The Performance Evaluation of the State-of-the-art EEG-based Seizure Prediction Models

Overview

Journal Front Neurol

Specialty Neurology

Date 2022 Dec 12

PMID 36504642

Authors

Zhe Ren

Xiong Han

Bin Wang

Affiliations

Soon will be listed here.

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.

Citing Articles

The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials.

Kerr W, McFarlane K, Pucci G Front Neurol. 2024; 15:1425490.

PMID: 39055320 PMC: 11269262. DOI: 10.3389/fneur.2024.1425490.

References

Hunter M, Smith R, Hyslop W, Rosso O, Gerlach R, Rostas J . The Australian EEG database. Clin EEG Neurosci. 2005; 36(2):76-81. DOI: 10.1177/155005940503600206. View

Alvarado-Rojas C, Valderrama M, Fouad-Ahmed A, Feldwisch-Drentrup H, Ihle M, Teixeira C . Slow modulations of high-frequency activity (40-140-Hz) discriminate preictal changes in human focal epilepsy. Sci Rep. 2014; 4:4545. PMC: 3971396. DOI: 10.1038/srep04545. View

Li Y, Liu Y, Guo Y, Liao X, Hu B, Yu T . Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction. IEEE Trans Cybern. 2021; 52(11):12189-12204. DOI: 10.1109/TCYB.2021.3071860. View

Iasemidis L . Epileptic seizure prediction and control. IEEE Trans Biomed Eng. 2003; 50(5):549-58. DOI: 10.1109/tbme.2003.810705. View

Mormann F, Elger C, Lehnertz K . Seizure anticipation: from algorithms to clinical practice. Curr Opin Neurol. 2006; 19(2):187-93. DOI: 10.1097/01.wco.0000218237.52593.bc. View

Howbert J, Patterson E, Matt Stead S, Brinkmann B, Vasoli V, Crepeau D . Forecasting seizures in dogs with naturally occurring epilepsy. PLoS One. 2014; 9(1):e81920. PMC: 3885383. DOI: 10.1371/journal.pone.0081920. View

Iasemidis L, Shiau D, Chaovalitwongse W, Sackellares J, Pardalos P, Principe J . Adaptive epileptic seizure prediction system. IEEE Trans Biomed Eng. 2003; 50(5):616-27. DOI: 10.1109/TBME.2003.810689. View

Tang J, El Atrache R, Yu S, Asif U, Jackson M, Roy S . Seizure detection using wearable sensors and machine learning: Setting a benchmark. Epilepsia. 2021; 62(8):1807-1819. PMC: 8457135. DOI: 10.1111/epi.16967. View

Behnam M, Pourghassem H . Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search. Comput Methods Programs Biomed. 2016; 132:115-36. DOI: 10.1016/j.cmpb.2016.04.014. View

10.

Martis R, Rajendra Acharya U, Tan J, Petznick A, Tong L, Chua C . Application of intrinsic time-scale decomposition (ITD) to EEG signals for automated seizure prediction. Int J Neural Syst. 2013; 23(5):1350023. DOI: 10.1142/S0129065713500238. View

11.

Krishnan B, Vlachos I, Wang Z, Mosher J, Najm I, Burgess R . Epileptic focus localization based on resting state interictal MEG recordings is feasible irrespective of the presence or absence of spikes. Clin Neurophysiol. 2014; 126(4):667-74. DOI: 10.1016/j.clinph.2014.07.014. View

12.

Bandarabadi M, Teixeira C, Rasekhi J, Dourado A . Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol. 2014; 126(2):237-48. DOI: 10.1016/j.clinph.2014.05.022. View

13.

Wei X, Zhou L, Zhang Z, Chen Z, Zhou Y . Early prediction of epileptic seizures using a long-term recurrent convolutional network. J Neurosci Methods. 2019; 327:108395. DOI: 10.1016/j.jneumeth.2019.108395. View

14.

Buyukcakir B, Elmaz F, Mutlu A . Hilbert Vibration Decomposition-based epileptic seizure prediction with neural network. Comput Biol Med. 2020; 119:103665. DOI: 10.1016/j.compbiomed.2020.103665. View

15.

Zhang Z, Parhi K . Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power. IEEE Trans Biomed Circuits Syst. 2015; 10(3):693-706. DOI: 10.1109/TBCAS.2015.2477264. View

16.

Huang Y, Li Q, Yang Q, Huang Z, Gao H, Xu Y . Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network. Front Neurorobot. 2021; 15:690220. PMC: 8245758. DOI: 10.3389/fnbot.2021.690220. View

17.

Hejazi M, Nasrabadi A . Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods. Cogn Neurodyn. 2019; 13(5):461-473. PMC: 6746896. DOI: 10.1007/s11571-019-09534-z. View

18.

Yang X, Zhao J, Sun Q, Lu J, Ma X . An Effective Dual Self-Attention Residual Network for Seizure Prediction. IEEE Trans Neural Syst Rehabil Eng. 2021; 29:1604-1613. DOI: 10.1109/TNSRE.2021.3103210. View

19.

Usman S, Khalid S, Bashir S . A deep learning based ensemble learning method for epileptic seizure prediction. Comput Biol Med. 2021; 136:104710. DOI: 10.1016/j.compbiomed.2021.104710. View

20.

Schulze-Bonhage A, Sales F, Wagner K, Teotonio R, Carius A, Schelle A . Views of patients with epilepsy on seizure prediction devices. Epilepsy Behav. 2010; 18(4):388-96. DOI: 10.1016/j.yebeh.2010.05.008. View