Prediction for High Risk Clinical Symptoms of Epilepsy Based on Deep Learning Algorithm

Abstract

Accurate forecasting of high-risk clinical symptoms, like epileptic seizures, has the potential to transform clinical epilepsy care and to create new therapeutic strategies for individuals in clinical decision support systems. With the development of pervasive sensor technologies, physiological signals can be captured continuously to prevent the serious outcomes caused by epilepsy. However, the progress on seizure prediction has been hindered by the lack of automatic early warning system. The existing research is classifying electroencephalograph (EEG) clips and is distinguishing the clips of onset epileptic seizures. Deep learning is a promising method to analyze the large-scale unlabeled data and to widely spread the clinical treatment and risk prediction. In this paper, we outline a patient-specific method for extracting the frequency domain and time-series data features based on the two-layer convolutional neural networks (CNNs). A data preprocessing method based on the discrete Fourier transform is proposed to convert the time-domain signal of the EEG data to the frequency-domain signal. Long short-term memory networks are introduced in seizure prediction using pre-seizure clips of the EEG dataset, expanding the use of deep learning algorithms with recurrent neural networks (RNNs). Furthermore, the proposed CNN and RNN are compared with the traditional machine learning algorithms, such as linear discriminant analysis and logistic regression, and the evaluation criteria are on the area under the curve. The extensive experimental results demonstrate that our method can effectively extract the latent features with meaningful interpretation and exhibits excellent performance for predicting epileptic preictal state changes, and hence is an effective method in detecting the epileptic seizure.