Deep learning has been the answer to many machine learning problems during the past two
decades. However, it comes with two significant constraints: dependency on extensive …

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 …

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 …

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

Deep learning has become popular for automatic sleep stage scoring due to its capability to
extract useful features from raw signals. Most of the existing models, however, have been …

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 …

Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal
dependency in the sleep data. Yet, exploring this long-term dependency when develo** …

Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a
preliminary step of this examination, sleep stages are systematically determined. In practice …

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of
skilled clinicians. Deep learning approaches have been introduced in order to challenge the …