Zou F, Gallagher R R
Department of Electrical and Computer Engineering, Kansas State University, Manhattan 66506.
Biomed Sci Instrum. 1994;30:57-62.
When applying transform techniques in data compression, an efficient approximation of the original signal using fewer transform coefficients is desired. The Discrete Cosine Transform (DCT) has been an effective tool in such applications. It decomposes a signal into a set of sinusoidal waveforms that are global in time. The DCT is not as efficient for signals with only local variations. The Wavelet Transform (WT) is a new technique that can decompose a signal into a set of small waveforms called wavelets. These wavelets possess local supports in the time domain, which makes the WT suitable for representing signals with local variations. Even though the ECG is dominated by low frequencies, its QRS complex exhibits strong localized characteristics. In this study, the effectiveness of the two transforms in compressing ECG data is investigated. Having noted the weakness of each transform, the two techniques are combined in two ways to compress the ECG data. It is observed that with the new techniques, better visualization quality can be achieved with the same total number of transform coefficients.
在数据压缩中应用变换技术时，人们希望使用更少的变换系数对原始信号进行高效逼近。离散余弦变换（DCT）在这类应用中一直是一种有效的工具。它将信号分解为一组在时间上全局的正弦波形。对于仅具有局部变化的信号，DCT的效率不高。小波变换（WT）是一种新技术，它可以将信号分解为一组称为小波的小波形。这些小波在时域中具有局部支撑，这使得WT适用于表示具有局部变化的信号。尽管心电图主要由低频成分主导，但其QRS复合波表现出很强的局部特征。在本研究中，研究了这两种变换在压缩心电图数据方面的有效性。鉴于每种变换的弱点，将这两种技术以两种方式组合起来压缩心电图数据。结果发现，使用这些新技术，在变换系数总数相同的情况下，可以获得更好的可视化质量。
