基于非负矩阵分解的癫痫脑电自动检测

摘要

In order to overcome the issue of high-dimensional features or unsatisfactory accuracy for epileptic seizure detection,we put forward an automatic seizure detection algorithm based on FrFT (Fractional Fourier Transform) and NMF (Non-negative Matrix Factorization).Firstly,FrFT was applied on the raw EEG (Electroencephalogram) to perform time-frequency concentration.Subsequently,STFT (Short-Time Fourier Transform) was carried out to characterize the time-frequency distribution of concentrated EEG.The generated time-frequency matrix was reshaped and then reduced by NMF.At last,SVM (Support Vector Machine) was employed to classify extracted features.Experimental results indicate that the proposed method is capable of identifying normal,inter-ictal and epileptic EEG with an accuracy of 98.8％.%针对多分类癫痫检测算法因特征维数多而导致识别率不理想的问题,提出了一种基于分数阶傅里叶变换(FrFT:Fractional Fourier Transform)和非负矩阵分解(NMF:Non-negative Matrix Factorization)的癫痫脑电自动识别算法.首先采用FrFT对脑电信号进行时频聚焦,并利用短时傅里叶变换(STFT:Short-Time FourierTransform)提取脑电信号的时频特征;再应用NMF对提取的时频特征进行降维;最后将降维后的特征输入到支持向量机(SVM:Support Vector Machine)分类器中进行识别.实验结果表明,该方法能识别正常、癫痫发作间期和癫痫发作期3类脑电信号,其分类准确率可达98.8％.