Compressive sampling is an emerging technique for developing data sampling in recent years. In this paper, we propose the architecture of compressive sampling for electroencephalogram (EEG) and electromyogram (EMG) signals in the telemedicine sensor network. In order to save the area of hardware in encoding-end, the sensing matrix must be simple. And the decoding algorithms require the medium computational complexity under the trade-off between the reconstructed error and the speed of convergence. Accordingly, we propose the modified compressive sampling matching pursuit (MCoSaMP) and multiple domains decoding method to enhance the performance. This proposed architecture is composed of Bernoulli matrix in encoding-end, Daubechies-4 (DB-4) for EMG signals (DCT for EEG signals), and MCoSaMP algorithm with multiple domains decoding method in decoding-end. The proposed architecture for EEG signals can reduce the percentage root mean square difference (PRD) by 17% compared to other papers. And then the compression ratio (CR) can be achieved 0.4 with PRD 9.1%. Moreover, the CR can be achieved 0.4 with PRD 21.3% for EMG signals. The MCoSaMP with multiple domains decoding method can achieve the almost same PRD with convex optimization for EMG signals. And the complexity can be reduced from O(N^3.5) to O( m^3/(log(N))^2 ), where m and N are the number of measurement and length of signal, respectively. Although the PRD of proposed architecture for EMG signals is 6% larger than traditional EMG compression method, the complexity of proposed method in encoding-end is much lower than it. That achieves the goal of low complexity in encoding-end in telemedicine sensor network.

近年來，人們對於遠程醫療的需求越來越重視，因此如何有效在資料量傳遞方面達到節省以及編碼端如何低複雜度成了重要的議題，本篇論文利用近年來新起的技術壓縮感測來達到資料壓縮的目的，但由於利用壓縮感測處理EEG訊號已有文獻探討，因此本篇論文著重於EMG訊號。

我們利用壓縮感測理論設計出適用於EMG和EEG訊號的壓縮以及解壓縮的架構，在編碼端的部分採用複雜度較低的Bernoulli matrix，在解碼端的部分尋找稀疏性較好的基底如: DCT、DFT、DHWT和DB-4，解壓縮的演算法使用複雜度較低精確度較好的CoSaMP (Compressive Sampling Matching Pursuit)，針對此演算法進行改良，並且提出multiple domains decoding method，使用兩個基底以達到誤差相抵銷的結果，使得解壓縮後的誤差比起原本單使用CoSaMP演算法更小。

利用PhysioNet以及NTHU cMEA Lab所提供的EMG和EEG訊號進行模擬，結果明確指出在EMG訊號方面我們提出的方式可以比單使用CoSaMP的PRD小9%，而在EEG方面，與其他文獻相比，我們提出的方法可以降低PRD 17%。最後與傳統 EMG壓縮方法相比，儘管解壓縮端部分複雜度大於傳統壓縮方式，但是編碼端複雜度利用壓縮感測的方式可以遠低於傳統壓縮方式，如此一來可以達到編碼端低複雜度的需求。

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