隨著社會高年齡層的老化，針對慢性疾病患者之遠距醫療照護將成為未來發展趨勢，心電圖為診斷是否有心血管疾病的重要工具，在有著24小時的心電訊號監護儀器或多導程的心電訊號擷取儀情況下，一個能達到高壓縮率與低失真性之訊號壓縮處理可以降低因長期監測而產生的龐大資料量造成網路頻寬與儲存空間的負擔。本論文提出一個在考慮重建誤差前提下之心電訊號壓縮演算法，因心電訊號為用於醫療診斷用之重要訊號，使用者可以根據需求調整重建品質，以免造成誤判。
本論文提出一個有效的心電訊號壓縮方法，可以根據使用需求設定誤差上限，本演算法以稀疏分解(Sparse decomposition)為核心基礎，搭配小波轉換(Discrete Wavelet Transform, DWT)作為輔助使用，在一般的情況下可以根據所設定的PRD上限求得最佳K稀疏度之稀疏解，在不規則波段的情況下則採用小波轉換取代稀疏分解來做壓縮，最終再搭配算術編碼進一步獲得壓縮倍率。
壓縮流程分兩階段，前處理階段會將心電信號根據R波所在位置進行切割後做零平均值(Zero-mean)將訊號移至水平軸，再將各個R-R區段從中間做零值內插(Zero-padding)，接著使用前720組內插後的R-R區段建立心電字典。壓縮階段利用OMP(Orthogonal Matching Pursuit)貪婪演算法對信號進行稀疏分解，接著根據設定之百分比均方根誤差(Percent Root-Mean-Square Difference, PRD)進行稀疏度K的反復迭代，求得最佳稀疏度K，在效果不佳的波段則使用小波轉換並使用能量包裝效率(Energy Packing Efficiency, EPE)做係數的丟棄。最終將稀疏係數與小波係數量化後與其餘差分編碼後的必要資訊進行算術編碼(Arithmetic coding)之熵編碼技術，來產生良好與壓縮品質。所提出的壓縮演算法使用MIT-BIH 心率不整資料庫的全部48組心率不整資料作為測試訊號，平均壓縮比(CR)為44.07、誤差(PRD)為0.411。

With the aging of population and the ascent of health awareness, application of remote long-term health care system gradually be taken. The electrocardiogram(ECG) is a significant signal for the diagnosis of heart disease. But the amount of ECG data generated by monitoring systems could be very large over a long period of time, and data compression is essential for efficient storage of such information. However, it is necessary to transmit ECG data for telemedicine applications, so data compression is required for valid transmission.
This paper proposed an effective ECG data compression algorithm based on sparse decomposition and discrete wavelet transform. In the preprocessing process, the signal has to be segmented accoding to the position of R-wave and removes the average value of each interval, and then elongated by zero-padding. In the compression process, it will compressed signal according to percent root-mean-square difference(PRD) that we setting by the sparse decomposition in regular segments and discrete wavelet transform thresholding in irregular segments based on Energy Packing Efficiency(EPE). After compression process performed the final step of entropy compression. Before the entropy encoding performed backward difference and quantization. Finally, increases the compression effect by arithmetic coding. Selected all 48 patterns of MIT-BIH arrhythmia database and the experimental results were simulated by Matlab, the average compression ratio (CR) is 44.07, distortion (PRD) is 0.411.

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