心電圖(electrocardiogram)在心臟疾病的診斷上是一種很重要的生理訊號，心電圖的判讀往往需要由受過訓練的心電圖學家耗費心力與時間來完成。為了使心電圖的處理更為快速方便，自動化與電腦化的心電圖處理因此成為近年來的一個研究主題；另一方面，電腦化的處理也使得心電圖的儲存更為有效。然而，儲存與傳輸的限制使得心電圖資料壓縮成為大多數電腦化心電圖系統的一個重要特徵。例如，Holter系統需要在體積小與耗能低的條件之下長時間儲存多導程心電圖資料，有效傳輸所儲存資料的能力也漸漸成為標準的系統要求，這類的系統需要一個能同時滿足高壓縮率與良好重建訊號品質兩個互相衝突特性之心電圖資料壓縮方法；此外可用來做為評估新開發之自動化心電圖處理系統有效工具的心電圖資料庫也需要一個有效的心電圖壓縮技術。

　　雖然大量的心電圖壓縮技術已發表在許多文獻中，以提高壓縮率同時保留重建訊號中重要臨床資訊為目標之新方法的研究仍然持續著。藉由觀察心電圖波形，我們可以歸納出一個事實：心電圖訊號通常在相鄰的心跳間顯現出相當程度的關聯性，並伴隨著相鄰取樣點間之短期關聯性。因此一個利用相鄰心跳間之關聯性的壓縮方式可以進一步提高壓縮效率；然而，多數現存的心電圖壓縮技術並未利用此種相鄰心跳間之相關性。本論文主要針對利用相鄰心跳間相關性之心電圖壓縮做研究，並提出能在達成高壓縮率的同時也保留良好重建訊號品質的演算法。

　　首先，本論文提出一個二維的壓縮法。此壓縮法將一維的心電圖轉換成二維資料陣列，再利用小波轉換加以分解，最後利用改良的SPIHT演算法將轉換後的小波係數編碼。由於心跳間之相關性極高，分解後的小波係數會更為集中，因此可以達到較高的壓縮率。此方法同時具有高壓縮率以及良好重建訊號品質的特性。

　　論文中的第二主題探討不利用各種正交轉換如離散餘弦轉換、小波轉換等的直接心電圖壓縮法。我們透過向量量化編碼來利用心跳周期間之相關性並使用一個有良好效率的次取樣演算法進一步將量化誤差編碼。此種方法可以在高壓縮率之外達成控制重建品質的目標。

　An electrocardiogram (ECG) is an important physiological signal for heart disease diagnosis. Well trained cardiologists are essential for the analysis of ECG data which is a time consuming task and needs great efforts. For the goal of efficient and convenient processing of ECG, automated and computerized ECG processing has become a major topic of research in the area of biomedical engineering. On the other hand, the storage of ECG data has become more effective because of the computerized processing of ECG. However, storage and transmission limitations have made ECG data compression an important feature for most computerized ECG systems. For example, the Holter systems call for long term storage of multichannel ECG data, with the restriction of small physical size and low power consumption. In addition, the capability of efficient transmission of the stored data is becoming a standard requirement. Such systems require a means of ECG data compression which leads to the conflicting requirements of a high compression ratio (CR) versus good signal fidelity. Besides, an efficient ECG compression technique is also needed for large ECG database, which is a very helpful tool in the evaluation of new automatic ECG processing systems.

　Although a great number of ECG compression techniques have appeared in the literature, the search for new methods continues, with the aim of achieving greater compression ratio while preserving the clinical information in the reconstructed signal. By observing the ECG waveforms, a fact can be concluded that the heartbeat signals generally show considerable similarity among adjacent heartbeats, along with short-term correlation among adjacent samples. A compression scheme employing the correlation among adjacent heartbeats can thus further improve the compression efficiency. However, most existing ECG compression techniques did not employ inter-beat correlation. In this dissertation, algorithms utilizing the correlation among adjacent heartbeats are developed to achieve high compression ratio while preserving good reconstructed signal quality.

　The first part of this dissertation describes a two-dimensional ECG compression scheme. 1-D ECG signal is segmented and aligned to form a 2-D data array. The 2-D ECG array is then wavelet transformed and its wavelet coefficients are encoded using the modified SPIHT. The highly correlated heartbeat waveforms will result in more centralized wavelet coefficients thus higher compression ratio can be achieved. This scheme can achieve high compression ratio while preserving the fidelity of the reconstructed signal.

　The second part of this dissertation is to explore direct ECG compression method without using orthogonal transform such as DCT, DWT, etc.. In the proposed method, vector quantization is used to utilize the inter-beat correlation of ECG signal and an efficient sub-sampling algorithm is adopted to encode the quantization errors. This scheme can achieve the goal of controlling the quality of the reconstructed signal along with high compression ratio.

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