Research on Detection of Interictal Epileptiform Discharges in Children Based on Deep Learning
Rao Wenhao1, Chen Duo1#*, Zhang Ling2, Jiang Jun3
1(School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210000, China) 2(School of Biomedical Engineering and Medical Imaging, Hubei University of Science and Technology, Xianning 437100, Hubei, China) 3(Department of Electrophysiology, Wuhan Children′s Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430010, China)
Abstract：Interictal epileptiform discharges (IED) are crucial for epilepsy diagnosis, but the non-stationarity of EEG signals make IED detection complicated. Traditional manual interpretation of EEG is subjective and time-consuming. With the development of machine learning and deep learning, computer-assisted models have been proposed in the field of IED detection. CNN-based IED detection methods have achieved promising results, but CNN is less effective in capturing long-range dependencies within time series data. Transformer is good at processing sequential data by adopting a self-attention mechanism, which enables it to capture long-term dependencies. This study proposes a novel Transformer-based IED detection method, which first uses simple convolution to extract local features of IEDs and then employs a Transformer to further model the long-range dependencies of these features. To address the scarcity of IED data, a new Transformer-based generative adversarial network (GAN) is also designed to augment the IED data. Based on an analysis of 11 pediatric epilepsy patients, the new method achieved an average accuracy of 96.11%, average recall of 97.08%, and average precision of 93.85% in the binary classification task on the augmented dataset. In the multi-class classification task, the average recall reached 93.47%, and the average precision was 93.84%. This study provides valuable reference for the future application of deep learning in automatic IED detection.
作者简介: #中国生物医学工程学会会员(Member,Chinese Society of Biomedical Engineering)
引用本文:
饶文豪, 陈多, 张玲, 江军. 基于深度学习的儿童发作间期癫痫样放电检测研究[J]. 中国生物医学工程学报, 2025, 44(5): 579-590.
Rao Wenhao, Chen Duo, Zhang Ling, Jiang Jun. Research on Detection of Interictal Epileptiform Discharges in Children Based on Deep Learning. Chinese Journal of Biomedical Engineering, 2025, 44(5): 579-590.
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