Abstract:  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%.

Key words: short-time fourier transform(STFT), seizure detection, non-negative matrix factorization(NMF), fractional fourier transform(FrFT), support vector machine(SVM)