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 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.