Unveil Sleep Spindles with Concentration of Frequency and Time

Riki Shimizu, Hau-Tieng Wu
10/27/2023
q-bio.NCcs.LGmath.NA

Official code: rikishimizu/ConceFT-Spindle (2 stars, last commit 2024-06-01).

Abstract

Objective: Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs). Methods: ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the time-frequency representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and MASS benchmark databases. We also quantify spindle IF dynamics. Results: ConceFT-S achieves F1 scores of 0.749 in Dream and 0.786 in MASS, which is equivalent to or surpass A7 and SUMO with statistical significance. We reveal that spindle IF is generally nonlinear. Conclusion: ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.

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Code Implementations(1)

rikishimizu/ConceFT-SpindleOfficial

Codes implementation for ConceFT-Spindle algorithm

22MATLABOct 21, 20232 years ago

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Cite this paper

@article{shimizu2023unveil,
  title  = {Unveil Sleep Spindles with Concentration of Frequency and Time},
  author = {Riki Shimizu and Hau-Tieng Wu},
  year   = {2023},
  eprint = {2310.18381},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.18381v1}
}

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