Abstract: The present disclosure relates to a method for detection of an abnormal sleep pattern based on a dataset of Electrooculography (EOG) signals obtained from a sleeping subject over a time interval, the method comprising the steps of dividing the time interval into a plurality of subintervals, each subinterval preferably corresponding to a sleep epoch, classifying each subinterval in terms of sleep stages, thereby obtaining a temporal sleep stage pattern, wherein a subject having an uncharacteristic temporal distribution of sleep stages is characterized as having an abnormal sleep pattern.

Abstract: The present invention relates to a system and a method for detection of abnormal motor activity during REM sleep, and further to systems and method for assisting in detecting neurodegenerative disorders such as Parkinson's. One embodiment relates to a method for detection of abnormal motor activity during REM sleep comprising the steps of: performing polysomnographic recordings of a sleeping subject, thereby obtaining one or more electromyography (EMG) derivations, preferably surface EMG recordings, and one or more EEG derivations, and/or one or more electrooculargraphy (EOG) derivations, detecting one or more REM sleep stages, preferably based on the one or more EEG and/or EOG derivations, determining the level of muscle activity during the one or more REM sleep stages based on the one or more EMG derivations, wherein a subject having an increased level of muscle activity during REM sleep compared to one or more normal subjects has abnormal motor activity during REM sleep.

Abstract: The present invention relates to the use of sleep spindles as a novel biomarker for early diagnosis of synucleinopathies, in particular Parkinson's disease (PD). The method is based on automatic detection of sleep spindles. The method may be combined with measurements of one or more further biomarkers derived from polysomnographic recordings.