Epileptic Seizure Prediction Based on EEG by Auto-Machine Learning

Abstract

The sudden epileptic seizures may not only cause accidental injuries to the patient, but also lead to psychological trauma. It is crucial to predict the onset of a seizure before it occurs. Although the current researches could achieve relatively high prediction performance, there still remain some challenges in the practical scenes, such as class-imbalance problem between pre-ictal and inter-ictal samples, manual hyperparameter tuning problem, etc. This paper proposes a feature-enhancing strategy combining automatic machine learning method to solve these problems. Firstly, the EEG signals are divided into preictal and interictal stages, and then separated into five sub-bands by the pre-processing stage. Then, the features are extracted from the preprocessed signals, followed by feature smoothing and feature augmentation process, which we employ conditional tabular generative adversarial network (CTGAN) to generate the preictal samples. Finally, the processed features are fed into the automatic machine learning (Auto-ML) for seizure prediction. The CHB-MIT EEG dataset is used in this study to evaluate the performance of our proposed method. The combination CTGAN and K-nearest neighbors (KNN), logistic regression (LR), Naive Bayes (NB) classifier and multilayer perceptron (MLP) achieved an average precision of 0.97, 0.94, 0.87 and 0.95, respectively. Auto-ML combined with CTGAN outperforms traditional machine learning models in seizure prediction, with an average accuracy of 99%. Results show that feature augmentation strategy and automatic machine learning can improve the epileptic seizures prediction performance.