A personalized semi-automatic sleep spindle detection (PSASD) framework

Mohammad Mehdi Kafashan, Gaurang Gupte, Paul Kang, Orlandrea Hyche, Anhthi H. Luong, G. V. Prateek, Yo El S. Ju, Ben Julian A. Palanca

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Sleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone. New methods: A Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components. Results: A single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures. Comparison with existing methods: PSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches. Conclusions: Overall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

Original languageEnglish
Article number110064
JournalJournal of Neuroscience Methods
Volume407
DOIs
StatePublished - Jul 2024

Keywords

  • Automated pattern recognition
  • Electroencephalogram (EEG)
  • Non-rapid eye movement sleep
  • Polysomnography
  • Signal processing
  • Sleep
  • Sleep spindle
  • Wireless EEG devices

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