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A reliable automatic algorithm to score fragmentary myoclonus.

Melanie Bergmann1, Birgit Högl1, Abubaker Ibrahim1

  • 1Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.

Journal of Sleep Research
|May 3, 2023
PubMed
Summary
This summary is machine-generated.

A new algorithm automatically scores fragmentary myoclonus (FM) during sleep, addressing the time and variability issues of manual scoring. This tool reliably identifies excessive fragmentary myoclonus (EFM) in patients.

Keywords:
computerised methodhypnic myoclonusmuscle activitypolyneuropathy

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Area of Science:

  • Neurology
  • Sleep Medicine
  • Biomedical Engineering

Background:

  • Excessive fragmentary myoclonus (EFM) is a polysomnographic finding requiring specific criteria for diagnosis.
  • Manual scoring of fragmentary myoclonus (FM) is labor-intensive and subject to inter-rater variability.
  • Objective and automated methods are needed for consistent FM and EFM assessment.

Purpose of the Study:

  • To validate an automated algorithm for scoring FM in whole-night polysomnography recordings.
  • To compare the algorithm's performance against manual scoring by an expert.
  • To assess the algorithm's reliability in identifying EFM.

Main Methods:

  • An automated leg movement identification algorithm was adapted to detect FM-like activity.
  • A post-processing step was implemented to filter activity based on amplitude criteria.
  • Leave-one-out cross-validation was used to optimize algorithm parameters.
  • Agreement was assessed using Cohen's kappa and correlation coefficients.

Main Results:

  • The algorithm demonstrated substantial agreement (kappa > 0.62) with human scoring across most sleep stages.
  • Moderate agreement (kappa = 0.58) was observed during wakefulness (W).
  • Correlation coefficients exceeded 0.96 for FM indices across all sleep stages.
  • EFM presence/absence was correctly identified in 80% of subjects.

Conclusions:

  • The developed algorithm provides a reliable method for automatic FM and EFM scoring.
  • This automated approach can improve objectivity and consistency in evaluating FM.
  • Future research will utilize this algorithm for large-scale population studies on FM and EFM.