Analysis of wrist pulse signals using nonlinear dynamical synchronization measure

Wrist pulse signals contain information about the status of health of a person and hence diagnosis using these signals is of great importance since long time. In this paper the efficacy of signal processing techniques in extracting useful information from wrist pulse signals has been demonstrated by using signals recorded before lunch and after lunch conditions. We have used correlation between probabilities of recurrence which is a recent nonlinear measure of synchronization, as well as Pearson's correlation coefficient, which is a traditionally used measure of synchronization. Statistical analysis using two-sample t-test is performed, which shows that there is significantly different synchronizations between signals acquired before lunch and those acquired after lunch. Moreover, upon performing RCE-SVM classification, the nonlinear synchronization measure showed a classification accuracy of 91.6% against the accuracy of 80.5% for correlation. These results show that the new nonlinear synchronization measure is effective in distinguishing the changes taking place after having lunch. This paper also demonstrates the ability of the wrist pulse signals in detecting changes occurring under two different conditions. This study is significant given that analysis of wrist pulse signals in case of food intake is less explored in literature and also that it has potential applications in healthcare.