{"type":"ScholarlyArticle","title":"GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification","authors":[{"name":"Ziyu Jia","slug":"ziyu-jia","given":"Ziyu","family":"Jia","affiliation":"School of Computer and Information Technology , Beijing Jiaotong University , Beijing , China; Beijing Key Laboratory of Traffic Data Analysis and Mining , Beijing , China"},{"name":"Youfang Lin","slug":"youfang-lin","given":"Youfang","family":"Lin","affiliation":"School of Computer and Information Technology , Beijing Jiaotong University , Beijing , China; Beijing Key Laboratory of Traffic Data Analysis and Mining , Beijing , China; CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation , Beijing , China"},{"name":"Jing Wang","slug":"jing-wang","given":"Jing","family":"Wang","affiliation":"School of Computer and Information Technology , Beijing Jiaotong University , Beijing , China; Beijing Key Laboratory of Traffic Data Analysis and Mining , Beijing , China; CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation , Beijing , China"},{"name":"Ronghao Zhou","slug":"ronghao-zhou","given":"Ronghao","family":"Zhou","affiliation":"School of Computer and Information Technology , Beijing Jiaotong University , Beijing , China"},{"name":"Xiaojun Ning","slug":"xiaojun-ning","given":"Xiaojun","family":"Ning","affiliation":"School of Computer and Information Technology , Beijing Jiaotong University , Beijing , China"},{"name":"Yuanlai He","slug":"yuanlai-he","given":"Yuanlai","family":"He","affiliation":"School of Computer and Information Technology , Beijing Jiaotong University , Beijing , China"},{"name":"Yaoshuai Zhao","slug":"yaoshuai-zhao","given":"Yaoshuai","family":"Zhao","affiliation":"CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation , Beijing , China"}],"abstract":"Sleep stage classification is essential for sleep assessment and disease diagnosis. However, how to effectively utilize brain spatial features and transition information among sleep stages continues to be challenging. In particular, owing to the limited knowledge of the human brain, predefining a suitable spatial brain connection structure for sleep stage classification remains an open question. In this paper, we propose a novel deep graph neural network, named GraphSleepNet, for automatic sleep stage classification. The main advantage of the GraphSleepNet is to adaptively learn the intrinsic connection among different electroencephalogram (EEG) channels, represented by an adjacency matrix, thereby best serving the spatial-temporal graph convolution network (ST-GCN) for sleep stage classification. Meanwhile, the ST-GCN consists of graph convolutions for extracting spatial features and temporal convolutions for capturing the transition rules among sleep stages. Experiments on the Montreal Archive of Sleep Studies (MASS) dataset demonstrate that the GraphSleepNet outperforms the state-of-the-art baselines.","doi":"10.24963/ijcai.2020/184","url":"https://ijcai.org/proceedings/2020/184","pdf":"https://ijcai.org/proceedings/2020/0184.pdf","markdown":"https://ijcai.org/proceedings/2020/184.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/184","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":184,"pages":{"first":1324,"last":1330},"keywords":["Machine Learning Applications: Bio/Medicine","Machine Learning: Time-series;Data Streams","Data Mining: Applications","Data Mining: Mining Spatial, Temporal Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24664/file_24664.m3u8","length_sec":289,"provider":"hls"}],"references":[{"position":1,"raw":"[Aydore et al., 2013] Sergul Aydore, Dimitrios Pantazis, and Richard M Leahy. 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