Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy

To develop a predicted algorithm for drug-resistant epilepsy (DRE) in newly diagnosed temporal lobe epilepsy (TLE) patients.

A total of 139 newly diagnosed TLE patients were prospectively enrolled, and long-term video EEG monitoring was recorded. Clinical evaluations, including seizure frequency and antiseizure medications (ASMs) usage, were collected and prospectively followed up for 24 months. Interictal EEG data were used for feature extraction, identifying 216 EEG network features. Traditional machine learning and ensemble learning techniques were employed to predict DRE outcomes.

Over two years, TLE patients with DRE exhibited significant EEG differences, particularly in frontotemporal θ-band networks, characterized by increased connectivity metrics such as phase lag index (P = 0.000), etc. The predictive algorithm based on EEG features achieved accuracies between 59.2 %-84.6 % (AUC: 0.60-0.87). When compared to the whole brain, EEG features of the frontotemporal network showed improved classification performance in Naïve Bayes (P = 0.032), Tree Bagger (P = 0.021), and Subspace Discriminant (P = 0.022) models. The ensemble learning technique (Tree Bagger) delivered the best prediction results, achieving 91.5 % accuracy, 97 % sensitivity, 81 % specificity, and AUC of 0.92.

Increased frontotemporal EEG connectivity was observed in TLE patients with 2-year DRE. A predictive model based on routine EEG provides an accessible method for forecasting ASMs efficacy.

This study highlights the clinical utility of EEG-based algorithms in identifying DRE early, aiding personalized treatment strategies and improving patient outcomes.