Polysomnography (PSG) is the reference standard of sleep measurement, but is
burdensome for the participant and labor intensive. Affordable electroencephalography …

Self-supervised learning (SSL) is a challenging task in sleep stage classification (SSC) that
is capable of mining valuable representations from unlabeled data. However, traditional SSL …

Study objectives To develop a non-invasive and practical wearable method for long-term
tracking of infants' sleep. Methods An infant wearable, NAPping PAnts (NAPPA), was …

All-night electroencephalography (EEG) was introduced in the 1960's to study the
physiological characteristics of sleep. While different states of sleep could be discerned from …

Manual sleep stage scoring of polysomnography recordings is an expensive and time-
consuming process, further complicated by inconsistent sleep stage agreement among …

Research on slow-wave sleep (SWS) began almost a century ago, not long after the
discovery of electroencephalography. From maintaining homeostasis to memory function …

Sleep research uses electroencephalography (EEG) to infer brain activity in health and
disease. Beyond standard sleep scoring, there is increased interest in advanced EEG …

Analyzing electroencephalographic (EEG) time series can be challenging, especially with
deep neural networks, due to the large variability among human subjects and often small …

Sleep is vital for people's physical and mental health, and sound sleep can help them focus
on daily activities. Therefore, a sleep study that includes sleep patterns and disorders is …

Digital therapeutics, enabled by advanced machine learning algorithms and medical
wearable devices, offer a promising approach to streamline diagnostics and improve access …