Nan Lin, Weifang Gao, Lian Li, Junhui Chen, Zi Liang, Gonglin Yuan, Heyang Sun, Qing Liu, Jianhua Chen, Liri Jin, Yan Huang, Xiangqin Zhou, Shaobo Zhang, Peng Hu, Chaoyue Dai, Haibo He, Yisu Dong, Liying Cui, Qiang Lu
To 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...
April 14, 2024: Neural Networks: the Official Journal of the International Neural Network Society