Current status and prospects of automatic sleep stages scoring: Review
Maksym Gaiduk1, Ángel Serrano Alarcón2, Ralf Seepold1
1HTWG Konstanz - University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany.
Biomedical Engineering Letters
|July 31, 2023
Summary
Automated sleep stage scoring using EEG/EEG+EOG+EMG signals shows high accuracy (over 90%). While advancements have plateaued, these methods offer objective sleep analysis potential for medical professionals.
Area of Science:
- Biomedical Engineering
- Sleep Medicine
- Artificial Intelligence
Background:
- Manual sleep stage scoring is resource-intensive and subjective.
- Automated sleep analysis offers significant advantages in efficiency and objectivity.
- Recent advancements focus on improving automated sleep scoring techniques.
Purpose of the Study:
- To provide a comprehensive overview of automated sleep stage scoring approaches.
- To summarize the characteristics of recent automated sleep scoring methods.
- To analyze the quality and trends in automated sleep stage scoring.
Main Methods:
- Systematic review of articles published between 2018 and 2022.
- Inclusion of 125 articles from an initial pool of 515 publications.
- Analysis of automated scoring quality using metrics like Cohen's kappa and accuracy.
Main Results:
- Automated scoring of EEG/EEG+EOG+EMG signals achieves high quality (Cohen's kappa > 0.80, accuracy > 90%).
- No significant breakthroughs in scoring quality using these traditional signals in recent years.
- Systems using alternative signals (respiratory, cardiac, movement) show innovation potential but face reliability challenges.
Conclusions:
- Automated sleep stage scoring demonstrates high reliability and objectivity.
- Traditional polysomnography signals yield consistently high-quality automated scoring.
- Further research into alternative biosignals is needed for more convenient and reliable automated sleep analysis.
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