Nieliniowa dynamika mózgu podczas snu (badanie EEG) przewiduje skuteczność głębokich implantów mózgowych w leczeniu padaczki - badanie wstępne

OBJECTIVE: Anterior nucleus of the thalamus deep brain stimulation (ANT-DBS) is an effective therapeutic option for drug-resistant epilepsy (DRE); however, substantial inter-individual variability in treatment response limits its clinical optimization. This study aimed to develop an objective and explainable preoperative prediction model for ANT-DBS outcomes using nonlinear dynamical features derived from preoperative N2 sleep electroencephalography (EEG). APPROACH: Artifact-free N2 sleep EEG segments were retrospectively collected from 26 patients with DRE who underwent ANT-DBS and were classified as responders or non-responders according to postoperative seizure reduction. Nonlinear dynamical features, including conditional entropy (CE), robust permutation entropy (RPE), and their multiscale variants, were extracted across six frequency bands (δ, θ, α, σ, β, and low-γ). Statistically significant features were identified using the Mann-Whitney U test with false discovery rate (FDR) correction (q < 0.05). These features were subsequently integrated into a multidimensional feature space to construct a support vector machine (SVM) classifier. Model interpretability was further evaluated using the SHapley additive explanations (SHAP) framework. MAIN RESULTS: Non-responders exhibited significantly higher complexity in the δ and σ bands (q < 0.05), indicating impaired thalamocortical rhythmic fidelity and disrupted synchronization stability. In contrast, responders demonstrated significantly higher α-band robust permutation entropy (q < 0.01), reflecting greater cortical functional flexibility and a richer dynamical repertoire. The SVM classifier achieved an area under the curve (AUC) of 0.933 on an independent validation set, with 100% sensitivity for responder identification. SHAP-based attribution further revealed that the most influential features were strongly associated with physiologically meaningful alterations in thalamocortical network dynamics. SIGNIFICANCE: These findings demonstrate that preoperative N2 sleep EEG complexity may serve as a non-invasive and interpretable biomarker for predicting ANT-DBS outcomes in DRE. This framework provides a clinically accessible decision-support tool for personalized patient selection and precision neuromodulation.