The market for smartphones, smartwatches, and wearable devices is booming. In recent
years, individuals and researchers have used these devices as additional tools to monitor …

Traditionally, automatic sleep stage classification is quite a challenging task because of the
difficulty in translating open-textured standards to mathematical models and the limitations of …

To improve applicability of automatic sleep staging an efficient subject-independent method
is proposed with application in sleep–wake detection and in multiclass sleep staging …

Automatic diagnosis of the Sleep Apnea‐Hypopnea Syndrome (SAHS) has become an
important area of research due to the growing interest in the field of sleep medicine and the …

Study objectives Development of inter-database generalizable sleep staging algorithms
represents a challenge due to increased data variability across different datasets. Sharing …

Background and objective Despite numerous deep learning methods being developed for
automatic sleep stage classification, almost all the models need labeled data. However …

This paper presents an automatic sleep stage method combining a sparse deep belief net
and combination of multiple classifiers for electroencephalogram, electrooculogram and …

The study of sleep stages and the associated signals have emerged as a very important
parameter to identify the neurological disorders and test of mental activities nowadays …

Context The currently existing literature about Application Program Interface (API) usability is
heterogeneous in terms of goals, scope, and audience; and its connection to accepted …

Many systems are developed for automatic sleep stage classification. However, nearly all
models are based on handcrafted features. Because of the large feature space, there are so …