Purpose: Heart Rate Variability (HRV) and Skin Conductance (SC) are two important parameters that reflect the response of the Autonomic Nervous System (ANS). In this research paper, the effect of postural change on HRV and SC has been explored. Sample entropy (SE) is one of the non-linear methods which is used to analyze HRV and SC. Conventional SE is sensitive to small signal length data and a single distance function between matched vector pairs is used in it, which made the analysis inconclusive to establish the relation between HRV and SC.
Method: This paper proposes a composite distance sample entropy method for matched vector pairs to remove the sensitivity of small-signal length data. In the proposed method, the distance between the two template vectors has been calculated using the composite distance function. To validate the superiority of the proposed method different noise signals of varied length is simulated for sample entropy calculation.
Results: A data set of 70 subjects for HRV and SC has been recorded in the position of supine and standing for this purpose. Comparative analysis has been done between the proposed and conventional methods on the selfrecorded data set using accuracy, and paired t-test,
Conclusion: The proposed method gives higher accuracy than the conventional SE method and appears to be less sensitive to the length of the signal and establishes the relation between HRV and SC in different body postures.