Journals on Medical Casereports - BJSTR Journal

Abstract

Sleep disorders have a great impact in the patients’ quality of life. The study of human sleep during the different sleep stages is crucial in the diagnosis of sleep disorders and is mainly performed with #polysomnography (PSG). In this work, a methodology for sleep staging using solely Electroencephalographic (EEG) signals from PSG recordings is presented. EEG signals from the #ISRUC-Sleep dataset are selected and used, aiming to automatically identify the five sleep stages. Initially, the EEG signal is filtered in order to extract the five EEG rhythms and the energy is calculated in each sub-band and used to train several typical classifiers. Results in terms of classification accuracy reached 75.29% with Random Forests. Sleep is a fundamental restorative process for human mental and physical health [1]. The nightlong study of human sleep and sleep-related behaviors during the different sleep stages is essential in the diagnosis of sleep disorders. Sleep disturbance and disorders, such as the life-threatening #Sleep Apnea Syndrome (SAS), can have devastating effects on both the quality of life and essential human activities, including learning and memorization. Monitoring the patient throughout the night sleep and identifying the alterations in sleep patterns, plays a significant role in the accurate diagnosis and in the implementation of the appropriate treatment plan. Sleep is a structured sequenced process comprised of five stages, which progress cyclically. The sleep-wake cycle consists of an awake stage, a #non-rapid eye movement stage (NREM), which is further divided into transitional sleep (N1), light sleep (N2) and deep sleep (N3) stages, and then a rapid eye movement sleep stage (REM) [2]. The N3 stage sometimes is considered as two separate stages (N3 and N4) and it is referred as slow wave sleep [3]. Monitoring of these different stages through use of recorded neural, respiratory, and #cardiac activity during sleep, can provide an assessment of sleep in patients suffering from sleep disorders.

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EEG-Based Automatic Sleep Stage Classification by Nikolaos Giannakeas in BJSTR