文献类型：期刊文献

英文题名：An Attention-UNet Model to Remove Artifacts from Single-Channel EEG

作者：Chen, Suo[1]; Wang, Bei[1]

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

年份：2024

外文期刊名：Proceedings - 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

收录：EI(收录号：20251318133247)

语种：英文

外文关键词：Brain mapping - Convolutional neural networks - Deep neural networks - Electrocardiography - Electrotherapeutics - Image segmentation - Signal denoising

摘要：Electroencephalography (EEG) signals are often unavoidably contaminated by physiological artifacts during acquisition. Reducing the effects of these artifacts is important for various EEG-based interpretation tasks. However, the existing deep learning EEG denoising methods currently have some shortcomings: information lost caused by multiple downsampling and upsampling, artifact-related regions not considered separately, etc. In this study, a novel deep learning network named Attention-Unet (AUNet) is proposed for EEG denoising. AUNet uses skip connections to connect the decoder and encoder for feature information fusion, while the attention gate focuses on activating artifact-related area information. The developed network is evaluated on a semi-simulated dataset including ocular electrocardiography (EOG), electromyography (EMG), and electrocardiography (ECG). The experimental results show that the presented AUNet outperforms existing methods in metrics including relative root mean square error (RRMSE) and signal-to-noise ratio improvement (ΔSNR). ? 2024 IEEE.