Actigraphy is a method for monitoring the movements of the nondominant arm, and the technology has found applications ranging from clinical devices to smart wristbands. Time series obtained from actigraphy data is used in chronobiology to define the sleep-wake cycle, as well as in sleep medicine to evaluate an individual’s sleep quality. In the study described in this paper, an algorithm based on recurrence quantification analysis (RQA) was applied to a time series obtained from a commercial actigraph, which was used to collect raw data alongside polysomnography (PSG), generally considered as the gold standard for assessing sleep quality. The central hypothesis is that transitions between sleep and wakefulness are not purely random events, but are strongly influenced by two internal processes: the homeostatic pressure and the circadian cycle. On the basis of this premise, application of RQA to time series as an estimator of this system should lead to improved results and allow more reliable investigations than a purely empirical approach. To compare the results from the RQA algorithm and those from PSG, we present a detailed statistical analysis involving a bias evaluation of the two methods following an approach suggested by Bland and Altman, a comparison of data processed using the kappa coefficient, and a comparison of consolidated sleep quality data using the p-value.