- 學位論文
適用於生醫應用之可調式小波轉換處理器
A Configurable Wavelet Processor for Biomedical Applications
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摘要
隨者高齡社會的來臨，遠距照護系統因其便利性而變得相當重要。藉此病人得以攜帶式裝置作為看護取代住院。然而需要監測的訊號相當多元，功率消耗變成在系統設計上的一個重要議題。 為了降低整體功率消耗，我們以生醫訊號的特徵萃取來達成目標。在各種現有的方法中，離散小波轉換 (Discrete Wavelet Transform) 因為擁有較低的複雜度與較佳的時域-頻域分析，而被選擇作為分析生醫訊號的方法。心電圖 (Electrocardiography) 與腦波 (Electroencephalography) 是兩項常見的生醫應用。在心電圖方面，為了診斷心臟相關疾病，我們以離散小波轉換去除原始訊號中冗餘的特徵，並由重建的訊號中偵測R-R區間；在腦波圖方面，我們藉由離散小波轉換分解帶有帕金森氏症 (Parkinson's Disease) 之老鼠腦波訊號，再觀察其頻域特徵。根據上述演算法分析，我們提出可調式小波轉換處理器與特徵萃取之電路架構。此架構在測量端 (Sensor) 可被整合與做訊號特徵萃取，故其能被應用於提高生醫應用之效率。在離散小波轉換計算器中，我們提出一套低複雜度的架構使其濾波器係數能由外部的微處理器做調整，同時此架構約省下50% 的硬體複雜度。 經由場式可程式閘陣列 (FPGA) 驗證與使用TSMC 0.18微米製程，我們提出的電路設計之大小為1.15平方公厘，功率消耗為0.52微瓦 (操作電壓為1.8伏特及工作頻率為360赫茲) 。相對於傳送原始心電圖訊號，我們所提出的萃取並傳送R-R區間之方法可以節省99.5% 功率消耗。
並列摘要
With the aging population, telecare systems are more and more popular since patients can be cared by wearing a sensor rather than being restricted in hospitals. However, with various signals to be monitored continuously, power consumption has become an important issue in this field. Feature extraction is the main concern in our design. Due to the lower complexity and the better time-frequency analysis, discrete wavelet transform (DWT) algorithm has been applied to analyze biomedical signals such as Electrocardiography (ECG) and Electroencephalography (EEG) here. In ECG signal processing, we can use DWT algorithm to remove unusable features from original signals, and extract R-R interval sequences from the reconstructed waveform for diagnosing heart-related diseases. In EEG signal processing, we also can use the algorithm based on DWT to observe frequency domain features in Parkinson's disease (PD). Moreover, we have proposed a configurable wavelet processor with feature extraction circuit according to the proposed algorithm. It can be integrated in the sensor to extract signal features for more efficient biomedical applications. At the DWT computer of the processor, we have developed a structure which can not only let us configure filter coefficients by external microcontroller, but also save about 50% of the hardware complexity. We have tested our design on FPGA emulation and have implemented it with TSMC 0.18 µm technology. The total core area is 1.15 mm2 , the operating voltage is 1.8 V, the operating clock frequency is 360 Hz, and the power consumption is 0.52 µW. Compared with sending all raw ECG data, our design saves as much as 99.5% power while only detecting and sending R-R interval sequences in ECG application.
參考文獻
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