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

J Neural Eng. 2019 Jun;16(3):036004. doi: 10.1088/1741-2552/ab0933. Epub 2019 Feb 21.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Cohort Studies
  • Computer Systems* / statistics & numerical data
  • Databases, Factual / statistics & numerical data
  • Deep Learning* / statistics & numerical data
  • Female
  • Humans
  • Male
  • Middle Aged
  • Neural Networks, Computer*
  • Sleep Stages / physiology*
  • Young Adult