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系統識別號 U0026-2910201919305300
論文名稱(中文) 基於神經群模型之容積卡爾曼濾波器於慢性癲癇發作之應用
論文名稱(英文) Applications of Neural Mass Model-Based Cubature Kalman Filter to Chronic Seizure
校院名稱 成功大學
系所名稱(中) 機械工程學系
系所名稱(英) Department of Mechanical Engineering
學年度 108
學期 1
出版年 108
研究生(中文) 湯登棋
研究生(英文) Teng-Chi Tang
學號 N16060125
學位類別 碩士
語文別 中文
論文頁數 119頁
口試委員 指導教授-朱銘祥
口試委員-林宙晴
口試委員-張仁宗
中文關鍵字 癲癇  容積卡爾曼濾波器  毛果芸香鹼  神經群模型 
英文關鍵字 epilepsy  cubature Kalman filter  pilocarpine  neural mass model  C57BL/6 
學科別分類
中文摘要 顳葉癲癇是常見的神經疾病,約三成的病患無法藉由藥物得到良好的控制,然而強直陣攣發作時易誘發癲癇猝死症,故偵測或預測癲癇發作並給予病患治療是重要的研究課題。神經群模型是基於神經電生理之非線性模型,不僅能描述神經細胞接受與感知訊號之動態過程,還能模擬癲癇腦波並解釋癲癇發作或停止機制。此模型具有作為癲癇偵測、預測與控制之潛力。故本研究之目的為以神經群模型作為參考模型,利用非線性拘束方根容積卡爾曼濾波器估測小鼠腦電波型,推估其波型對應之模型參數與神經系統狀態,並以模型參數偵測與預測癲癇發作。本研究以毛果芸香鹼誘發之慢性癲癇小鼠進行實驗,結果顯示,在19次的慢性癲癇發作之中,本研究發展之癲癇辨識器能在強直陣攣動作開始前13.3±13.4 秒或腦電波型開始變化前3.9±11.5 秒辨別癲癇發作,其辨識準確度平均為94.2%,靈敏度為84.0%,偽陽率為4.5%。結論:本研究發展之估測器能預測及偵測癲癇發作,所使用的模型未來有可能用以發展閉迴路癲癇控制系統並提供顳葉癲癇治療的新方法。
英文摘要 Temporal lobe epilepsy is a common neurological disease to which about 30% of patients cannot control the seizures by medications. Moreover, tonic-clonic seizure is the leading cause of the sudden unexpected death in epilepsy, a fatal complication of epilepsy. Thus, there is an urgent need to develop a system that can predict or detect the onset of seizures. The neuron mass model is a nonlinear model capable of interpreting the dynamics of neuron reception and sensation. Not only the initiation and termination of epileptic activities but also the unforeseeable onset could be simulated by the model. The model may be utilized for seizure prediction, detection, or even exploring the mechanisms of seizure suppression. The purpose of this study is to develop a seizure detection algorithm based on estimation of the parameter of the neural mass model by a nonlinear constrained square root cubature Kalman filter. The pilocarpine-treated chronic seizure mice were used to verified the proposed filter. The results from 19 spontaneous recurrent seizures show that the proposed filter can indicate seizures 13.3±13.4 seconds before the beginning of tonic-clonic seizures or 3.9±11.5 seconds before the onset of seizures with a mean accuracy of 94.2 %, sensitivity of 84.0 %, and false alarm rate of 4.5%. In conclusion, the estimation of parameter and the state variables has been achieved in silico and both prediction and detection could be accomplished by the model-based cubature Kalman filter.
論文目次 摘 要 i
誌 謝 xiii
目錄 xiv
圖目錄 xvi
表目錄 xviii
符號表 xix
第一章 緒 論 1
1.1 癲癇簡介 1
1.2 癲癇治療 2
1.3 動物癲癇模型 4
1.4 神經群模型(neural mass model) 7
1.5 動態系統狀態與參數估測 8
1.6 研究動機與目的 10
第二章 方法與實驗 11
2.1 實驗動物 11
2.2 毛果芸香鹼動物癲癇模型與腦波測量系統 11
2.2.1 立體定位手術 12
2.2.2 癲癇誘發 13
2.2.3 腦電訊號擷取系統 16
2.3 神經群體模型 17
2.3.1 海馬迴癲癇計算模型 17
2.3.2 實驗測量條件限制與修正海馬迴癲癇模型 24
2.3.3 神經群模型癲癇狀態與參數之映射分析 28
2.4 狀態與參數估測 31
2.4.1 非線性連續-離散狀態方程式 31
2.4.2 擴增矩陣與參數估測 32
2.4.3 方根容積卡爾曼濾波器 34
2.4.4 拘束方根容積卡爾曼濾波器 40
2.4.5 參數濾波設計 41
2.5 拘束方根容積卡爾曼濾波癲癇辨識器 42
2.6 性能指標 44
第三章 結果 47
3.1 毛果芸香鹼動物癲癇模型結果 47
3.2 神經群模型模擬之慢性癲癇訊號結果 51
3.3 神經群模型模擬之慢性癲癇訊號估測結果 52
3.4 小鼠腦波參數估測結果 59
3.5 慢性癲癇發作辨識統計 65
第四章 討論 68
4.1 模型參數B(t)與神經生理之關聯性 68
4.2 估測器偏差於癲癇發作判別之影響 69
4.3 閾值對癲癇發作判定之影響 70
4.4 癲癇辨識器誤報片段討論 73
4.5 癲癇辨識演算法比較 75
第五章 結論與建議 78
5.1 結論 78
5.2 建議 79
參考文獻 82
附錄A 慢性癲癇發作相關紀錄 90
A.1 毛果芸香鹼與小鼠次品系之關係 90
A.2 慢性癲癇發作的模式 94
A.3 小鼠JW-02詳細資料 96
A.4 檔案與行為紀錄 96
A.5 慢性癲癇訊號發作時間 100
附錄B 小鼠慢性癲癇估測紀錄 102
B.1 其他慢性癲癇估測結果 102
B.2 慢性癲癇發作統計表 118
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