Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making
and revolutionizing the field of health care. A major open challenge that AI will need to …

Modern deep learning holds a great potential to transform clinical studies of human sleep.
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …

The clinical and societal measurement of human sleep has increased exponentially in
recent years. However, unlike other fields of medical analysis that have become highly …

Study Objectives: We evaluated the interrater reliabilities of manual polysomnography sleep
stage scoring. We included all studies that employed Rechtschaffen and Kales rules or …

Background: Despite recent significant progress in the development of automatic sleep
staging methods, building a good model still remains a big challenge for sleep studies with a …

Deep learning is widely used in the most recent automatic sleep scoring algorithms. Its
popularity stems from its excellent performance and from its ability to process raw signals …

Analyzing polysomnography (PSG) is an effective method for evaluating sleep health;
however, the sleep stage scoring required for PSG analysis is a time-consuming effort for an …

Background The past few years have seen a rapid emergence of artificial intelligence (AI)-
enabled technology in the field of sleep medicine. AI refers to the capability of computer …

Automatic deep-learning models used for sleep scoring in children with obstructive sleep
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …

Sleep assessment depends both on the subjective experience of the individual and
objective measurements, which are traditionally collected through an overnight sleep study …