Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a
human expert, according to official standards. It could appear then a suitable task for modern …

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 …

Automatic sleep staging has been often treated as a simple classification problem that aims
at determining the label of individual target polysomnography epochs one at a time. In this …

Abstract Study Objectives The development of ambulatory technologies capable of
monitoring brain activity during sleep longitudinally is critical for advancing sleep science …

Sensors | Free Full-Text | The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep
Stage Detection Using the Oura Ring Next Article in Journal The 2019–2020 Rise in Lake …

For automatic sleep stage classification, the existing methods mostly rely on hand-crafted
features selected from polysomnographic records. In this paper, the goal is to develop 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 …

The identification of sleep stages is essential in the diagnostics of sleep disorders, among
which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring …

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 …