癲癇是一種最常見的神經系統失調疾病之一，全球約有1%的人患有癲癇，其中25%的癲癇患者不能經由任何方式成功治療。癲癇是由大腦的不正常放電所引起，因此在臨床評估、偵測、及治療癲癇上，腦電圖已是極為重要的工具。近年來已開發出許多藉由腦電圖來控制釋放藥物或給予電刺激來抑制癲癇發作的裝置且在臨床下運作。然而，運算快速並能立即根據各種癲癇種類在人類病理上的變化而作用的裝置目前則尚待開發。
在這篇論文中，我們經由頻譜和複雜度的分析，提出了一個運算快速且可靠的癲癇偵測方法。我們提出一個複雜度的測度ApC，並將之與所挑出來的頻帶的能量結合以用來偵測癲癇。另外也將使用一能提早偵測時間的方法使之能在癲癇發作後極短時間內偵測到以便給予抑制。此方法已應用在三種不同種類的癲癇上，由實驗結果可得知，在癲癇發作後0.36秒到0.69秒內能偵測到，並可達到95%以上的準確率。

Epilepsy is one of the most common neurological disorders, approximately 1% of people in the world have epilepsy, 25% of epilepsy patients cannot be treated sufficiently by any available therapy. Epilepsy is caused by abnormal discharges in the brain, thus EEG has been an especially valuable clinical tool for the evaluation, detection, and treatment of epilepsy. Through EEG recordings, a number of systems which can release drug or give an electrical stimulation to suppress the seizures have been developed and under clinical operation for years. However, a robust device has not yet been developed which compute quickly and fast enough to action to meet immediately pathological changes of different types of seizures in human.
In this paper, we propose a fast and reliable epilepsy detection method based on the complexity analysis and spectrum analysis. We propose complexity measure ApC and combine it with selected frequency bands power as the features for detecting seizures. An early seizure detection method is also presented which can detect seizures in a short time while seizures onset. Three different types of seizures are used for testing the detection performance. By the experiment result, the proposed epilepsy detection method can detect seizures in accuracy above 95% with a short detection delay 0.36-0.69 sec.

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