LAUSR

Computer-aided diagnosis of epilepsy based on Electroencephalography (EEG) analysis is a beneficial practice which adopts machine learning to increase the recognition rate and saves physicians from long hours of EEG inspection. However multi-channel epilepsy EEG signals reflect significant nonlinearity with different degrees of cross-talk among channels, which further leads to high dimensional features extracted from EEG. These shortcomings make the performance of epilepsy detection with machine learning difficult to improve. In order to get fast and accurate detection performance, a feature dimension reduction algorithm based on epilepsy locality preserving projections (E-LPP) is proposed. E-LPP, by preserving the low-dimensional manifold as much as possible, enables to analyze signals of non-linear, non-stationary and high-dimensional nature. To get the best performance, we determine the hyperparameters of E-LPP by grid search. Subsequently a fusion epilepsy detection framework combined feature extraction with E-LPP is proposed to classify whether subjects’ seizure onset or not. We test our method on two well-known and widely studied datasets which includes ictal and interictal EEG recordings. The experimental result on recall, precision and F1 is superior to the common traditional dimensionality reduction algorithm, manifold learning algorithm and autoencoder based deep learning, which indicates this proposed method not only makes it possible to solve nonlinearity and cross-talk among channels in EEG, but also tackles the inherent difficulties regarding unbalanced epilepsy data with high metrics.