Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice.
Sleep Med Rev
; 74: 101897, 2024 Apr.
Article
de En
| MEDLINE
| ID: mdl-38306788
ABSTRACT
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Intelligence artificielle
/
Apprentissage profond
Type d'étude:
Prognostic_studies
/
Qualitative_research
Limites:
Humans
Langue:
En
Journal:
Sleep Med Rev
Sujet du journal:
MEDICINA
Année:
2024
Type de document:
Article
Pays de publication:
Royaume-Uni