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Deciphering Insomnia: Benchmarking Automated Sleep Staging Algorithms for Complex Sleep Disorders.

Umaer Hanif1,2, Anis Aloulou1,3, Flynn Crosbie1

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Automated sleep staging algorithms show variable performance in chronic insomnia patients. GSSC and U-Sleep demonstrated the best overall accuracy and minimal bias, making them leading tools for sleep disorder diagnosis.

Keywords:
automated sleep stagingchronic insomniamachine learningpolysomnography

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Area of Science:

  • Sleep Medicine
  • Computational Neuroscience
  • Medical Informatics

Background:

  • Polysomnography (PSG) is crucial for diagnosing sleep disorders but requires laborious manual interpretation.
  • Automated sleep staging algorithms offer a potential solution to reduce manual workload.
  • The effectiveness of these algorithms in complex conditions like chronic insomnia is not well-established.

Purpose of the Study:

  • To evaluate the performance of five prominent automated sleep staging classifiers (U-Sleep, STAGES, GSSC, Luna, YASA).
  • To assess these classifiers using PSG data from a large cohort of 904 patients with chronic insomnia.
  • To investigate the influence of patient demographics and PSG metrics on classifier performance.

Main Methods:

  • Utilized PSG data from 904 chronic insomnia patients.
  • Assessed five sleep staging algorithms: U-Sleep, STAGES, GSSC, Luna, and YASA.
  • Performance metrics included F1 scores, confusion matrices, and accuracy of predicted sleep metrics (TST, SOL, WASO).
  • Linear regression analyzed the impact of demographics and PSG metrics on performance.

Main Results:

  • GSSC achieved the highest macro F1 score (0.66), followed by U-Sleep (0.62).
  • GSSC and U-Sleep showed minimal demographic bias, outperforming STAGES and Luna.
  • U-Sleep excelled in Total Sleep Time (TST) prediction (R²=0.88), while STAGES and GSSC were accurate for Sleep Onset Latency (SOL) and Wake After Sleep Onset (WASO) respectively.
  • Common misclassifications involved N1, N3, and REM sleep stages across several algorithms.

Conclusions:

  • Automated sleep staging algorithms offer a viable, though variable, approach for analyzing PSG data in chronic insomnia.
  • GSSC and U-Sleep emerge as the most robust and reliable classifiers for this patient population.
  • Further refinement of algorithms is needed to improve accuracy for specific sleep stages and reduce misclassifications.