A new method for detection of rhythmic signals in oscillatory systems

Abstract

The study of biological rhythms is receiving a lot of attention in the literature in recent years. At the core of this research lies the methodological problem of how to detect rhythmic signals in measured data. Night and day, or dark and light patterns impact on human health in many different ways. For this reason, researchers are studying the effect of sleep on the circadian clock in human body during various stages of life. Important components of this clock are the circadian genes which have rhythmic expression overtime with phases suitably matching the night and day. Consequently, the identification of rhythmic signals is a problem of considerable interest for biologists. In this work, we develop a novel statistical procedure to detect rhythmic signals in oscillatory systems based on Order Restricted Inference (ORI). This methodology is tested both on simulations and on real data bases. Moreover the obtained results are compared with the most widely extended rhythmicity detection algorithms in literature.