Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice
Huijun Yue1, Zhuqi Chen1, Wenbin Guo1
1Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
Sleep Medicine Reviews
|February 2, 2024
Summary
Deep learning significantly enhances automated sleep stage classification accuracy and efficiency. This review explores deep learning methods, applications in sleep disorders, and future directions for intelligent sleep staging.
Area of Science:
- Sleep Medicine
- Artificial Intelligence
- Biomedical Engineering
Background:
- Traditional sleep stage classification methods face challenges in widespread acceptance and efficiency.
- Artificial intelligence (AI), especially deep learning (DL), shows promise for trusted automated sleep-staging systems.
- Integrating automated systems into clinical practice and daily life requires robust and accurate methods.
Purpose of the Study:
- To comprehensively review the latest deep learning methods for improving sleep staging efficiency and accuracy.
- To elucidate the current landscape of deep learning in sleep staging, from data requirements to model performance.
- To discuss the applications, challenges, and future prospects of intelligent sleep staging.
Main Methods:
- Review of recent literature on deep learning applications in sleep staging.
- Analysis of fundamental modeling processes: signal selection, data pre-processing, model architecture, classification tasks, and performance metrics.
- Examination of automated sleep staging applications in sleep disorder screening, diagnostics, and health management.
Main Results:
- Deep learning methods offer significant advancements in sleep staging efficiency and accuracy compared to traditional approaches.
- A comprehensive overview of the deep learning pipeline for sleep staging, including data considerations and evaluation metrics, is presented.
- Current applications demonstrate the potential of AI in clinical sleep analysis and personal health monitoring.
Conclusions:
- Deep learning is a key technology for advancing automated sleep staging, fostering clinical trust and wider adoption.
- Future developments require focus on large-scale datasets, interdisciplinary collaboration, and improved human-computer interaction for intelligent sleep staging.
- AI-driven sleep staging holds potential for improved diagnosis, management of sleep disorders, and personalized health monitoring.
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