Abstract Interictal epileptiform discharge (IED) and its spatial distribution are crucial for the diagnosis, classification and treatment of epilepsy. Manual annotation by electroencephalography (EEG) experts led to a lack of publicly available datasets from multiple epilepsy centers, impeding the development of automatic IED detection. We present an EEG database containing annotated interictal epileptic EEG data from 84 patients. We extracted 20-minute continuous raw EEG data from each patient, amassing 28 hours in total. IEDs and the state of consciousness (wake/sleep) were meticulously annotated by at least 3 EEG experts. Based on the occurrence regions, the discharges are categorized into five types: generalized IED, frontal IED, temporal IED, occipital IED, and centro-parietal IED. All EEG data were segmented into 4-second epochs. This resulted 2,516 IED epochs and 22,933 non-IED epochs totally. We develop a VGG model for IED detection trained and validated on the present dataset. The integration of consciousness information improves model performance, particularly at high levels of sensitivity. Furthermore, our dataset is demonstrated to serve as a robust tool for validating existing IED detection models and for automatically classifying IEDs types based on spatial distribution.

Abstract enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) and 166 094 non-IED 4-second video-EEG segments. The video data is processed by the proposed patient detection method, with frame difference and Simple Keypoints (SKPS) capturing patients' movements. EEG data is processed with EfficientNetV2. The video and EEG features are fused via a multilayer perceptron. We developed a comparative model, termed nEpiNet, to test the effectiveness of the video feature in vEpiNet. The 10-fold cross-validation was used for testing. The 10-fold cross-validation showed high areas under the receiver operating characteristic curve (AUROC) in both models, with a slightly superior AUROC (0.9902) in vEpiNet compared to nEpiNet (0.9878). Moreover, to test the model performance in real-world scenarios, we set a prospective test dataset, containing 215 hours of raw video-EEG data from 50 patients. The result shows that the vEpiNet achieves an area under the precision-recall curve (AUPRC) of 0.8623, surpassing nEpiNet's 0.8316. Incorporating video data raises precision from 70% (95% CI, 69.8%-70.2%) to 76.6% (95% CI, 74.9%-78.2%) at 80% sensitivity and reduces false positives by nearly a third, with vEpiNet processing one-hour video-EEG data in 5.7 minutes on average. Our findings indicate that video data can significantly improve the performance and precision of IED detection, especially in prospective real clinic testing. It suggests that vEpiNet is a clinically viable and effective tool for IED analysis in real-world applications.