Author:
Ren Zhe,Han Xiong,Wang Bin
Abstract
The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.
Subject
Neurology (clinical),Neurology
Reference116 articles.
1. Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the global burden of disease study 2016;Collaborators;Lancet Neurol.,2019
2. Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine;Zhang;Br J Clin Pharmacol.,2018
3. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy;Ramgopal;Epilepsy Behav.,2014
4. Nonlinear dynamics of electrocorticographic data in temporal lobe epilepsy;Iasemidis;J Clin Neurophysiol.,1988
5. Long time scale spatio-temporal patterns of entrainment in preictal ecog data in human temporal lobe epilepsy;Iasemidis;Epilepsia.,1990
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献