A deep learning approach for real-time detection of sleep spindles

Journal of Neural Engineering
Prathamesh M KulkarniZhe Chen

Abstract

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. 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. 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. 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.

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Citations

Jan 9, 2020·Molecular Pain·Bassir CaravanJing Wang
Feb 15, 2020·Sensors·Beanbonyka RimMin Hong
Dec 29, 2020·BMC Medical Informatics and Decision Making·Cong ZhuGuo-Qiang Zhang
Dec 17, 2020·Journal of Neural Engineering·Dihong JiangYuanyuan Wang
Mar 27, 2021·Journal of Neural Engineering·David GengZhe Sage Chen
May 1, 2021·International Journal of Molecular Sciences·Rosalba SiracusaDaniela Impellizzeri

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