Date of Award

Summer 2005

Document Type

Thesis

Degree Name

Master of Science in Biomedical Engineering - (M.S.)

Department

Biomedical Engineering

First Advisor

Stanley S. Reisman

Second Advisor

Ronald H. Rockland

Third Advisor

Tara L. Alvarez

Abstract

Heart rate variability (HRV) has proven to be a useful noninvasive tool to study the neuronal control of the heart. Recently, nonlinear dynamic methods based on chaos theory and fractal dynamics have been developed to uncover the nonlinear fluctuations in heart rate. Approximate Entropy (ApEn), Sample Entropy (SampEn) and Multi-scale Entropy (MSE) are measures based on chaos theory that quantify the regularity in time series. This study has been designed to examine the ability of these measures to distinguish the RR-interval time series of normal subjects (NSR) from subjects with congestive heart failure (CHF). The study was conducted on the RR-interval data of 44 NSR subjects and 18 CHF subjects. In addition to this, entropy measures of three apparently healthy subjects were calculated during sitting, standing, exercise and paced breathing to determine the change in entropy measures during these conditions. The results showed that ApEn and SampEn measures for 1000 RR-intervals were significantly (P < 0.005) higher for the NSR group than the CHF group. However, no significant difference was observed for these measures calculated for 40,000 RRintervals. MSE analysis revealed that the complexity of the RR-interval time series was significantly higher for the NSR group than the CHF group at all scales but one. SampEn was significantly lower during exercise while there was no significant difference in SampEn for other activities. The results reproduced the findings of others. This study suggests a general decrease in entropy in subjects with congestive heart failure.

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