顳葉癲癇為最常見的神經疾病之一，常見的病徵為動作停止且病患發作完恢復意識的時間較久，而強直性痙攣發作時易誘發癲癇猝死症。因此有必要發展技術以及時給予病患警告及介入。現有神經群模型是基於神經細胞群電生理的非線性模型能描述癲癇間期及發作期的腦波，先前研究將其配合拘束方根容積卡爾曼濾波器(以下簡稱CSC卡爾曼濾波器)發展具有生理意義的辨識指標，但因軟硬體計算速度不足無法即時辨識癲癇且無足夠的實驗數據驗證。本研究目的為改善樣本熵光刺激控制法則及CSC卡爾曼濾波器因個體差異而造成的估測偏差，並發展新的指標以預測及偵測癲癇發作期。應用海藻酸誘導小鼠實驗結果顯示，本研究提出基於機率分佈的閾值能提升辨識成功率及特異性，但不能改善樣本熵指標少數辨識靈敏度低的問題。另外，以CSC卡爾曼濾波器的參數B_k辨識癲癇發作的成功率不高，而本研究提出的抑制性狀態指標平均辨識成功率可達98%，可改善此缺點，且能辨識出指標S_e無法辨識出的發作期波形並提早發出警告。總之，基於神經群模型及CSC卡爾曼濾波器發展的指標能更準確的辨識癲癇發作，若能克服硬體計算速度限制，有可能發展出較樣本熵好的即時辨識癲癇系統，提供辨識及治療顳葉癲癇的新方法。

Temporal lobe epilepsy is a common neurological disease by which the common symptoms are stopping of movement and amnesia of a single memory or set of memories, and tonic seizure is the leading cause of sudden unexpected death of these patients. It is necessary to develop a warning system for the patient in time, and the model describing average dynamic of neuron papulation called the neural mass model may assist to develop the warning system. The neural mass model, a non-linear model based on the electrophysiology of the neuron populations, can describe the EEG of interictal and ictal. In previous studies a constrained square root Cubature Kalman filter (CSC Kalman filter) was developed based on the neural mass model for predicting the occurrence of epilepsy. However, due to the large amount of calculations only off-line detection of seizure was achieved on a single subject. The goals of this study are real-time suppress seizure by using photic stimulation and to develop new indicators based on the state of the nervous system for detection of seizure. The kainic acid-treated seizure mouse was used to verify the accuracy of the new indicators. Four indicators, namely parameter B of the neural mass model, the sample entropy, the state-based inhibitory index and the excitorary index were computed for their capability to detect seizure. The results show that thresholds determined based on probability distribution can improve the accuracy of seizure detection but the sensitivity. In addition, the average accuracy of identifying seizure using parameter B is lowest among all indicators. The inhibitory state indicator with accuracy up to 98% can detect the depth EEG waveform of ictal that can not be detected by sample entropy and warn patients earlier. The inhibitory index outperforms the other three indicators and it might be applied for real-time detection of seizure if the algorithm could be implemented on hardware with higher computing speed and larger memory. The integration of the CSC Kalman filter and photic stimulation may have contribution to development of new method for treating temporal lobe epilepsy.

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