Koski Antti, Tossavainen Timo, Juhola Martti
Department of Computer Science, Lemminkäisenkatu, University of Turku, Turku, Finland.
J Med Eng Technol. 2004 Mar-Apr;28(2):61-6. doi: 10.1080/0309190031000139056.
Electrocardiogram (ECG) signals are the most prominent biomedical signal type used in clinical medicine. Their compression is important and widely researched in the medical informatics community. In the previous literature compression efficacy has been investigated only in the context of how much known or developed methods reduced the storage required by compressed forms of original ECG signals. Sometimes statistical signal evaluations based on, for example, root mean square error were studied. In previous research we developed a refined method for signal compression and tested it jointly with several known techniques for other biomedical signals. Our method of so-called successive approximation quantization used with wavelets was one of the most successful in those tests. In this paper, we studied to what extent these lossy compression methods altered values of medical parameters (medical information) computed from signals. Since the methods are lossy, some information is lost due to the compression when a high enough compression ratio is reached. We found that ECG signals sampled at 400 Hz could be compressed to one fourth of their original storage space, but the values of their medical parameters changed less than 5% due to compression, which indicates reliable results.
心电图（ECG）信号是临床医学中使用的最突出的生物医学信号类型。其压缩在医学信息学界具有重要意义且受到广泛研究。在以往的文献中，压缩效果仅在已知或已开发的方法能在多大程度上减少原始心电图信号压缩形式所需存储量的背景下进行研究。有时也会研究基于例如均方根误差的统计信号评估。在之前的研究中，我们开发了一种用于信号压缩的改进方法，并将其与其他生物医学信号的几种已知技术一起进行了测试。我们使用小波的所谓逐次逼近量化方法在那些测试中是最成功的方法之一。在本文中，我们研究了这些有损压缩方法在多大程度上改变了从信号计算出的医学参数（医学信息）的值。由于这些方法是有损的，当达到足够高的压缩比时，一些信息会因压缩而丢失。我们发现，以400Hz采样的心电图信号可以被压缩到其原始存储空间的四分之一，但其医学参数的值因压缩而变化不到5%，这表明结果是可靠的。
