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題 名 | 以小波轉換與自適性向量量化作近乎無失真心電圖資料壓縮=Nearly Lossless ECG Data Compression Using Wavelet Transform and Adaptive Vector Quantization |
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作 者 | 繆紹綱; 簡明成; | 書刊名 | 中華醫學工程學刊 |
卷 期 | 18:1 1998.03[民87.03] |
頁 次 | 頁39-51 |
分類號 | 410.1644 |
關鍵詞 | 心電圖壓縮; 小波轉換; 自適性向量量化; ECG data compression; Wavelet transform; Adaptive vector quantization; |
語 文 | 中文(Chinese) |
中文摘要 | 本文提出將小波轉換與自適性向量化應用在特性有明顯差異之各種病變心電圖的 資料壓縮法。我們採用一次五層的正交小波轉換後產生六個子頻帶,去掉最高頻帶的係數後 ,對其他頻帶的小波係數分別做以 Gold Washing 機制為基礎的自適性向量量化。本實驗採 用 MIT/BIH 資料庫中的心電圖信號做訓練與測試, 其中向量的初始碼簿係以正常心電圖為 訓練集所得, 再以特性與正常心電圖有明顯差異之病變心電圖做測試。 結果在位元率 1.39bpp 以下,重建信號的均方根誤差百分比均在 0.80% 以下。 此結果遠較直接作基本向 量量化得到的 6.99% ( 0.909bpp )、 直接作自適性向量量化所得的 1.58% ( 2.08bpp )以及用小波轉換加基本向量量化所得到的 5.83% ( 1.12bpp )為優。在計算所花費的時 間上與小波轉換加基本向量量化相當,但比直接做自適性向量量化快約 2.5倍,比基本向量量 化快約 4.5 倍。 另外,從信號重建的品質可看出,在相當低的位元率下,本文所提方法為 一個近乎無失真且適用於不同病症之心電圖信號的壓縮法。 |
英文摘要 | A data compression scheme based on a wavelet transform and adaptive vector quantization is proposed in this paper for ECG signals with obviously different characteristics. An orthogonal wavelet transform with six-band decomposition is used and the resulting wavelet coefficients in the highest band are dropped. Then the coefficients in each remaining band are vector quantized adaptively based on the Gold Wasging mechanism. The MIT/BIH arrhythmia database is used for training and testing in our experiment, where a normal ECG signal is used for training initial vector codebooks and the three abnormal ECG signals whose characteristics are quite different from the normal one are used for testing. For the bit rate below 1.39bpp, the percent root-mean-square difference of the reconstructed signal is less than 0.8%. This result is much better than 6.99%(0.909bpp), 1.58%(2.08bpp) and 5.83%(1.12bpp), which are obtained by using basic vector quantization alone, adaptive vector quantization alone, and basic vector quantization in associated with the wavelet transform, respectively. The required computation time is equivalent to that of wavelet transform in associated with basic vector quantization. It can run approximately 2.5 times faster than adaptive vector quantization and 4.5 times faster than basic vector quantization. In addition, by judging the quality of the reconstructed signals, it is found that the proposed compression scheme is nearly lossless and is applicable to variety of ECG signals at a relatively low bit rate. |
本系統中英文摘要資訊取自各篇刊載內容。