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。

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.

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