A non-parametric model-free analysis of actigraphic recordings of acute insomnia patients

Both parametric and non-parametric approaches to time series analysis have
advantages and disadvantages. Parametric methods, although powerful and
widely used, can yield inconsistent results due to the oversimplification
of the observed phenomena, they require the setting of arbitrary constants
for their creation and refinement, and, although these constants relate to
assumptions about the observed systems, it can lead to erroneous results
when treating a very complex problem with a sizable list of unknowns.
Their non-parametric counterparts, instead, are more widely applicable but
present a higher detrimental sensitivity to noise and low density in the
data. For the case of approximately periodic phenomena, such as human
actigraphic time series, parametric methods are widely used and concepts
such as acrophase are staple in chronobiology; in this work we present a
non-parametric method of analysis of actigraphic time series from
insomniac patients and healthy age-matched controls, the method is fully
data-driven, reproduces previous results in the context of sleep-onset
delay and, crucially, extends the concept of acrophase not only to
circadian but also for ultradian spectral components.