心電圖為醫生在心臟疾病診斷上重要的生理訊號參考。近年來，針對心血管慢性疾病患者的心電訊號監控和個人居家醫療照護需求日漸增加，以偶發性心率不整患者而言，通常會安排24至72小時的不間斷的收集方式，才可達到協助醫師診斷病人的身體狀態。為了避免心電信號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，因此提出一個在考慮重建品質的前提下對心電信號進行壓縮相對重要，以免造成醫師誤判。心電信號壓縮除資料量的減少，更重要的是要能同時保留住心電圖中重要的波形特徵，作為醫生診斷依據。
本演算法提出一個考慮誤差(PRD)下的高壓縮倍率心電信號壓縮方法，心電信號壓縮演算法採用稀疏編碼(Sparse coding)為核心基礎，結合適應性字典(Adaptive dictionary)與誤差控制(Error control)方式等，使心電信號在所規定之PRD範圍中可得最稀疏解。本論文提出的演算法採用稀疏表示結合算術編碼的方式，並藉由適應性稀疏度K取代壓縮感知中固定K值求稀疏解的方式，能在固定誤差下創造更高的稀疏度，再配合算術編碼進行壓縮。
在壓縮流程中，首先將心電信號根據R波所在位置(index)進行分段，去掉各別區段的平均值後，將各個R-R區段由中間做零值內插(Zero-padding)，使用前720組內插後的R-R區段建成初始字典。再利用OMP(Orthogonal Matching Pursuit)針對信號進行稀疏分解，接著根據預先設定之百分比均方根誤差(Percent Root-Mean-Square Difference, PRD)進行改變稀疏度的反復迭代，尋找最佳化之稀疏度(K)。並將稀疏係數值量化後與其餘必要資訊進行算術編碼法(Arithmetic coding)等壓縮編碼技術，來產生良好的壓縮比與壓縮品質。所提出的壓縮演算法使用MIT-BIH 心率不整資料庫的全部48組心率不整資料作為測試訊號，平均壓縮效果較現有文獻效果高出2-3倍。

The ECG is an electronic signal for recording the heartbeat of a patient. This signal assists physicians to diagnose the patient health from his/her cardiovascular system. Because of long hours of uninterrupted monitoring of cardiovascular activity required for medical purposes, it is essential that the effective compression of ECG waveforms requires for transmission and storage applications due to the huge amount of signal data. In this paper, a novel compression algorithm of ECG signals is proposed based on sparse decomposition and arithmetic coding theorems. The experimental results from the MIT-BIH arrhythmia database show that the proposed algorithm achieves 2-3 times higher than other existing algorithms in the compression performance.

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