Abdulkader Helwan, Lina Abou-Abbas, Hussein El Amouri +2 more
Seizures are rare in EEG recordings, which makes them hard to learn: fewer than one in ten clinical segments is an actual seizure, and labeling all that data is costly. DiffEEG sidesteps both problems. It's a compact model, around 9.6 million parameters, pretrained on 1.3 million unlabeled segments so it learns the general grammar of brain signals before seeing any labels, much like a language model reading raw text first.
The fine-tuning is the clever part. Instead of chasing overall accuracy, which just rewards ignoring rare seizures, it uses reinforcement learning to push up F1 directly. Under strict patient-by-patient testing across 279 patients, it reached 81% accuracy for yes-or-no detection and kept usable seizure recall despite the imbalance. Sorting events into four subtypes was harder, near 61%.
These figures come from the abstract, so read the paper for how it holds up in a real clinic.
Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events comprise less than 10\% of clinical recordings. We present DiffEEG, a 9.6M-parameter self-supervised foundation model that addresses both limitations through denoising diffusion pre-training and reinforcement learning (RL)-based fine-tuning. Pre-trained on 1.3M unlabeled segments from the Temple University Hospital Seizure Corpus…
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