Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review

Le résumé fourni par la source

Epilepsy is a neurological disorder affecting ~50 million patients worldwide (30% refractory cases) with complex dynamical behavior governed by nonlinear differential equations. Seizures severely impact patients' quality of life and may lead to serious complications. As a primary diagnostic tool, electroencephalography (EEG) captures brain dynamics through non-stationary time series with measurable chaotic and fractal properties. However, EEG signals are highly nonlinear and non-smooth, and conventional linear analysis methods limited by Fourier spectral decomposition cannot capture the inherent phase space dispersion and multifractal geometries of epileptic signals. In recent years, nonlinear dynamics methods such as chaos theory, fractal analysis, and entropy computation have provided new perspectives for EEG signal analysis, while deep learning approaches like convolutional neural networks and long short-term memory networks further enhance the robustness of dynamical pattern recognition through end-to-end nonlinear feature extraction. These methods reveal dynamic patterns in signals, thereby substantially improving epilepsy detection and prediction accuracy. This survey reviews research progress in automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning, evaluating key techniques including Lyapunov exponents, fractal dimensions, and entropy metrics. Results highlight three paradigm shifts, including the demonstrated superiority of nonlinear features in capturing preictal transitions, the critical role of attention mechanisms in processing long-range dependencies, and the significant advantages achieved by integrating nonlinear attributes with deep learning architectures for cross-patient generalization and noise suppression. Furthermore, this survey identifies persistent challenges including clinical translation barriers, algorithm performance trade-offs, and feature extraction/selection limitations. It emphasizes the need to integrate algebraic topology and graph convolutional deep learning to address multiscale dynamics, and proposes a unified framework for regulatory-compliant clinical translation that bridges the gap between research innovations and real-world clinical deployment, while outlining future research priorities focused on multimodal data fusion and regulatory-compliant validation frameworks.

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