.
Recommended Citation
Tamayo, Michael, "Automated High Frequency Oscillation Detection and Seizure Onset Zone Estimation Using TCRES" (2018). Open Access Master's Theses. Paper 1234.
https://digitalcommons.uri.edu/theses/1234
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