SEMIAUTOMATIC ANALYSIS OF SLEEP MICROSTRUCTURE PARAMETERS: AROUSAL, CYCLIC ALTERNATING PATTERN AND REM MUSCLE ATONIA.

This thesis project is focused on systems of automatic analysis of sleep parameters and it is composed by two main parts: the first is focused on the process of creation of a software for the analysis of Cyclic Alternating Pattern (CAP) a particular parameter of sleep microstructure and the second part is focused on the use of automatic analysis of muscle activity during sleep. CAP is defined as periodic EEG activity of NREM sleep characterized by sequences of transient electrocortical events, that are distinct from the background electroencephalogram (EEG) activity and occurs at up to 1-minute intervals. CAP represents the microstructure of sleep, and its analysis gives fundamental information that are otherwise neglected with the analysis of sleep macrostructure (sleep staging) alone. CAP is considered a marker for the evaluation of sleep stability and its oscillatory presence is fundamental preservation of sleep stability through the night and in response to arousal stimuli. Analysis of CAP is a very time consuming procedure and it is still used mainly for research purpose rather than in the clinical practice. The development of a software for the analysis of CAP was the main focus of the work in collaboration with Micromed® (an international company for the manufacturing of hardware and software for neurophysiology based in Mogliano Veneto (TV)). During the months spent at Micromed® the PhD student worked with the software programmers and engineers for the creation and validation of the software, individuating all the clinical parameters from guidelines and verifying their correct application and the validity of the results. In the first part of this thesis all the creation process is described in detail. The second part of this thesis is focused on the automatic analysis of muscle EMG tone during both REM and NREM sleep. Muscle tone during sleep gradually diminishes throughout the different sleep stages reaching its minimum with REM muscle atonia. Evaluation of muscle tone during REM sleep is fundamental for the diagnosis of REM sleep Behavior Disorder (RBD) in which there is loss of muscle atonia during REM associated to dream enacting behavior. Muscle activity is measured in polysomnography (PSG) through the recording of different EMG channels. This activity is evaluated almost exclusively during REM sleep using a manual method of visual scoring that require high expertise is highly time consuming. A validated method developed by R. Ferri and co. allows automatic analysis of chin EMG activity through the calculation of Atonia index. Few studies evaluated muscle tone during NREM sleep, and little is known about the neurophysiology of muscle control. Manual methods would be difficult to apply to NREM sleep; the method developed by Ferri is capable to perform an analysis of muscle tone for all sleep stages. RBD is associated to neurodegenerative disorders, synucleinopathies such as Parkinson disease (PD), Multiple System Atrophy (MSA). MSA patients have a more severe loss of atonia during REM sleep compared to PD with RBD. Starting from the fortuitous observation of a prominent facial activity during NREM sleep, we decided to evaluate the facial activity recorded in vPSG in patients with PD, MSA and controls and to evaluate the muscle tone in both REM and NREM sleep using the automatic method for the calculation of atonia index. Our results showed that MSA have a more sustained muscle tone compared to healthy controls in all sleep stages and compared to PD in all NREM stages. Moreover a particular facial expression was noted to be significantly more frequent in MSA compared to PD. This results may help the differential diagnosis between PD and MSA. This is the first study to evaluate muscle tone during all sleep stages using Atonia index and this analysis may open to different perspectives for the understanding of REM behavior disorder and the mechanism underlying the control of muscle tone in NREM sleep