心电数据压缩的小波神经元选择方法

Xinping Yan, Qiaohui Guo, Yongming Yang
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引用次数: 1

摘要

本文提出了一种用于心电数据压缩的小波网络及其小波神经元的选择。讨论了频域匹配和正交最小二乘(OLS)算法选择小波基及其数量的方法。我们选择Morlet小波作为母小波,用心电信号进行仿真。结果表明,在心电上定位的Morlet小波多达152个。但经过OLS算法的过滤后,它急剧减少。该方法可以使小波网络驱动的大小达到最优,并显著减少小波网络的训练时间。该算法还能很好地重建心电信号。仿真结果表明,该方法能较好地反映各波的位置和强度。因此,该算法具有较高的压缩比和保真度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wavelet neuron selection method for ECG data compression
In this paper, a wavelet network for the Electrocardiograph (ECG) data compression and the selection of its wavelet neuron are presented. The methods of the frequency-domain matching and the orthogonal least square (OLS) algorithm in selecting the wavelet basis and its quantity were discussed. We choose Morlet wavelet as the mother wavelet, and use the ECG signal for simulation. The result demonstrates that the number of Morlet wavelets whose spectrums locating at the ECG is up to 152. But after filtrated by the OLS algorithm, it reduces sharply. This method can make the size of the wavelet network driving to optimum and reduce the training time of the wavelet network significantly. The algorithm also can reconstruct the ECG signal very well. The results of simulation indicate that it can reflect the location and intensity of all waves correctly. Consequently, the algorithm has higher compression ratio and fidelity.
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