Key result
Combining probabilistic diffusion models for data augmentation with AUPRC maximization improved precision by 4.5% and F1-score by 0.7% in within-hospital evaluation, and by 40.04% and 18.74% in cross-hospital evaluation.
Why the study?
Automated interictal epileptiform discharge detection suffers from severe data imbalance and low precision when applied to cross-institution datasets, resulting in false detections that consume clinical time.
Using probabilistic diffusion models for data augmentation and AUPRC maximization significantly improves the precision and generalizability of automated IED detection in EEG data.
May assist epilepsy diagnosis via automated IED detection amid imbalance; leaves open prospective clinical validation.
Recently, automated Interictal Epileptiform Discharge (IED) detection has attracted significant attention as a challenging predictive data analysis task aimed at improving early epilepsy diagnosis. Automated IED detection simplifies visual inspection and assists clinicians in identifying crucial IED waveform patterns in electroencephalographic (EEG) brain activities. However, IEDs are vastly outnumbered by non-IED or background data, directly training on such data leads to a detrimental impact on model performance. Moreover, most existing methods lack high precision when tested on cross-institution datasets and this will lead to time wasted by the neurologist having to look at predicted IEDs that are not IEDs. To address these issues, we propose a novel approach that employs probabilistic diffusion models for data augmentation alongside AUPRC maximization methods. This approach effectively addresses data scarcity and imbalance by combining real and synthesized IEDs to fully balance the training dataset, resulting in a 4.5% increase in precision and a 0.7% improvement in the F1-score during within-data evaluation. Additionally, it proves to be robust and generalizable, as evidenced by a 40.04% and 18.74% enhancement in precision and F1-score when applied to cross-data evaluation using data from another hospital.
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ZHANG et al. (2025) studied Epilepsy (Interictal Epileptiform Discharges) (n=510). Probabilistic diffusion models for data augmentation with AUPRC maximization vs. Standard training without data augmentation or with Binary Cross-Entropy loss was evaluated on Precision and F1-score for IED detection. Combining probabilistic diffusion models for data augmentation with AUPRC maximization improved precision by 4.5% and F1-score by 0.7% in within-hospital evaluation, and by 40.04% and 18.74% in cross-hospital evaluation.
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