Review of Application of Neural Networks in Epileptic Seizure Prediction

Abstract

Epilepsy, a central nervous system disease caused by abnormal discharge of brain neurons, has a significant impact on patients’ normal life. Early prediction of epileptic seizures and timely preventive measures can effectively improve the quality of life of patients. With the development of data science and big data technology, neural networks are increasingly being applied in the field of epilepsy prediction and have shown great potential for application. This paper provides a review of the application and deficiencies of neural networks in the field of epilepsy prediction, discussing the construction process of epilepsy prediction models in the following order: data- sets, data preprocessing, feature extraction, and neural networks. After introducing the characteristics of EEG signals, common types of datasets, common data preprocessing methods, and common feature extraction methods, especially manual feature extraction methods, this paper focuses on analyzing and summarizing the principles and applications of multi-layer artificial neural networks and spiking neural networks in the field of epilepsy prediction. The disadvantages of neural networks are systematically analyzed, and further application of neural networks in the field of epilepsy prediction is prospected.

Keywords

• epilepsy; eeg signal; neural networks; epilepsy prediction