應用於無線傳輸心電向量圖之分散式感測壓縮法

Abstract

本論文所探討的問題為:隨著現今社會之老年化，且隨著醫療技術的進步且電子產業的蓬勃，結合兩者以提供可攜式電子儀器提供全天候的生理監測為一許多現今研究之重點，故其內低功耗之訊號處理法則相更為重要。近年，感測壓縮提供了一個在感測端低功耗的嶄新訊號壓縮處理法。若當我們欲利用感測壓縮之技術於無線傳輸心電向量圖於無線感測網路之內，而在實際情況之內，在單一無線網路之內會有很多機會同時有許多人會希望可以傳送心電訊號至中央處理端，故傳統之感測壓縮法僅處理單一訊號已無法有效率的解決此需求，故本論文結合分散式壓縮感測應用於此向量心電圖傳輸，且在接收端要解碼傳送之量測值部分也提出一較有效率之解碼方法，以讓系統可不因為同時處理訊號數量提高而造成整體效能下降，反之可利用眾多心電訊號之間之特有相關性使得可使用更少的量測值以達到更高的解碼品質。 則由實驗結果，在使用相同的單一訊號壓縮率之下且訊號數目足夠多的情況下，使用分散式壓縮感測的之解碼效果較傳統壓縮感測優於我們心電向量圖無線傳輸之應用上。

With the population structure aging and the improving the quality of health cares, we can combine it with the electrical techniques to offer a portable electrical device so as to offer a service monitoring the status of patients’ vital signals. This issue has become a popular research topic in recent years. Therefore, a signal processing technique with low power consumption applying to the device becomes more and more important. Recently, an emerging sensing technique called compressive sensing to provide a low computational complexity compression method at the sensor node. If we try to exploit compressive sensing to vectorcardiogram telemetry applications in a wireless sensor network in practical uses, there might be more than one person requiring to transmitting VCG signals to the central processing node. Accordingly, conventional compressive sensing which just process one signal copes with this requirement inefficiently. Hence, this thesis will focus on the vectorcardiogram telemetry applications using distributed compressive sensing, and distributed compressive sensing provides an efficient decode method of measurements. Consequently, when the receiver receives many signals at the same time, it can decode utilizing the advantages of common features between signals in an ensemble to reduce the total number of measurements to perfectly recover signals with high probability. From the experiment results, under the same compression ratio of single signal with sufficient number signals, the average root mean square error performance using distributed compressive sensing is reduced than using conventional compressive sensing.