EEG-Based Epileptic Seizure Detection Model Using CNN Feature Optimization

To solve the problem that traditional epileptic seizure detection methods are cumbersome and prone to human errors, a hybrid model combining optimized feature convolutional neural network (CNN) model and traditional machine learning model is proposed, and its performance is verified on two small sample epileptic EEG datasets of Bonn and Hauz Khas. The model is based on the optimized feature CNN model for feature extraction, and the support vector machine (SVM) and random forest (RF) classifiers are selected to detect and recognize the Epileptic Electroencephalogram (EEG) seizure and normal state. The results showed that the optimized feature CNN-SVM model performs well in the binary classification tasks of epileptic EEG detection, with the highest accuracy of 99.57% and 98.00%. Compared with the traditional SVM and RF model, the classification performance is better, which can be improved by 3.92 %. The results indicated that the advantages of the deep learning algorithm in automatic feature extraction could improve the classification performance of the traditional machine learning model, and the traditional machine learning model is more suitable for small sample binary classification detection tasks than the deep learning model. It provides a scientific reference for the research of machine learning models and the clinical diagnosis of epilepsy.