This study presents an automatic sleep-stage classification system based on utilizing
compressive sensing (CS) for data reduction. The amount of electroencephalogram (EEG) …

The development of automatic sleep based abnormality detection in patient for sleep related
problem is a key field in the recent research. However the sleep signals are obtained as …

This paper presents a recurrent neural classifier for automatically classifying sleep stages
based on energy features from the EEG signal of the Fpz− Cz channel. The energy features …

Sleep-stages play important roles in the diagnosis of the sleep disorders and the sleep-
related illnesses. In this sense, accurate identification of the sleep-stages is a necessity for …

Sleep is associated with a variety of chronic diseases as well as most psychiatric, addiction
and mood disorders. To analyze sleep patterns in rodents, researchers analyze …

This paper proposes a practical approach to addressing limitations posed by using of single-
channel electroencephalography (EEG) for sleep stage classification. EEG-based …

The efficient compression and classification of medical signals, particularly
electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body …

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 …

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 …

The division of sleep into different stages using EEG signals is a commonplace practice in
sleep laboratories and an indispensable tool for clinicians and researchers. Despite the …