Obstructive sleep apnea (OSA) is thought to affect almost 1 billion people worldwide. OSA
has well established cardiovascular and neurocognitive sequelae, although the optimal …

Diabetes is the leading cause of severe health complications and one of the top 10 causes
of death worldwide. To date, diabetes has no cure, and therefore, it is necessary to take …

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or
tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD …

Automatic sleep stage mymargin classification is of great importance to measure sleep
quality. In this paper, we propose a novel attention-based deep learning architecture called …

Real-world data often exhibit imbalanced distributions, where certain target values have
significantly fewer observations. Existing techniques for dealing with imbalanced data focus …

Sleep disorders affect a large portion of the global population and are strong predictors of
morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence …

Background: Black-box skepticism is one of the main hindrances impeding deep-learning-
based automatic sleep scoring from being used in clinical environments. Methods: Towards …

Aims Apnoea–hypopnoea index (AHI), the universal clinical metric of sleep apnoea severity,
poorly predicts the adverse outcomes of sleep apnoea, potentially because the AHI, a …

Objective The gold standard for diagnosing sleep disorders is polysomnography, which
generates extensive data about biophysical changes occurring during sleep. We developed …

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve
millions experiencing sleep deprivation and disorders and enable longitudinal sleep …