Recurrence quantification analysis as a tool for discrimination among different dynamics classes: The heart rate variability associated to different age groups

Abstract

We propose a classification method based on recurrence quantification analysis (RQA) combined with support vector machines (SVM). This method combines in an effective way various quantitative descriptors to allow a refined discrimination among dynamical non linear systems that presents dynamics which are very similar to each other. To show how effective this methodology is, firstly, based on synthetic data, it is applied on time series generated from the logistic mapwith nearby parameter values and in the chaotic regime. Next, it is applied to human biosignals, namely, heart rate variability (HRV) time series obtained from four groups of individuals (premature newborns, full-term newborns, healthy young adults, and adults with severe coronary disease). Roughly the proposed methodology works as follows: The signals are transformed into recurrence plots (RP) and a set of RQA statistical features (recurrence rate, determinism, averaged and maximal diagonal line lengths, entropy, laminarity, trapping time, and length of longest vertical line) are extracted to form the input vector for a SVM classifier. Results show that the method discriminates groups of different ages with classification accuracy better than 75%. Given that heart rate continuously fluctuates over time and reflects different mechanisms to maintain cardiovascular homeostasis of an individual, the results obtained may allow to draw important information on the autonomic control of circulation in normal and diseased conditions.