Extensive experiments have been carried out in this study to classify sleep EEG from three
different standard databases–Sleep EDF, DREAMS and Expanded sleep EDF databases …

This work proposes a method utilizing the fusion of graph-based and temporal features for
sleep stage identification. EEG epochs are transformed into visibility graphs from which …

Study Objectives: Although sleep is frequently disrupted in the pediatric intensive care unit, it
is currently not possible to perform real-time sleep monitoring at the bedside. In this study …

This work attempts to design an effective sleep staging system, making the best use of the
available signals, strategies, and features in the literature. It must not only perform well on …

Using a single EOG channel, sleep-wake states of patients with different sleep disorders are
accurately classified. We used polysomnography data of 27 patients (mixed apnea, periodic …

This work proposes a method utilizing only the submentalis EMG channel for the
classification of sleep and wake states among the healthy individuals and patients with …

T his chapter presents the main motivation, contextualisation, and objectives of the
presented study. Section 1. 1 presents the main motivation behind working towards effective …

Sleep-wake patterns are disrupted in patients with sleep disorders, and automated
identification of sleep and wake states will be extremely useful for the timely diagnosis of …

This thesis develops a machine learning pipeline to automate sleep staging in canines,
focusing on sedation and natural sleep states. Recognizing the essential role of EEG in …

Sleep is not a single uniform state, but rather a cyclical pattern involving multiple stages
such as rapid eye movement (REM) and non-REM (NREM stages N1, N2, and N3) …