A deep learning approach for real-time detection of sleep spindles. (19th March 2019)

• Record Type: Journal Article
• Authors: Kulkarni, Prathamesh M; Xiao, Zhengdong; Robinson, Eric J; Jami, Apoorva Sagarwal; Zhang, Jianping; Zhou, Haocheng; Henin, Simon E; Liu, Anli A; Osorio, Ricardo S; Wang, Jing; Chen, Zhe
• Is Part Of: Journal of neural engineering. Volume 16:Number 3(2019:Jun.)
• Publication Date: 2019-03-19
• Subjects: sleep spindles -- spindle detection -- deep learning; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ab0933
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications. Approach . Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications. Main results . Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150–350 ms (~two–three spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species. Significance . SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments.