Quantification of irregular rhythms in chronobiology: a time-series perspective

In optimal conditions of youth and health, most—if not all—physiological systems obey regular circadian rhythms in response to the periodic day-night cycle and can be well described by standard techniques such as cosinor analysis. Adverse conditions can disturb the regularity and amplitude of circadian cycles, and, recently, there is interest in the field of chronobiology to quantify irregularities in the circadian rhythm as a means to track underlying pathologies. Alterations in physiological rhythms over a wide range of frequency scales may give additional information on health conditions but are often not considered in traditional analyses. Wavelets have been introduced to decompose physiological time series in components of different frequencies and can quantify irregular patterns, but the results may depend on the choice of the mother wavelet basis which is arbitrary. An alternative approach are recent data-adaptive time-series decomposition techniques, such as singular spectrum analysis (SSA), where the basis functions are generated by the data itself and are user-independent. In the present contribution, we compare wavelets and SSA analysis for the quantification of irregular rhythms at different frequency scales and discuss their respective advantages and disadvantages for application in chronobiology.