Nave G, Cohen A
Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
IEEE Trans Biomed Eng. 1993 Sep;40(9):877-85. doi: 10.1109/10.245608.
A new algorithm for ECG signal compression is introduced. The compression system is based on the subautoregression (SAR) model, known also as the long-term prediction (LTP) model. The "periodicity" of the ECG signal is employed in order to further reduce redundancy, thus yielding high compression ratios. The suggested algorithm was evaluated using an in-house database. Very low bit rates on the order of 70 b/s are achieved with a relatively low reconstruction error (percent rms difference-PRD) of less than 10%. The algorithm was compared, using the same database, with the conventional linear prediction (short-term prediction--STP) method, and was found superior at any bit rate. The suggested algorithm can be considered a generalization of the recently published average beat subtraction method.
介绍了一种用于心电图（ECG）信号压缩的新算法。该压缩系统基于子自回归（SAR）模型，也称为长期预测（LTP）模型。利用ECG信号的“周期性”来进一步减少冗余，从而实现高压缩率。使用内部数据库对所提出的算法进行了评估。在相对较低的重建误差（均方根差百分比 - PRD）小于10%的情况下，实现了约70 b/s的极低比特率。使用相同的数据库，将该算法与传统的线性预测（短期预测 - STP）方法进行了比较，发现在任何比特率下该算法都更优越。所提出的算法可被视为最近发表的平均搏动减法方法的推广。
