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

MohammadMehdi Kafashan; Gaurang Gupte; Paul Kang; Orlandrea Hyche; Anhthi H Luong; G V Prateek; Yo-El S Ju; Ben Julian A Palanca

doi:10.1016/j.jneumeth.2024.110064

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

J Neurosci Methods. 2024 Jan 30:407:110064. doi: 10.1016/j.jneumeth.2024.110064. Online ahead of print.

Authors

MohammadMehdi Kafashan¹, Gaurang Gupte², Paul Kang², Orlandrea Hyche², Anhthi H Luong², G V Prateek³, Yo-El S Ju⁴, Ben Julian A Palanca⁵

Affiliations

¹ Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA. Electronic address: kafashan@wustl.edu.
² Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
³ Calico Life Sciences LLC, South San Francisco, CA, USA.
⁴ Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA; Department of Neurology, Division of Sleep Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
⁵ Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Center on Biological Rhythms and Sleep, Washington University in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

PMID: 38301832
DOI: 10.1016/j.jneumeth.2024.110064

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.

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