摘要
小波变换在信号处理中有着广泛的应用,能同时分析时域和频域方面的信息,但是传统的小波变换依赖于傅立叶变换,有大量的卷积运算,运算速度较慢.该文讨论了第二代小波变换的原理,并采用它来处理脑电信号.提升算法作为构造第二代小波的关键技术,通过预测确定高频信息,更新后得到正确的低频信息,它不依赖于傅立叶变换,大大提高了运算速度.通过分析提升算法的基本原理,用第二代小波变换实现了对脑电信号的节律(δ、θ、α、β)提取,并得到了令人满意的效果.
Wavelet transform is widely applied in signal processing, which can analyze time domain or frequency domain information. But the traditional wavelet transform depends on Fourier transform, and its realization is based on a large amount of convolution computation,leading to the low operation speed. This paper discusses the principles of second generation wavelet transform, and applies it to process the electroeneephalo-graph （EEG）. Lifting scheme is the key technique to construct the second generation wavelet transform. High-frequency information is determined by predicting, and the correct low-frequency information is obtained through update. Lifting scheme does not depend on the Fourier transform, thus greatly improves the speed of operation. By analyzing the basic principles of lifting scheme, we extracted the four rhythms of EEG through second generation wavelet transform, and got satisfactory re- sults.
出处
《南京信息工程大学学报（自然科学版）》
CAS
2013年第1期60-63,共4页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
广东省科技计划项目(2009B08-0701007)
关键词
第二代小波变换
脑电信号
提升算法
节律提取
second generation wavelet transform
EEG
lifting scheme
rhythm extraction
