基于子波变换的癫痫脑电信号检测方法的研究

Detection of Epileptic electroencephalogram signals based on wavelet transform

当前癫痫自动检测方法,通常采用希尔伯特黄变换结合脑电信号变换规律进行检测,易受到噪声的干扰,检测结果存在一定的误差。据此,深入研究基于子波变换的癫痫脑电信号检测方法,依据子波变换检测癫痫脑电信号的原理,采用子波变换对含噪的脑电信号进行去噪后,考虑到癫痫患者发病时,脑电信号里异常特征波导致信号波动幅度较大,采用TQWT小波分解并重构脑电信号,提取重构后的脑电信号里有效值与峰峰值指标构成特征分量,根据特征分量设定正常与发病两种样本,通过支持向量机(support vector machine,SVM)分类器对脑电波信号样本分类,实现患者癫痫脑电信号的准确检测。实验结果表明,所提方法可有效检测癫痫脑电信号,检测灵敏度、特异性和准确率均值分别是98.73%、18.84%、98.87%,适用于癫痫脑电信号检测。

The current automatic detection method for Epilepsy usually adopts Hilbert Huang transform combined with EEG signal transformation law,and is susceptible to noise interference,and the result is not accurate.Based on this,the method of detecting Epileptic EEG signals based on wavelet transform was studied,and the principle of detecting Epileptic EEG signals based on wavelet transform was adopted.The wavelet transform was used to denoise the noisy EEG signals,considering the incidence of epilepsy patients.When the abnormal characteristic wave in the EEG signal causing the signal fluctuation amplitude large,the TQWT wavelet was used to decompose and reconstruct the EEG signal,and the eigenvalue and peak-to-peak value of the reconstructed EEG signal were used to form the characteristic component,which was set according to the characteristic component.Both normal and onset samples were used to classify brainwave signal samples by support vector machine(SVM)classifiers to achieve accurate detection of epilepsy EEG signals.The experimental results show that the proposed method can effectively detect epileptic EEG signals.The sensitivity,specificity and accuracy of detection are 98.73%,18.84%%and 98.87%,respectively.It is suitable for EEG detection of epilepsy.