Speaker
Description
Transcranial direct current stimulation (tDCS) shows promise for drug-resistant epilepsy patients, but monitoring treatment efficacy remains challenging. While current monitoring relies on subjective seizure diaries, electroencephalogram (EEG) recordings offer a more objective approach through the detection of interictal epileptiform discharges (IEDs). However, automated IED detection faces challenges with varying recording conditions and treatment-induced changes in signal patterns.
We present a novel framework that enhances patient-specific deep learning models with synthetically generated EEG data for automated IED detection. Our approach incorporates personalized simulations of both the patient's epileptic activity and their tDCS treatment response. We expect to show that this synthetic data augmentation improves model resilience to recording variations and maintains consistent performance across treatment sessions. This method should reduce the reliance on expert annotation while providing robust, objective monitoring of tDCS treatment outcomes.
