Department of Digital Content Design, Tzu Hui Institute of Technology, Pingtung 926, Taiwan.
IEEE Trans Biomed Eng. 2010 Jun;57(6):1399-409. doi: 10.1109/TBME.2009.2037605. Epub 2010 Feb 5.
Maintaining reconstructed signals at a desired level of quality is crucial for lossy ECG data compression. Wavelet-based approaches using a recursive decomposition process are unsuitable for real-time ECG signal recoding and commonly obtain a nonlinear compression performance with distortion sensitive to quantization error. The sensitive response is caused without compromising the influences of word-length-growth (WLG) effect and unfavorable for the reconstruction quality control of ECG data compression. In this paper, the 1-D reversible round-off nonrecursive discrete periodic wavelet transform is applied to overcome the WLG magnification effect in terms of the mechanisms of error propagation resistance and significant normalization of octave coefficients. The two mechanisms enable the design of a multivariable quantization scheme that can obtain a compression performance with the approximate characteristics of linear distortion. The quantization scheme can be controlled with a single control variable. Based on the linear compression performance, a linear quantization scale prediction model is presented for guaranteeing reconstruction quality. Following the use of the MIT-BIH arrhythmia database, the experimental results show that the proposed system, with lower computational complexity, can obtain much better reconstruction quality control than other wavelet-based methods.
维持重建信号的质量是至关重要的，对于有损 ECG 数据压缩。基于小波的方法使用递归分解过程不适合实时 ECG 信号记录和通常获得非线性压缩性能与失真敏感量化误差。敏感的反应是造成不影响字长增长（WLG）的影响和不利于 ECG 数据压缩的重建质量控制。在本文中，1-D 可逆舍入非递归离散周期小波变换应用于克服 WLG 放大效应的机制，抵抗误差传播和八度系数的显著归一化。这两个机制可以设计一个多变量量化方案，获得具有近似线性失真特性的压缩性能。量化方案可以用一个单一的控制变量进行控制。基于线性压缩性能，提出了一种线性量化尺度预测模型，以保证重建质量。在使用麻省理工学院-比奇心律失常数据库后，实验结果表明，该系统具有较低的计算复杂度，可以获得比其他基于小波的方法更好的重建质量控制。
