Probing epileptic disorders with lightweight neural network and EEG's intrinsic geometry

The nonlinear dynamical systems can be stabilized on attractors in chaotic states, where the attractors depicted by dynamical trajectories may take on specific geometries. Electroencephalogram (EEG) signals are typically chaotic signals that have various nonlinear dynamic characteristics. Intrinsic...

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Bibliographic Details
Published in:	Nonlinear dynamics Vol. 111; no. 6; pp. 5817 - 5832
Main Authors:	Song, Zhenxi, Deng, Bin, Zhu, Yulin, Cai, Lihui, Wang, Jiang, Yi, Guosheng
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.03.2023 Springer Nature B.V
Subjects:	Accuracy Algorithms Attractors (mathematics) Automotive Engineering Classical Mechanics Control Convulsions & seizures Deep learning Disorders Dynamic characteristics Dynamical Systems Electroencephalography Engineering Epilepsy Geometry Machine learning Mechanical Engineering Neural networks Nonlinear dynamics Nonlinear systems Original Paper Segments Seizures Vibration EEG geometry Epileptic disorders Electroencephalogram (EEG) Seizure detection Chaotic system Fully convolutional neural network
ISSN:	0924-090X, 1573-269X
Online Access:	Get full text
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