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This study introduces a collaborative sleep scoring system to improve efficiency in diagnosing sleep disorders. The human-computer approach reduces manual scoring time by over 50%, enhancing diagnostic accuracy for polysomnography (PSG) recordings.

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Area of Science:

  • Sleep Medicine
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Visual sleep scoring of overnight polysomnography (PSG) recordings is essential for diagnosing sleep disorders but is time-consuming and subjective.
  • Existing automatic sleep staging methods lack transparency and full understanding of their reliability, often necessitating expert re-scoring.
  • The need for efficient and reliable sleep scoring methods is critical for clinical practice and patient care.

Purpose of the Study:

  • To develop and evaluate a human-computer collaborative system for automatic sleep scoring.
  • To reduce the time and subjectivity associated with manual sleep scoring while maintaining high reliability.
  • To assist experts by focusing their efforts on low-reliability scored epochs.

Main Methods:

  • A rule-based automatic sleep scoring system was developed, adhering to the American Academy of Sleep Medicine (AASM) guidelines.
  • The system analyzes the reliability of each sleep epoch based on physiological patterns and stage transition characteristics.
  • Experts are prompted to re-score only those epochs identified as having low reliability.

Main Results:

  • The collaborative system achieved an average agreement rate of 90.42% with fully manual scorings, with a kappa coefficient of 0.85.
  • Over 50% reduction in manual scoring time was demonstrated.
  • The system showed robustness and applicability for sleep monitoring.

Conclusions:

  • The proposed human-computer collaborative sleep scoring system significantly enhances the efficiency and reliability of sleep disorder diagnosis.
  • This approach effectively reduces expert workload by minimizing the need for extensive manual re-scoring.
  • The system holds potential for integration into clinical and homecare settings for advanced sleep monitoring applications.