JultikaUniversity of Oulu repository

Abstract

Our heart rate is varying every time, and the autonomic nervous system maintains this complex control mechanism. Analysis of heart rate variability (HRV) is a useful tool for autonomic nervous system assessment. It can be a useful marker for different cardiac arrhythmias and heart diseases, and its’ clinical relevance is increasing day by day. HRV analysis has an important impact on exercise physiology since it can be a useful marker for stress and recovery. HRV during exercise differs a lot from the normal condition as body movement, exercise intensity, and other factors modulate the HRV. Few recent studies show the effect of running cadence and pedaling frequency on the HRV during treadmill exercise and cycling exercise, respectively. Our research is based on incremental treadmill exercise, and we tried to figure out which part of HRV can be explained by running cadence. We tried to create a polynomial model for HRV, which can predict the future HRV by training the model with appropriate training data and later validate the model with the HRV signal from different running intervals. We observed a significant reduction in the model performance with the increment of running speed. The reduction in model performances validates that the HRV signal is affected most when the running intensity is maximum. We tried to correlate our model residuals with the actual acceleration signal, but due to some complexity, we couldn’t achieve what we have hypothesized.