文献类型：期刊文献

英文题名：A Filter-based GAN to Enhance Learning Frequency Information of EEG for Sleep Staging

作者：Fan, Yannan[1]; Wang, Bei[1]

机构：[1] East China University of Science and Technology, School of Information Science and Engnieering, Department of Automation, Shanghai, 200237, China

年份：2023

起止页码：236

外文期刊名：ICIIBMS 2023 - 8th International Conference on Intelligent Informatics and Biomedical Sciences

收录：EI(收录号：20240315383671)

语种：英文

外文关键词：Biomedical signal processing - Electroencephalography - Frequency domain analysis - Sleep research

摘要：In recent years, automatic sleep staging models based on artificial intelligence have been developed to provide physicians with more efficient diagnostic assistance support. However, the performance of automatic sleep staging models is limited by several problems such as insufficient available training data or imbalance among the classes. A common method is to manually synthesis the required data using generative adversarial networks(GANs). As an extremely informative signal in the frequency domain, existing electroencephalogram(EEG) synthesis methods pay little attention to this aspect. In this study, to enhance the learning of frequency domain information, a filter-based GAN is proposed for EEG signals synthesis. Rather than full-band learning, filtering allows the GAN to focus the learning on the frequency components containing the dominant rhythmic characteristics under different sleep stages. The results of several comparison experiments show that the filter-based GAN can synthesize higher quality EEG signals for sleep staging, with significantly improved scores on the three metrics compared to the baseline model. ? 2023 IEEE.