標題: 使用開放式精簡指令集處理器應用於即時偵測失神性癲癇動物模型之軟硬體共同實現
Software-hardware Co-implementation for Real-time Epileptic Seizure Detection Using OpenRISC Processor Core on Absence Animal Models
作者: 張舜婷
闕河鳴
電信工程研究所
關鍵字: 開放式精簡指令集處理器;癲癇;閉迴路;OpenRISC processor;epilepsy;closed-loop
公開日期: 2011
摘要: 癲癇是一種最常見的神經系統失調疾病之一,全球有1%的人罹患癲癇,25%的癲癇病患無法完全被治癒。在過去幾年,開迴路的癲癇控制器已經被提出,如迷走神經刺激和腦深部刺激,但連續性或間歇性的電刺激會導致高功率消耗以及神經細胞損害的可能性。相反的,近年來閉迴路的硬體和生理訊號處理器已經被提出。但這些研究中,在癲癇發作5秒後才會偵測到癲癇或者是沒有提及偵測時間。此外,大部分的研究通常是利用片段的腦電圖驗證癲癇演算法,無法全然地證實演算法的健全性。因此,在過去我們提出一套無線可攜式即時癲癇偵測與抑制系統,並使用連續性的腦電圖長時間偵測癲癇。 在本篇論文中,改進上述所提出的即時癲癇偵測閉迴路系統的決定參數方式,提出一個快速決定參數方法,使這些參數最適合每個老鼠的模型。這個快速決定參數方法比先前決定參數的方法快了416*10E6倍,同時也可達到92-99%的高偵測率以及可以在0.63-0.79秒偵測到癲癇。此外,使用精簡指令集的技術實現一個低功耗的生理訊號處理器,將偵測癲癇演算法實現在生理訊號處理器上可達到即時處理生理訊號以及只消耗6 mW。與先前提出的系統比較,可以降低93.8%的功率消耗。本篇所提出的癲癇偵測器可應用於即時偵測系統中,以及在未來可以與類比前端電路和後端刺激器整合成一個積體化的閉迴路偵測系統。
Epilepsy is one of the most common neurological disorders. Approximately 1% of people in the world suffer from epilepsy, and 25% of epilepsy patients cannot be healed by today’s available treatments. In past years, open-loop seizure controllers have been proposed, such us vagus nerve stimulation and deep brain stimulation devices; however, the device drives a stimulator continuously or intermittently that causes high power consumption and the likelihood of neuronal damage. In contrast, the closed-loop implementation of hardware prototypes or biomedical signal processors has been proposed recently. Nevertheless, the average of seizure detection delay is either longer than 5 seconds or often not mentioned in these works, and it is insufficient to validate the robustness of detection algorithm. Moreover, most of studies often use the discontinuous electroencephalogram (EEG) signal fragments to validate seizure detection algorithm. As a result, a portable wireless online closed-loop seizure controller in freely moving rats was proposed, which validated seizure detection algorithm by using continuous online EEG signals. In this thesis, the fast parameter determination method, which determines a fitting model for each rat, is proposed to improve our previous work. The proposed parameter determination method is 416*10E6 times faster than our previous work, and it can attain the same detection accuracy (92-99%) and detection delay (0.63-0.79 s). Additionally, a low-power biomedical signal processor which bases on reduced instruction set computer (RISC) technology consumes only 6 mW for real-time epileptic seizure detection algorithm. Compared with our previous prototype, the measurement results show that the implemented processor can reduce 93.8% power consumption. The developed seizure detector can be applied to monitor the online EEG signals and integrate with analog front-end circuitries and an electrical stimulator to perform a closed-loop seizure controller in the future.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079813606
http://hdl.handle.net/11536/47086
Appears in Collections:Thesis


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