隨著世界人口高齡化的現象與健康意識的抬頭，遠距居家看護系統逐漸受到重視。為了避免心電圖資訊因為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，本論文提出利用差分QRS波方式降低動態範圍的無失真與失真性心電訊號壓縮演算法，並應用於無線心電圖量測系統中，使用者可視裝置儲存空間、生理訊號觀測品質等不同環境與需求，選擇採用訊號品質高的無失真資料壓縮演算法或是高壓縮率(低儲存空間)的失真性資料壓縮演算法。
本論文所提出的無失真心電訊號壓縮演算法將心電訊號經過後向差分與差分QRS波處理後，再使用霍夫曼編碼得到壓縮資料；在失真性壓縮方面，使用快速傅立葉轉換為核心，將差分QRS波後之訊號依照R波所在位置分割區段，再將各區段內插成256點後進行頻譜轉換，最後對頻譜訊號進行後向差分、遊程長度編碼與霍夫曼編碼。
經過MIT-BIH資料庫的48組訊號與5組實測訊號測試，無失真壓縮演算法可達2.66：1與1.22：1之平均壓縮率；而失真性壓縮演算法使用MIT-BIH測試效能，其CR、PRD、PRDB、PRDN、RMS、SNR、QS為6.69、0.13、1.37、1.90、1.20、35.07、53.69，使用實測訊號則分別為5.44、0.14、0.99、3.46、37.28、30.01、40. 21。

With the aging population in the world and people’ awareness of health improved, “Remote Home Care System” is gradually taken seriously. To avoid the burden of bandwidth and memory capacity, which are sprung from the ECG holter system, we present the lossless and lossly compression algorithms with the method of QRS-wave difference and apply to the Wireless Holter System. With different application requirements, users can choose high-compression rate or perfect reconstructed algorithms to obtain higher signal quality and to reduce more storage memory size.
For the proposed lossless compression algorithm, we use backward difference and QRS-wave difference to process ECG signal, and huffman encoding to produce the compressed data. In the lossy compression algorithm respect, we take fast fourier transform (FFT) as the kernel function which belongs to the lossy compression. After the procedure of QRS-wave difference, we partition off the RR interval and interpolate to 256 points. And then, we can get the compressed data by using FFT coefficients to the procedure of backward difference, run length encoding and Huffman coding.
The proposed algorithms were evaluated by using all patterns from MIT-BIH arrhythmia database and the test pattern which we measure the ECG signal from our ECG read-out circuit. For the proposed lossless algorithm, the average CR is 2.66 and 1.22. Furthermore, for the proposed lossy algorithm, the analytic results such as CR, PRD, PRDB, PRDN, RMS, SNR, and QS’s value is 6.69, 0.13, 1.37, 1.90, 1.20, 35.07 and 53.69. By using the test pattern which we measure the ECG signal, the analytic results such as CR, PRD, PRDB, PRDN, RMS, SNR, and QS’s value is 5.44, 0.14, 0.99, 3.46, 37.28, 30.01 and 40.21.

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