Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms

Abstract

Abstract Detection and spatial distribution analysis of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalography (EEG) recordings of the frontal, temporal, and occipital scalp regions. This study included 38 patients with frontal (n = 15), temporal (n = 13), or occipital (n = 10) IEDs and 232 controls without IEDs from a single tertiary center. All EEG recordings were segmented into 1.5-s epochs and fed into 1- or 2-dimensional convolutional neural networks to construct binary models to detect IEDs in each focal region and multiclass models to categorize IEDs into frontal, temporal, and occipital regions. The binary models exhibited accuracies of 79.3–86.4%, 93.3–94.2%, and 95.5–97.2% for frontal, temporal, and occipital IEDs, respectively. The three and four multiclass models exhibited an accuracy of 87.0–88.7% and 74.6–74.9%, respectively, with temporal, occipital, and non-IEDs F1-scores of 89.9–92.3%, 84.9–90.6%, and 84.3–86.0% and 86.6–86.7%, 86.8–87.2%, and 67.8–69.2% for the three- and four-class (frontal, 50.3–58.2%) models, respectively. The constructed deep learning-based models could help enhance EEG interpretation. Although they performed well, the resolution of region-specific focal IED misinterpretations and further model improvement are needed.