A hybrid self-attention deep learning framework for multivariate sleep stage classification

• Published in: BMC bioinformatics Vol. 20; no. S16; pp. 586 - 10
• Main Authors: Yuan, Ye, Jia, Kebin, Ma, Fenglong, Xun, Guangxu, Wang, Yaqing, Su, Lu, Zhang, Aidong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02.12.2019; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Attention mechanism; Benchmarking; Bioinformatics; Biological activity; Cable television broadcasting industry; Classification; Computational biology; Databases as Topic; Deep Learning; Electrocardiography; Electroencephalography - methods; Eye movements; Humans; Inspection; Machine learning; Medical research; Multivariate Analysis; Multivariate time series; Neural networks; Patient monitoring equipment; Physiology; Polysomnography; Representations; Researchers; ROC Curve; Sleep; Sleep monitors; Sleep stage classification; Sleep Stages - physiology; United States; Taiwan; Deep learning; Attention mechanism; Sleep stage classification; Multivariate time series; Polysomnography

Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-related disorders. To alleviate the time consumption caused by m...