基于小波神经网络的ECG数据压缩研究

应用变学习速率、变动量因子的BP神经网络和传统小波变换相结合,构造了一种小波神经网络,实现了高压缩比的ECG数据压缩,该算法除了具有泛化能力强、收敛速度快的特点外,还兼具了多分辨和自适应特性,有较强的特征提取能力。实验结果表明使用该算法进行ECG压缩,可以获得较高的压缩比和保真度,并且复杂度低,在异常ECG波形出现时仍可保持较好的实时性。

ECG Data Compression Research Based on Wavelet Neural Network

Combining the alterable study rate and variable momentum factor of BP neural network with traditional wavelet transformation,a new BP wavelet neural network is constructed and the higher compression ratio of ECG data is realized.It enjoys the merits of good generalization ability and high converging speed.Multi-resolution and self-adaptation are also gained,moreover,it has a strong ability of feature extraction.The experimental result indicates that this algorithm can be used to carry on the ECG compression,and obtain a desirable compression ratio and high fidelity,in addition,the complexity is low and it can still keep good real-time character while abnormal ECG wave appears.