Abstract

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Title:: Epilepsy prediction model based on 2D-CNN and Cox-Stuart early stopping mechanism

Author(s):: ZHANG Xizhen1; 2; ZHANG Xiaoli1; 2; L?Yang3; CHEN Fuming2; 1. School of Medical Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730050, China 2. Medical Security Center, The 940th Hospital of Joint Logistics Support Force of Chinese Peoples Liberation Army, Lanzhou 730050, China 3. Department of Ophthalmology, The 940th Hospital of Joint Logistics Support Force of Chinese Peoples Liberation Army, Lanzhou 730050, China

Keywords:: Keywords: epilepsy prediction Cox-Stuart test two-dimensional convolutional neural network deep learning

Abstract:: An epilepsy prediction model based on two-dimensional convolutional neural network and Cox-Stuart test for non-independent patients is proposed to address the problem of how to effectively predict whether epilepsy patients are going to have an attack or not. After EEG data normalization and EEG signal noise removal using a notch filter and a high-pass filter, the filtered signals are inputted into the two-dimensional convolutional neural network model for feature extraction and classification, and Cox-Stuart test is used to determine whether an early stopping is needed or not, thereby reducing the computational and time complexities of the model. The model is tested under the conditions with pre-seizure periods of 10, 30 and 60 min, respectively, and the results show that the model performs best when the pre-seizure period is 10 min. The model has an average accuracy, sensitivity and specificity of 97.70%, 97.36% and 98.04% on the test set, demonstrating its superior performance.