Czy można przewidzieć epilepsję po pierwszym ataku gorączkowym? Wskazówki z zastosowania uczenia maszynowego do analiz EEG po ataku

OBJECTIVE: Febrile seizures (FS) are the most common seizures in childhood, yet identifying children at risk of developing epilepsy after the first FS remains challenging. We aimed to evaluate the prognostic potential of machine learning (ML) algorithms applied to post-febrile seizure electroencephalography (EEG) recordings. METHODS: We retrospectively reviewed 104 children (69 boys; mean age at first febrile seizure: 39.4 ± 18.2 months) who presented with their first febrile seizure between January 2018 and December 2021. Clinical data and EEG recordings obtained during N2 sleep were collected. EEG analysis was performed using separate preprocessing pipelines. For conventional EEG analysis, recordings were band-pass filtered between 1 and 40 Hz, and artifact-free segments were analyzed using Python-based pipelines (YASA, MNE) to extract 34 time-domain. The 34 extracted electrophysiological features were calculated across different bipolar EEG channels and evaluated together with aggregated inter-channel measures, resulting in a total of 93 input attributes used for ML model development. High-frequency oscillations (HFOs) were analyzed using a distinct pipeline applied to wideband EEG data before low-pass filtering. Six machine learning algorithms-J48 Consolidated Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting, k-nearest neighbor, and Support Vector Machine-were trained using 10 × 7 repeated cross-validation. Model performance was evaluated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (ROC AUC), and F1-score. RESULTS: Over a median follow-up of 4.2 months, 13 patients (12.5%) developed epilepsy, and all diagnoses were made within 9 months. XGBoost achieved the highest accuracy (0.89) and specificity (0.95) but had low sensitivity (0.46). J48 achieved the highest sensitivity (0.87) and ROC AUC (0.79), with a specificity of 0.71. Incorporating clinical features, including recurrent seizures, increased sensitivity to 0.95. The most relevant predictors were patient history, frequency band power, particularly increased power in lower frequency bands, and high-frequency oscillations counts. CONCLUSION: ML-based analysis of initial EEG after a first febrile seizure may assist in early epilepsy risk stratification. J48 provided superior sensitivity, and combining electroencephalography-derived biomarkers with clinical data further enhanced predictive performance. Prospective, multicenter studies are warranted to confirm these findings.