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External validation of a data-driven algorithm for muscular activity identification during sleep.

Matteo Cesari1, Julie A E Christensen1,2, Helge B D Sorensen1

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|May 28, 2019
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Summary

This study validates an automated algorithm for detecting sleep disorders like periodic limb movements during sleep (PLMS) and REM sleep without atonia (RSWA). The algorithm shows promise in identifying these conditions across different clinics, even in Parkinson's disease patients.

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electromyographymotor events during sleeppolysomnography

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Area of Science:

  • Neurology
  • Sleep Medicine
  • Biomedical Engineering

Background:

  • Automated scoring of sleep disorders like periodic limb movements during sleep (PLMS) and REM sleep without atonia (RSWA) is crucial for diagnosis.
  • Existing algorithms are often validated within a single clinical setting, potentially introducing bias.
  • Cross-center validation is necessary to ensure generalizability and reliability of automated sleep scoring methods.

Purpose of the Study:

  • To validate a data-driven algorithm for muscular activity detection during sleep across different clinical centers.
  • To assess the algorithm's performance in identifying participants with elevated PLMS indices and REM sleep behaviour disorder (RBD).
  • To investigate potential inter-clinical differences in manual scoring and their impact on automated algorithm performance.

Main Methods:

  • Validation of a previously developed data-driven algorithm for muscular activity detection during sleep.
  • Utilized a cohort of 240 participants (de novo Parkinson's disease patients and healthy controls) from a German sleep clinic.
  • Compared algorithm performance against manual scoring of sleep data, including PLMS indices and presence of RSWA.

Main Results:

  • The algorithm demonstrated good agreement with manual scoring for PLMS indices in the German cohort.
  • Accurate identification of participants with PLMS index > 15 (88.75%) and patients with RSWA (84.17%).
  • Algorithm performance was slightly reduced in Parkinson's disease patients due to increased motor-related artefacts.

Conclusions:

  • The developed algorithm shows reasonable performance in identifying participants with RBD and increased PLMS index across different recording centers.
  • Cross-center validation highlights potential inter-clinical scoring variations, suggesting a need for standardized automated methods.
  • The algorithm's ability to detect sleep disorders in diverse cohorts supports its potential clinical utility and aids in understanding scoring discrepancies.