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Chia-Hung Kuo_PhD Thesis.pdf (2.92 MB)

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THE ANALYSIS OF HIGH FREQUENCY OSCILLATIONS AND SUPPRESSION IN EPILEPTIC SEIZURE DATA

Kuo, Chia-Hung
http://rave.ohiolink.edu/etdc/view?acc_num=case1396411237

Abstract Details

2014, Doctor of Philosophy, Case Western Reserve University, EECS - System and Control Engineering.
In this study, several techniques for the variability analysis of electroencephalography (EEG) have been developed with application to the detection of high-frequency oscillations (HFOs) and suppressed EEG classification. EEG refers to the electrical activities in the subdural or cerebral cortex of the brain and can be recorded by several neurophysiological methods, including noninvasive and invasive recordings. EEG in the range of 40-500 Hz, referred to as HFOs, can be recorded in human brains using depth microelectrodes. Various studies in human or animal models supported that HFOs were related to epileptogensis, and evidence was also provided to suggest that neocortical seizures might begin with low-amplitude HFOs. We apply several HFO detection and FFT-based algorithms to 19 IEEG recordings from the Epilepsy Monitoring Unit at University Hospitals Case Medical Center to further verify the connection between HFO activities and clinical seizure onset. Based on the confirmed onset channel from the recordings, the performance of these HFOs detection algorithms has been evaluated in both the time domain and the frequency domain including full band analysis. The frequency band from 35 to 80 Hz with no overlapping of analysis epochs has been selected for further investigations on the full 100/102 channel recordings. Results from various algorithms including Line-Length, Curve-Length, Benchmark and the algorithm proposed in this work indicated quantifiable changes in HFO energy before and after seizure onset. This suggests that HFOs may play an important role in the development of methods for the detection and prediction of epileptic seizures. Post-ictal generalized EEG suppression (PGES) is an Electroencephalographic (EEG) biomarker for risk of Sudden Unexpected Death in Epilepsy (SUDEP). Breathing, movement and muscle artifact can render quantification of PGES difficult. An automated EEG suppression detection algorithm using a genetic algorithm (GA) and adaptive neuro–fuzzy inference system (ANFIS) is proposed for detecting EEG suppression after epileptic seizures. The EEG features for separating the normal and suppressed EEG include short-time energy measurement methods and approximate entropy (ApEn). The ANFIS classifier used the GA to select an optimal feature set as the input for classification. Two types of classifiers, patient specific and patient independent, are developed and tested and the results support that the proposed scheme is a robust approach for the detection of EEG suppression. Two-channel and six-channel classifiers using the short-time energy measurement methods obtained from subdividing the 1-9 Hz EEG frequency band have also been developed and tested.
Kenneth Loparo (Committee Chair)
Marc Buchner (Committee Member)
Vira Chankong (Committee Member)
Samden Lhatoo (Committee Member)
Farhad Kaffashi (Committee Member)
115 p.
Biomedical Engineering; Biomedical Research; Electrical Engineering
EEG; HFOs; High Frequency Oscillations; Line-Length; Adaptive Neuro-Fuzzy Inference System; ANFIS; Epilepsy; Genetic Algorithm;

Recommended Citations

Citations

  • Kuo, C.-H. (2014). THE ANALYSIS OF HIGH FREQUENCY OSCILLATIONS AND SUPPRESSION IN EPILEPTIC SEIZURE DATA [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1396411237

    APA Style (7th edition)

  • Kuo, Chia-Hung. THE ANALYSIS OF HIGH FREQUENCY OSCILLATIONS AND SUPPRESSION IN EPILEPTIC SEIZURE DATA. 2014. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1396411237.

    MLA Style (8th edition)

  • Kuo, Chia-Hung. "THE ANALYSIS OF HIGH FREQUENCY OSCILLATIONS AND SUPPRESSION IN EPILEPTIC SEIZURE DATA." Doctoral dissertation, Case Western Reserve University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1396411237

    Chicago Manual of Style (17th edition)

case1396411237
170
© 2014, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.