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研究生:江濬安
研究生(外文):Jun-An Jiang
論文名稱:失神性癲癇幼兒病患之腦電波訊號應用多尺度樣本熵法分析
論文名稱(外文):Multiscale Entropy Applied to Multi-channel Electroencephalogram Signals Analysis in Childhood Absence Epilepsy
指導教授:謝建興
指導教授(外文):Jiann-Shing Shieh
口試委員:徐業良范守仁
口試委員(外文):Yeh-Liang HsuShou-Zen Fan
口試日期:2014-07-14
學位類別:碩士
校院名稱:元智大學
系所名稱:機械工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:29
中文關鍵詞:失神性癲癇腦電波信號樣本熵多尺度樣本熵複雜度
外文關鍵詞:absence seizureelectroencephalographysample entropymultiscale entropycomplexity index
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失神性癲癇在綜合幼兒癲癇疾病裡是其中極其重要的一種。本研究中以19個獨立採樣頻道,記錄下24個5到12歲幼童病患完整頭部的腦電波訊號,並將失神性癲癇前與失神性癲癇發生階段之腦電波訊號,利用樣本熵法與多尺度樣本熵法進行分析運算,最後加以分析在癲癇發生過程中腦電波訊號複雜度隨著時間位移下之變化。在此研究中發現,腦電波訊號經分析後癲癇發生前之熵值相較於癲癇發生中之熵值高出許多存在顯著的差異性,失神性癲癇發生時的複雜度亦隨之在個頻道中降低許多。利用多尺度熵法在全腦的複雜度變化分析中,分析了每位病人失神性癲癇發生時各頻道之複雜度變化,最後以相關係數分析,發現在相同病人條件下各頻道之複雜度指數在其數次癲癇發作時,存在相當高的一致性,說明了透過多尺度熵法所運算出之複雜度,可判別每位病人失神性癲癇發作時的差異,並加以了解每位病人因癲癇發病時大腦各部位功能的複雜度變化情形。此外,分析後的腦電波訊號,由於採樣頻率較高之訊號相較採樣頻率較低之訊號來得更佳靈敏,在利用多尺度熵法分析後能較樣本熵法分析更佳的能揭示大腦的生物現象,並可利用複雜度指數來判別失神性癲癇時的腦部功能。
Absence epilepsy is an important epileptic syndrome in children. In this research, electroencephalogram (EEG) signals from 19 electrode channels of the entire brain of 24 children between 5-12 years with absence epilepsy were analyzed. EEG signals of preictal (before seizure happening) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and multiscale entropy (MSE) methods. The variation of complexity index (CI) that followed the timeline from preictal state to ictal state was also analyzed. We found that the entropy values at preictal state had a significantly higher value than those at ictal state. The occurrence of absence attacks decreased the CI in all channels. The analysis of correlation coefficients in different time periods of preictal and ictal states in the same patient also showed significant high correlation in ictal state in different attacks while there were great differences in CI in different patients. It is indicated that CI changes were consistent in different absence seizures of the same patient but were quite different from patient to patient, and suggests that the brain stayed at a homogeneous activation state during absence attack. In addition, higher sampling frequency was also found to be more sensitive to detect the functional change in disease state in the present study. We concluded that MSE analysis is more likely to reveal the brain biological phenomenon than SamEn analysis, and can be used to investigate the brain function during seizure attacks.
A Thesis i
ABSTRACT iii
Contents iv
List of Tables v
Abbreviation vii
Chapter 1: Introduction 1
1.1 Background 1
1.2 Review of Studies 2
1.3 Purpose 3
1.4 Brief Summary of Chapters 4
Chapter 2: What is Absence Seizure 5
Chapter 3: Analysis Algorithm 6
3.1 Sample Entropy 6
3.2 Multiscale Entropy 7
3.3 Complexity index 7
Chapter 4: Experiment Condition and Method 9
4.1 Data Source Ethics Statement 9
4.2 EEG Recording 9
4.3 Subjects 11
4.4 Statistical analysis 12
Chapter 5: Results 13
5.1 Entropy of preictal state vs. ictal state 13
5.2 Variation of complexity index 17
Chapter 6: Conclusion and Future Work 23
References 27
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