Simplified recurrence plots approach on heart rate variability data

Recurrence plot analysis has been applied to heart rate variability (HRV) data to identify hidden rhythms and complex dynamical patterns of fluctuations. Since the autonomic nervous system (ANS) is the primary regulating factor of HRV, derived measures as well as structural and qualitative aspects of these plots may be associated to different states or changes in neural control. A procedural feature of recurrence plots requires reconstruction of the dynamics in an equivalent topological space. The unknown dimensionality of HRV dynamics confounds the interpretation of measures, such as percent recurrence, percent determinism or entropy that have been proposed to characterize these plots. We observed that the structural stability manifested by recurrence plots was largely independent of increasing embedding dimension so that faithful characterization was feasible without embedding.