Document Type
Thesis
Date of Award
Spring 5-31-1987
Degree Name
Master of Science in Electrical Engineering - (M.S.)
Department
Electrical Engineering
First Advisor
Stanley S. Reisman
Second Advisor
Walter N. Tapp
Third Advisor
Jacob Klapper
Abstract
Time series signal processing techniques (Fourier based) were applied to analyze the relationship between heart rate and respiration. This analysis takes advantage of the different timing charecteristics of the autonomic nervous system inputs to analyze their influence on the heart rate.
Data from three experiments were analyzed. The first set of data was from helicopter pilots of the US Army. They injected atropine into themselves on certain days. The effect of atropine on the heart rate spectrum and the relationship between heart rate and respiration was studied. It was found that atropine abolishes the respiration peak in the heart rate spectrum indicating that the respiration peak is vagally mediated. Also the respiration spectrum could be used to locate the respiration peak in the heart rate spectrum.
An experiment was then carried out to see how stable the respiration peak was in the heart rate spectrum. Five subjects were used in this study. It was found that the respiration peak is quite stable from day to day.
The effect of paced breathing and stress on the heart rate spectrum was then studied. An experiment was designed for this and data was collected from eight subjects. It was found that there was a strong negative correlation between frequency of respiraton and the magnitude of the respiration peak. Further studies need to be done to learn more about this.
Spectral analysis of heart rate variability and respiration is a quantitative measure of autonomic activity. It is hoped that, by understanding the various components in the heart rate spectrum, a non-invasive means of detecting heart defects could be achieved.
Recommended Citation
Iyengar, Anuradha Ramaswamy, "Time series analysis of respiration component in heart rate variability" (1987). Theses. 1382.
https://digitalcommons.njit.edu/theses/1382
