本論文提出一個基於腦波量測的癲癇偵測與抑制之光電刺激系統及癲癇辨識演算法的硬體設計。腦電圖(Electroencephalography, EEG)是於臨床中時常被用來檢測腦部疾病，例如癲癇。透過腦電圖的量測，可以得知腦部的狀態，並配合即時辨識演算法，辨識出癲癇是否發作，若偵測到癲癇發作，隨即施以深腦部刺激(deep brain stimulation)與光基因刺激(optogenetic stimulation)進行抑制，達到緩解癲癇疾病的效果。
本論文提出的腦波量測與光電刺激系統包含了四個部分：前端類比腦波量測電路、電刺激電路、光刺激電路以及藍芽無線傳輸韌體。可將接收到的腦波即時傳送至電腦端，配合自行開發的圖形化使用者介面(Graphical User Interface, GUI)進行辨識，且透過無線控制刺激模組，適時給予光電刺激。並將此系統透過國立成功大學醫學院進行動物實驗，已證明可達到癲癇抑制的效果。
本論文的辨識演算法流程為腦波訊號採集、腦波訊號預處理、特徵萃取(Feature extraction)、類神經網路(Neural network)分類器，先將收集到的腦波進行濾波後，經由特徵萃取給予分類器特徵值進行分類，最終輸出辨識結果。訊號取樣頻率為400Hz，演算法採用每64點(約0.16秒)進行辨識。特徵萃取採用頻域、振幅、變異數以及近似熵(Approximate Entropy, ApEn)作為特徵。由於每個個體間的腦波略有差異，因此採用具學習能力的類神經網路架構進行分類，可使演算法更適用於不同個體的腦波上，以提升精準度。
本論文將演算法於硬體與軟體上分別實現，在軟體的準確率可達98.76%，硬體準確率為89.88%，因此證實提出的架構確實能透過腦波量測進行癲癇的即時辨識。
結合腦波量測、無線傳輸、癲癇辨識演算法以及刺激功能，相較於以往癲癇病患須靠長期服藥或是手術進行治療，此系統能使癲癇患者於癲癇發作的第一時間進行抑制，可避免許多意外的傷害。

This paper proposes a system design based on brain waveform measurement for the detection and suppression of epilepsy and the hardware design of the epileptic recognition algorithm. Electroencephalography (EEG) is often used in the clinic to detect brain diseases such as epilepsy. According to the real-time EEG measurement, the identification algorithm can be used to identify whether seizures attack or not. If seizures are detected, the deep brain stimulation or optical stimulation is used to inhibit the epilepsy.
The EEG measurement and stimulation system proposed in this paper contains four parts: an analog front end, an electrical stimulation circuit, an optical stimulation circuit and a Bluetooth wireless transmission firmware. The system can transmit the EEG signal in real time to the computer and identify it with an epilepsy identification algorithm in graphical user interface and control the stimulation module wirelessly to give stimulation immediately. This system has successfully inhibited epilepsy in animal experiments with mice.
The identification algorithm flow of this thesis includes the signal acquisition, the signal pre-processing, the feature extraction and the neural network classifier. The signal with sample rate of 400Hz is filtered by pre-processing processor. The algorithm will output the identification result every 64 points. Then, feature extraction will output 6 features including the information of frequency domain, amplitude of time domain, variance and approximate entropy. Because there are slight differences of EEG between individuals, the classification using a neural network architecture with learning ability can make the algorithm more suitable for biology to improve accuracy.
In this paper, the algorithm is implemented on hardware and software respectively. The accuracy of the proposed system are about 98.76% and 89.88% by the verification on the software and hardware implementation, respectively. It reveals the proposed architecture is effective for epilepsy recognition. Combined with EEG measurement, wireless transmission, epilepsy recognition algorithm and stimulation system, this system can prevent epilepsy patients from suppressing seizures at the early time.

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