Deep learning‐based classification and segmentation of interictal epileptiform discharges using multichannel electroencephalography

Abstract Objective This study was undertaken to develop a deep learning framework that can classify and segment interictal epileptiform discharges (IEDs) in multichannel electroencephalographic (EEG) recordings with high accuracy, preserving both spatial information and interchannel interactions. Methods We proposed a novel deep learning framework, U‐IEDNet, for detecting IEDs in multichannel EEG. The U‐IEDNet framework employs convolutional layers and bidirectional gated recurrent units as a temporal encoder to extract temporal features from single‐channel EEG, followed by the use of transformer networks as a spatial encoder to fuse multichannel features and extract interchannel interaction information. Transposed convolutional layers form a temporal decoder, creating a U‐shaped architecture with the encoder. This upsamples features to estimate the probability of each EEG sampling point falling within the IED range, enabling segmentation of IEDs from background activity. Two datasets, a public database with 370 patient recordings and our own annotated database with 43 patient recordings, were used for model establishment and validation. Results The results showed prominent advantage compared with other methods. U‐IEDNet achieved a recall of .916, precision of .911, F1‐score of .912, and false positive rate (FPR) of .030 on the public database. The classification performance in our own annotated database achieved a recall of .905, a precision of .902, an F1‐score of .903, and an FPR of .072. The segmentation performance had a recall of .903, a precision of .916, and an F1‐score of .909. Additionally, this study analyzes attention weights in the transformer network based on brain network theory to elucidate the spatial feature fusion process, enhancing the interpretability of the IED detection model. Significance In this paper, we aim to present an artificial intelligence‐based toolbox for IED detection, which may facilitate epilepsy diagnosis at the bedside in the future. U‐IEDNet demonstrates great potential to improve the accuracy and efficiency of IED detection in multichannel EEG recordings.