基于时频分析检测EEG中癫痫样棘/尖波的方法

AUTOMATIC DETECTION OF EPILEPTIFORM SPIKES IN EEG BASED ON TIME-FREQUENCY ANALYSIS

提出了一种基于Choi-Williams分布检测EEG中癫痫样棘波/尖波的方法。该方法通过计算EEG信号的时频分布 ,得到一段信号在各个时刻上沿频率方向上的能量分布。这种能量分布相当于一种瞬时频谱 ,反映了EEG信号在局部时间范围里的波形特征。以一段EEG信号在各个时刻的瞬时频谱的平均作为这段脑电的背景信号频谱 ,通过计算每一时刻的瞬时频谱与背景信号频谱之间的频谱差 ,检测这段信号中的棘波/尖波。对临床EEG数据检测的结果表明 ,这种方法能够有效地从复杂的背景EEG信号中检出棘波 ,有良好的应用前景。

A new method for detecting spikes in the EEG based on the time-frequency representation is presented. The EEG records are divided into epochs having duration of 5s, and then the time-frequency representation of a segment of the EEG signals is evaluated by using Choi-Williams distribution in order to be able to overcome the drawbacks of the cross-terms. We define the spectral components at any time instant in the time-frequency distribution of a signal as the signal's instantaneous spectrum. The mean values of all the instantaneous spectra in a segment of EEG signal are used to describe the background activity of the EEG over the epoch. The quantitative difference between the instantaneous spectrum at each instant and the mean instantaneous spectrum is called the spectral difference. Based on the experiments, we design a nonlinear transform for processing the spectral difference. Spikes are detected by the results of the transform of spectral difference. The results show that the new method works well.