Sleep Studies

EEG is one of the major tools in sleep research and a valuable aid in diagnosing sleep disorders and other neurological problems. Sleep is a non-uniform biological state that has been divided into several stages. The standard for terminology and scoring of sleep stages is the manual by Rechtschaffen and Kales, which is followed by the vast majority of sleep laboratories, worldwide.

Polysomnography (PSG) is the most important laboratory technique for assessment of sleep and its disorders. It is usually performed over the duration of an entire night, or at least 6.5 hours, in order to investigate normal and disturbed sleep. PSG records multiple physiological characteristics simultaneously during sleep. The inseparable part of polysomnographic recordings are EEG, electromyogram (EMG) and electrooculogram (EOG), but they can also include electrocardiogram (ECG), respiratory effort and respiratory airflow, blood oxygen saturation (SpO2) and temperature, as well as movement or body position.

Normal healthy sleep is organized into sequences of stages that typically cycle every 60-90 minutes. In adults, sleep can be categorized into three states: non-rapid eye movement (NREM), rapid eye movement (REM) and wakefulness. NREM state is divided into four particular stages, reflecting a continuum of lighter to deeper sleep.

Sleep problems belong to the most common serious neurological disorders. Reliable and robust detection of these disorders would improve the quality of life of many people. The aims of automated processing of sleep data are on one side to ease the work of medical doctors and on the other side to make the evaluation more objective.

Our work

The aim is to overcome disadvantages of existing methods and to enhance classification and differentiation of individual states.


Fig. 1. Hypnogram (schematic representation of sleep dynamics)

detectin of artefacts
Fig. 2. Spectral analysis applied to sleep EEG data


PSGLab is Matlab toolbox for processing of polysomnographic (PSG) data. PSGLab implements signal preprocessing, feature extraction, classification, cluster analysis and data visualization methods.

Detailed information about PSGLab:


In our work, we are using real clinical EEG/PSG recordings for which the classification has been known. Data are obtained form the cooperating medical institution - Department of Neurology, Faculty Hospital Na Bulovce in Prague.


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