Research Area:  Machine Learning

Abstract:

In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number of innovative algorithms have been introduced that can aid in epilepsy diagnosis with a high degree of accuracy. In recent years, the frontiers of computational epilepsy research have moved to seizure prediction, a more challenging problem. While antiepileptic medication can result in complete seizure freedom in many patients with epilepsy, up to one-third of patients living with epilepsy will have medically intractable epilepsy, where medications reduce seizure frequency but do not completely control seizures. If a seizure can be predicted prior to its clinical manifestation, then there is potential for abortive treatment to be given, either self-administered or via an implanted device administering medication or electrical stimulation. This will have a far-reaching impact on the treatment of epilepsy and patients quality of life. This paper presents a state-of-the-art review of recent efforts and journal articles on seizure prediction. The technologies developed for epilepsy diagnosis and seizure detection are being adapted and extended for seizure prediction. The paper ends with some novel ideas for seizure prediction using the increasingly ubiquitous machine learning technology, particularly deep neural network machine learning.

Keywords:  
Automated Seizure Prediction
Machine Learning
Deep Learning

Author(s) Name:  U. Rajendra Acharya, Yuki Hagiwara, Hojjat Adeli

Publisher name:  Elsevier

DOI:  10.1016/j.yebeh.2018.09.030

Volume Information:  Volume 88, November 2018, Pages 251-261

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S1525505018305791