在有著24小時的ECG (Electro-cardiography)心電訊號監護儀器或多導程的心電圖量測裝置如Holter system 能供醫生對患者進行數日之連續心電圖量測的情況下，一個能達到即時且高壓縮率的訊號壓縮處理方法可以有效降低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。然而訊號壓縮可分為無損(Lossless)與有(Lossy)壓縮。對於醫療人員而言，如果能提供即時(Real Time)的無損心電訊號壓縮不但能有效提升醫療人員診斷心血管疾病的準確度也能提供後續的醫療應用。為了達成此目的我們利用參考前幾筆的心電資料之可變動式的預測模組(Adaptive Linear Prediction)來降低動態預測誤差跟一個內容可變式的哥倫布編碼(Content - Adaptive Golomb rice coding)來提升整體系統壓縮率。最後我們也利用 MIT-BIH Arrhythmia 的資料庫包含48組兩導程(Lead II、Lead V1)的心電資料來做壓縮效能評估，結果顯示利用本論文所提出的有效無損心電圖壓縮演算法在經MIT-BIH Lead II資料庫內壓縮率能達到2.77x、經MIT-BIH Lead V1資料庫內壓縮率能達到2.83x。除此之外，我們也將此壓縮演算法開發在嵌入式系統當中能達到方便攜帶並用於遠距醫療之相關應用。

With a 24-hour ECG (Electro-cardiography) signal monitoring system such as Holter system, it would be produced huge amount of data. It is helpful to reduce the data of ECG signal and save storage space by presenting an idea that combine an effective electrocardiogram (ECG) compression algorithm However, signal compression can be divided into lossless and lossy compression. In this thesis, we present an idea that combine an effective ECG lossless data compression with the telemedicine in order to save storage space and reduce transmission time. Different from literatures, we use the Adaptive Linear Prediction to reduce dynamic prediction range and Content – Adaptive Golomb rice coding to raise the compression ratio. Finally, we also take MIT-BIH Arrhythmia Database as the input pattern which contains 48 two-lead recordings, the result show in MIT-BIH Lead II database that compression ratio (CR) is 2.77x and in MIT-BIH Lead V1 database the compression ratio is 2.83x. Furthermore, we also implement the proposed compression algorithm on the embedded development board which is suitable for Telemedicine application.

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