Document Type
Thesis
Date of Award
Spring 5-31-1996
Degree Name
Master of Science in Biomedical Engineering - (M.S.)
Department
Biomedical Engineering Committee
First Advisor
Stanley S. Reisman
Second Advisor
David S. Kristol
Third Advisor
Thomas W. Findley
Abstract
Power spectral analysis of heart rate variability is a powerful tool to measure the activity of the parasympathetic division of the autonomic nervous system noninvasively. To determine the parasympathetic activity, the frequency of respiration must be known. However, during ambulatory studies, the frequency of respiration is not acquired. To alleviate this problem, methods have been proposed in the past to derive the respiration from the ECG. Unfortunately, these previous methods are unreliable if the subject's breathing rate is uncontrolled. In this study, four methods to derive the respiration waveform from the electrocardiogram (ECG) were developed. Two leads of ECG and a measure of respiration were taken from nine healthy subjects during rest, paced breathing, and exercise. To determine the optimum method, the respiration was then derived using all four methods and compared to the measured respiration in the time domain and frequency domain using cross-correlation and coherence, respectively. The results of this study indicate that three of the four methods developed can accurately and reliably derive the respiration during every section of the experimental protocol. In addition, the respiration waveform derived using the variable QRS window, dependent leads method is quantitatively identified as the most accurate.
Recommended Citation
Caggiano, David J., "Comparison of different signal processing algorithms to extract the respiration waveform from the ECG" (1996). Theses. 1066.
https://digitalcommons.njit.edu/theses/1066
