基于小波神经网络的脑电信号数据压缩与棘波识别研究

Study on EEG Signals data compression and spikes rcognition with wavelet neural network

在对小波神经网络及其算法研究的基础上，提出了一种对脑电信号压缩表达和痫样脑电棘波识别的新方法。实验结果显示，小波网络在大量压缩数据的同时，能够较好的恢复原有信号，另外，在脑电信号的时频谱等高线图上，得到了易于自动识别的棘波和棘慢复合波特征，说明此方法在电生理信号处理和时频分析方面有着光明的应用前景。

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented in this paper. Wavelet network not only can compress data effectively but also can recover original signal. In addition the characteristics of the spikes and the spike-slow rhythm are detected automatically from the time-frequency isoline of EEG signal. This method can be generalized in the field of the electrophysiological signal processing and time-frequency analyzing.