隨著通訊技術和醫療器材電子化的應用，遠端的醫療監控逐漸成為醫療的趨勢，生理訊號對慢性病的監控尤其重要，長時間的遠端醫療監控的過程中會產生龐大的資料量，造成資料傳輸和儲存上龐大的壓力，因此生理訊號的壓縮便成為重要的議題，本文針對腦波訊號提出一種使用嵌入式零樹小波和適應性差異訊號編碼之壓縮方法，將腦波訊號先透過離散小波轉換成為頻域上的小波係數，再將係數的高頻係數和低頻係數做兩種不同的量化編碼，由於腦波訊號本身為高度隨機的訊號，其不具有任何的相似性，因此量化編碼的部分的目標即是將訊號量化成為相似性較高的訊號，對於較多高頻係數，我們使用嵌入式零樹編碼，可以大大提高訊號的相似性，為整段訊號的主要壓縮效果來源，也因此在係數還原上的誤差會較大，但由於高頻係數在訊號上為對波型的細微描述，在整體訊號還原上的影響較小，對於低頻係數，由於低頻係數在重建時較為重要，若有太大的誤差會造成整段訊號在還原時有很大的影響，因此這邊希望將低頻係數能過做近無損的編碼方式，透過適應性差異訊號編碼雖然無法其在相似的提升上很有限，但在還原時可以達到近無損的目的，透過兩部分的量化編碼提高相似性之後，再做無損的霍夫曼編碼，可以得到很好的效果達到壓縮目的。

Remote medical monitor has become a tendency as the improvement of the communication technology and electrical medical equipment. Monitoring Biological signals takes an important role in chronic illness diagnosis. It produces a large amount of data to transmit or store when it comes to long-term medical monitoring. Therefore, data compression of biological signals becomes an important problem. This paper proposed an Electroencephalography(EEG) algorithm using Embedded zero-tree wavelet(EZW) encoding and adaptive differential pulse code modulation (DPCM). Because EEG signal is a highly random and non-stationary which make it hard to compress. Therefore, the proposed algorithm is aimed to promote its repeatability. First, we do the discrete wavelet transform analysis on EEG data turning it from time-scale value to frequency-scale coefficients. Second, we have different quantization process on high frequency coefficients and low frequency coefficients separately. For high frequency coefficients, we use EZW which can highly promote the repeatability of the signal. This part is the main source of the compression ratio but has larger distortion. However, it will not cause a big problem in the overall reconstruction. For low frequency coefficients, we use DPCM to quantize because we need its distortion as low as possible. Low frequency coefficients play a major part in overall reconstruction. Therefore, we expect it’s a near-lossless method. At last, we use Huffman coding on the quantized coefficients. Through EZW and DPCM, EEG data have highly promoted its repeatability which make it suitable for Huffman coding.

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