Using recurrence network approach to quantify nonlinear dynamics of skin blood flow in response to loading pressure.

This paper presents a recurrence network approach to quantify dynamic complexity of skin blood flow oscillations (BFO) in response to loading pressure. This approach consists of three processes, including 1) phase space reconstruction by means of time delay embedding, 2) construction of a recurrence matrix that represents neighboring states in phase space, and 3) consideration of the recurrence matrix as an adjacency matrix representing links in a network and the use of clustering coefficients to characterize phase space properties. By using the Lorenz system and real data, we demonstrate that the global clustering coefficient is robust to the embedding parameters. We applied this approach to study skin BFO at baseline and during loading pressure, a causative factor of skin breakdown. The results showed that global clustering coefficients of BFO significantly decreased in response to loading ( <0.05). Moreover, surrogate tests indicated that such a decrease was associated with a loss of nonlinearity of BFO. Our results suggest that the recurrence network approach can practically quantify the nonlinear dynamics of BFO.