(A) chaos wavelet analysis of the EEG in patients with panic disorder = 혼돈 이론과 웨이브렛 변환을 이용한 공황장애 환자의 EEG 분석

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Panic disorder is a debilitating disease that has been reported to have a lifetime prevalence of about 4% and has been recurrently linked with the instance of other mental disorders, such as depression with which it has reportedly significant comorbidity. However, there have been a number of reports about the current state of underdiagnoses of panic disorder, because of its largely prominent somatic symptoms, which cause afflicted individuals to seek clinical care as opposed to mental health consultation and treatments. This study aimed to explore the plausibility of creating an EEG-based machine learning classifier that could serve as a diagnostic aid for panic disorder and address the underdiagnoses. In addition to this, by applying a non-linear dynamical analysis procedure towards studying the effects of panic disorder on the brain using the method of chaos-wavelets that allows insights into the changes in each EEG sub-band, this study aims to show insights to panic disorder using techniques that have largely not been used to explore panic disorder in the brain. The results from this study showed an SVM based classifier for Panic vs. Depression with 86% classification accuracy and a KNN based classifier for panic vs Controls with 76% Classification accuracy. Differences in Approximate entropy between panic disorder and MDD patients were also observed along with Potential channel asymmetry differences in the correlation dimension after Bonferroni correction, between panic disorder patients and the control group.
Advisors
Jeong, Jaeseungresearcher정재승researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2019.2,[iv, 45 p. :]

Keywords

Panic disorder▼adepression▼amachine learning▼achaos wavelets▼aElectroencephalograph (EEG); 공황장애▼a우울증▼a기계학습▼a중요한 뉴런▼a웨이브렛 변환▼a혼돈 이론▼aEEG

URI
http://hdl.handle.net/10203/266188
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843180&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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