Traditional sleep scoring based on visual inspection of Electroencephalogram (EEG) signals
is onerous for sleep scorers because of the gargantuan volume of data that have to be …

The classification of sleep stages is the first and an important step in the quantitative
analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual …

In this paper, we compare and analyze the results from automatic analysis and visual
scoring of nocturnal sleep recordings. The validation is based on a sleep recording set of 60 …

The categorization of sleep stages helps to diagnose different sleep-related ailments. In this
paper, an entropy-based information–theoretic approach is introduced for the automated …

Convolutional neural networks (CNN) have demonstrated state-of-the-art classification
results in image categorization, but have received comparatively little attention for …

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

The classification of sleep stages is an important process. However, this process is time-
consuming, subjective, and error-prone. Many automated classification methods use …

Electroencephalogram (EEG) is able to indicate states of mental activity ranging from
concentrated cognitive efforts to sleepiness. Such mental activity can be reflected by EEG …

Sleeping problems have become one of the major diseases all over the world. To tackle this
issue, the basic tool used by specialists is the polysomnogram, which is a collection of …

Background Sleep and sleep quality assessment by means of sleep stage analysis is
important for both scientific and clinical applications. Unfortunately, the presently preferred …