Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data

However, as the development of computer technology, the application of machine learning introduced new ideas for seizure forecasting. Applying machine learning model onto the predication of epileptic seizure co uld help us obtain a better result and there have been plenty of scientists who have been doing such works so that there are sufficient medical data provided for researchers to do training of machine learning models. In our research, we applied traditional machine learning algorithms, such as Linear SVM, Logistic Regression, KNN (K Nearest Neighbors), and Neural Networks, like CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), and LSTM (Long Short - Term Memory), for prediction. The emphasi s of our research is to compare the AUC (Area Under the Curve) and accuracy of various models. The research result indicates that machine learning has made epileptic seizure prediction an achievable reality.