Automatic Seizure Detection Based on Nonlinear Dynamical Analysis of EEG Signals and Mutual Information

Basic and Clinical Neuroscience

ARTICLE

Akbarian, Behnaz¹  Erfanian, Abbas² 

[1] Iran Neural Technology Research Centre, Iran University of Science and Technology;Department of Bioelectrical Engineering, School of Electrical Engineering, Iran University of Science and Technology

关键词: Epilepsy;    Mutual information;    Nonlinear analysis;    Recurrence quantification analysis;    Seizure detection;   

DOI  :  10.32598/bcn.9.4.227

来源: Iran University of Medical Sciences

【 摘 要 】

Introduction: In this paper, nonlinear dynamical analysis based on Recurrence Quantification Analysis (RQA) is employed to characterize the nonlinear EEG dynamics. RQA can provide useful quantitative information on the regular, chaotic, or stochastic property of the underlying dynamics. Methods: We use the RQA-based measures as the quantitative features of the nonlinear EEG dynamics. Mutual Information (MI) was used to find the most relevant feature subset out of RQA-based features. The selected features were fed into an artificial neural network for grouping of EEG recordings to detect ictal, interictal, and healthy states. The performance of the proposed procedure was evaluated using a database for different classification cases. Results: The combination of five selected features based on MI achieved 100% accuracy, which demonstrates the superiority of the proposed method. Conclusion: The results showed that the nonlinear dynamical analysis based on Rcurrence Quantification Analysis (RQA) can be employed as a suitable approach for characterizing the nonlinear EEG dynamics and detecting the seizure.

【 授权许可】

Unknown