癲癇偵測演算法應用於癲癇輔助診斷系統

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姓名

黃思齊(Szu-Chi Huang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

癲癇偵測演算法應用於癲癇輔助診斷系統
(Seizure Detection Algorithm for Auxiliary Epilepsy Diagnosis System)

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至系統瀏覽論文 (2028-7-23以後開放)

摘要(中)

本論文提出基於深度學習的癲癇偵測演算法應用於長時EEG分析，並基於機器學習提出穿戴式癲癇偵測演算法應用於非醫院環境之癲癇偵測。本論文使用CHB-MIT資料集與波恩資料集對演算法效能進行評估，實驗結果顯示本論文深度學習演算法具備最佳的分類效能。本論文深度學習模型在CHB-MIT資料集受試者相依測試平均f1-score為69.34%，跨受試者測試平均f1-score為37.31%。在波恩資料集本論文深度學習模型平均準確度為98.91%，穿戴式演算法平均準確度為97.13%。本論文將穿戴式演算法實現於超低功耗嵌入式系統計算演算法執行時間，實驗結果顯示本論文所提出熵之估算法能夠減少81.58%的計算時間，與近幾年演算法相比本論文穿戴式演算法提供相當的分類效能並具備最少計算時間。

摘要(英)

This thesis proposed a deep learning-based seizure detection algorithm for long-term EEG analysis and a machine learning-based wearable seizure detection algorithm for non-hospital seizure detection. We conducted an experiment on the CHB-MIT and Bonn datasets to evaluate the algorithm′s performance. The experimental results show that our deep learning algorithm has the best classification performance. For the CHB-MIT dataset, our deep learning model achieved an average f1-score of 69.34% in the subject dependence experiment and an average f1-score of 37.31% in the cross-subject experiment. In the Bonn dataset, the average accuracy of the deep learning model and the wearable algorithm is 98.91% and 97.13%, respectively. Our wearable algorithm is implemented on the ultra-low power embedded system and analysis the calculation time of the algorithm. The experimental results show that the proposed entropy estimation method can reduce the calculation time by 81.58%. Compared with the previous algorithms, our wearable algorithm provides a comparable classification performance and has the fastest inference speed.

關鍵字(中)

★ 深度學習
★ 機器學習
★ 腦電圖
★ 癲癇偵測
★ 端對端模型
★ 熵

關鍵字(英)

★ Deep learning
★ machine learning
★ EEG
★ seizure detection
★ end-to-end models
★ entropy

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
一、緒論 1
1-1 前言 1
1-2 文獻回顧 2
1-3 研究動機與目的 4
1-4 內容大綱 5
二、穿戴式癲癇偵測演算法 6
2-1 穿戴式癲癇偵測演算法架構 6
2-2 LBPMAD特徵提取法 7
2-3 熵 8
2-4 邏輯回歸 9
三、深度學習癲癇偵測演算法 11
3-1 深度學習癲癇偵測模型架構 11
3-1-1 Inception 模組 12
3-1-2 Residual模組 12
3-1-3 分類器 13
3-2 網路訓練策略 13
四、實驗設計 15
4-1 癲癇資料集 15
4-1-1 波恩資料集 15
4-1-2 CHB-MIT資料集 16
4-2 長時EEG癲癇偵測實驗設計 19
4-2-1 CHB-MIT資料集受試者相依測試 19
4-2-2 CHB-MIT資料集跨受試者測試 20
4-2-3 波恩資料集癲癇腦波分類 20
4-3 長時EEG癲癇偵測之基線方法 21
4-3-1 FBCSP+SVM： 21
4-3-2 EEGNet-8,2： 22
4-3-3 Stacked 1D-CNN： 23
4-3-4 EEGWaveNet： 24
4-4 效能評估指標 25
五、實驗結果與討論 27
5-1 長時EEG癲癇偵測結果 27
5-2 深度學習模型錯誤輸出分析 30
5-2-1 雜訊 30
5-2-2 極端地類別非平衡 31
5-2-3 異質性 31
5-3 穿戴式癲癇演算法特徵無效性分析 32
5-4 波恩資料集癲癇腦波分類結果 34
5-5 穿戴式裝置演算法計算時間分析 38
六、結論與未來研究方向 41
6-1 結論 41
6-2 未來研究方向 42
論文發表列 44
參考文獻 45

參考文獻

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指導教授

徐國鎧(Kuo-Kai Shyu)

審核日期

2023-7-18

推文