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Zeng X, Cai S, Xie L. Attention-guided graph structure learning network for EEG-enabled auditory attention detection. J Neural Eng 2024; 21:036025. [PMID: 38776893 DOI: 10.1088/1741-2552/ad4f1a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 05/22/2024] [Indexed: 05/25/2024]
Abstract
Objective: Decoding auditory attention from brain signals is essential for the development of neuro-steered hearing aids. This study aims to overcome the challenges of extracting discriminative feature representations from electroencephalography (EEG) signals for auditory attention detection (AAD) tasks, particularly focusing on the intrinsic relationships between different EEG channels.Approach: We propose a novel attention-guided graph structure learning network, AGSLnet, which leverages potential relationships between EEG channels to improve AAD performance. Specifically, AGSLnet is designed to dynamically capture latent relationships between channels and construct a graph structure of EEG signals.Main result: We evaluated AGSLnet on two publicly available AAD datasets and demonstrated its superiority and robustness over state-of-the-art models. Visualization of the graph structure trained by AGSLnet supports previous neuroscience findings, enhancing our understanding of the underlying neural mechanisms.Significance: This study presents a novel approach for examining brain functional connections, improving AAD performance in low-latency settings, and supporting the development of neuro-steered hearing aids.
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Affiliation(s)
- Xianzhang Zeng
- School of Intelligent Engineering, South China University of Technology, Guangzhou, People's Republic of China
| | - Siqi Cai
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Longhan Xie
- School of Intelligent Engineering, South China University of Technology, Guangzhou, People's Republic of China
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Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Med Rev 2024; 74:101897. [PMID: 38306788 DOI: 10.1016/j.smrv.2024.101897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuqi Chen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yidan Dai
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Yiming Wang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China; Department of Otolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
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Hu Y, Shi W, Yeh CH. Spatiotemporal convolution sleep network based on graph attention mechanism with automatic feature extraction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107930. [PMID: 38008039 DOI: 10.1016/j.cmpb.2023.107930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 11/12/2023] [Accepted: 11/13/2023] [Indexed: 11/28/2023]
Abstract
BACKGROUND AND OBJECTIVE Graph neural networks (GNNs) are widely used for automatic sleep staging. However, the majority of GNNs are based on spectral approaches, as far as we know, which heavily depend on the Laplacian eigenbasis determined by the graph structure with a large computing cost. METHODS We introduced a non-spectral approach named graph attention networks v2 (GATv2) as the core of our network to extract spatial information (S-GATv2 in our work), which is more flexible and intuitive than the routined spectral method. Meanwhile, to resolve the issue of weak generalization of using traditional feature extraction, the multi-convolutional layers are implemented to automatically extract features. In this work, the proposed spatiotemporal convolution sleep network (ST-GATv2) consists of multi-convolution layers and a GATv2 block. Of note, the graph attention technique to the time domain was applied to construct temporal GATv2 (T-GATv2), which intends to capture the connection between two channels in the adjacent sleep stages. Besides, the modified function is further proposed to capture the hidden changing trend information by the difference in the feature's value of the two adjacent stages. RESULTS In our experiment, we used the SS3 datasets in the MASS as our test datasets to compare with other advanced models. Our result reveals our model achieves the highest accuracy at 89.0 %. Besides, the proposed T-GATv2 block and modified function bring an approximate 0.5 % improvement in Kappa and F1-score. CONCLUSIONS Our results support the potential of graph attention mechanisms and creative blocks (T-GATv2 and modified function) in sleep classification. We suggest the proposed ST-GATv2 model as an effective tool in sleep staging in either healthy or diseased states.
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Affiliation(s)
- Yidong Hu
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China; School of Cyberspace Security, Beijing Institute of Technology, Beijing 100081, China
| | - Wenbin Shi
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education (Beijing Institute of Technology), Beijing 100081, China
| | - Chien-Hung Yeh
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education (Beijing Institute of Technology), Beijing 100081, China.
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Li T, Gong Y, Lv Y, Wang F, Hu M, Wen Y. GAC-SleepNet: A dual-structured sleep staging method based on graph structure and Euclidean structure. Comput Biol Med 2023; 165:107477. [PMID: 37717528 DOI: 10.1016/j.compbiomed.2023.107477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 08/16/2023] [Accepted: 09/04/2023] [Indexed: 09/19/2023]
Abstract
Sleep staging is a precondition for the diagnosis and treatment of sleep disorders. However, how to fully exploit the relationship between spatial features of the brain and sleep stages is an important task. Many current classical algorithms only extract the characteristic information of the brain in the Euclidean space without considering other spatial structures. In this study, a sleep staging network named GAC-SleepNet is designed. GAC-SleepNet uses the characteristic information in the dual structure of the graph structure and the Euclidean structure for the classification of sleep stages. In the graph structure, this study uses a graph convolutional neural network to learn the deep features of each sleep stage and converts the features in the topological structure into feature vectors by a multilayer perceptron. In the Euclidean structure, this study uses convolutional neural networks to learn the temporal features of sleep information and combine attention mechanism to portray the connection between different sleep periods and EEG signals, while enhancing the description of global features to avoid local optima. In this study, the performance of the proposed network is evaluated on two public datasets. The experimental results show that the dual spatial structure captures more adequate and comprehensive information about sleep features and shows advancement in terms of different evaluation metrics.
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Affiliation(s)
- Tianxing Li
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, 130000, China
| | - Yulin Gong
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, 130000, China.
| | - Yudan Lv
- The Department of Neurology, First Hospital of Jilin University, Changchun, 130000, China
| | - Fatong Wang
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, 130000, China
| | - Mingjia Hu
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, 130000, China
| | - Yinke Wen
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, 130000, China
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