基於共空間模式與通道相關同步分析之前期癲癇預測

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姓名

王裕森(Yu-Sen Wang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於共空間模式與通道相關同步分析之前期癲癇預測
(Pre-seizure Prediction Based on Common Spatial Pattern and Channel-Dependent Synchronization Analysis)

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至系統瀏覽論文 (2027-7-19以後開放)

摘要(中)

腦部為人類重要的器官之一，當其中一部份發生問題將造成患者極大的威脅，例如大腦受損的話將導致智力退化、記憶力喪失；小腦受損則是有小腦萎縮症Spinocerebellar Ataxia(SCA)，將造成運動失調等；而癲癇發作(Epilepsy)為上述疾病中的共同現象，會造成反覆抽蓄、失神等症狀，導致患者在生活中遇到危險。

本論文藉由擷取少量電極之腦電圖(Electroencephalography, EEG)訊號，基於共空間形樣法(Common Spatial Patterns, CSP)、功率譜密度(Power Spectral Density, PSD)以及大腦電極通道間的同步相關性做為特徵，之後使用支持向量機(Support Vector Machine, SVM)進行分類。最後再藉由投票法進行最終的分類，以降低癲癇誤報率，提前預測癲癇發生。論文中以公開資料集CHB-MIT腦電圖數據庫進行演算法測試驗證，實驗分別在前期長度10分鐘以及30分鐘的情況下得到預測率91.3%、誤報率0.097以及預測率92.9%、誤報率0.108，可有效預測癲癇的發生。

摘要(英)

Brain is one of the most important organs of human beings. When a problem happens in one part of it, it will pose a great threat to the patient. For example, cerebrum injury will cause intelligence degeneration, memory loss; cerebellum injury will cause movement disorders, called Spinocerebellar Ataxia(SCA). While epilepsy is the common phenomenon of above disorders, lead to repeated withdrawal, absence and other symptoms cause patients to encounter danger in life.

In this paper, by extracting EEG signals with few channels, based on Common Spatial Patterns(CSP), Power Spectral Density(PSD) and the synchronous correlation between brain electrode channels as features, then using Support Vector Machine(SVM) for classification. Finally using the voting method for the final classification, reducing the false alarm rate of epilepsy and predict the occurance of epilepsy in advance. Public dataset, CHB-MIT EEG database is used in this paper for algorithm verification. The result shows that the sensitivity reach 91.3%, 92.9% and average false prediction rate 0.097, 0.108 when preictal length is set to 10mins and 30mins respectively, which can predict seizure from happen efficiently.

關鍵字(中)

★ 癲癇
★ 腦電圖
★ 共同空間形樣法
★ 支持向量機

關鍵字(英)

★ Epilepsy
★ Electroencephalography
★ common spatial patterns
★ support vector machine

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章緒論 1
1-1 前言 1
1-2 研究動機與目的 2
1-3 文獻回顧 2
深度學習： 3
共空間模式與頻譜功率 3
電極通道間相關性、同步性 4
集成學習 4
1-4 內容大綱 5
第二章研究背景 6
2-1 大腦活動 6
2-2 癲癇種類及診斷 8
2-3 癲癇EEG資料庫 9
2-3-1 波恩大學EEG癲癇數據庫(Bonn EEG-dataset)[19] 9
2-3-2 CHB-MIT癲癇數據庫[23] 10
2-4 集成學習 11
2-4-1 簡介 11
2-4-2 裝袋算法(Bagging) 13
2-4-3 提升方法(Boosting) 14
2-4-4 堆疊泛化(Stacking) 15
第三章演算法原理與系統架構 18
3-1 癲癇資料分類及預測判定概述 18
3-2 共空間模式(Common Spatial Pattern, CSP) 19
3-3 功率譜密度(Power Spectral Density, PSD) 23
3-4 相位鎖定值(Phase Locking Value, PLV) 25
3-5 帶通濾波器組(Filter Bank) 26
3-6 支持向量機(Support Vector Machine, SVM) 27
3-7 投票集成 28
3-8 移動平均濾波(Moving Average Filter) 31
第四章實驗結果與討論 32
4-1 腦電訊號資料與實驗設計 32
4-2 集成學習與原始演算法之性能比較 33
4-3 比較方法與結果 48
第五章結論與未來展望 51
參考文獻 52

參考文獻

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指導教授

徐國鎧(Kuo-Kai Shyu)

審核日期

2022-7-28

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