Techniques for analyzing complexity in heart rate and beat-to-beat blood pressure signals

Two techniques for quantifying the complexity of a signal, the approximate entropy and approximate dimension, that are based on ideas from nonlinear dynamics are described. The two transformations are shown to be suitable for characterizing heart rate and blood pressure variability. Because the distinction between noise and chaos ultimately comes down to the complexity of the generating system, each of them can be interpreted as measuring the complexity of the system. For typical conditions encountered in the analysis of heart rate and blood pressure signals-signals of short duration that may not show clear evidence of deterministic dynamics-these techniques are more appropriate than conventional methods for calculating fractal dimensions and Kolmogorov entropy. They provide a robust way of characterizing variability with real heart rate and blood pressure data