Date of Award

Spring 2008

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Biomedical Engineering

First Advisor

Steven A. Jones

Abstract

Heart rate varies continuously depending on the amount of activity being performed or the emotional state of an individual. Both branches of the autonomic nervous system work to alter heart rate depending on the needs of the body. While healthy individuals are capable of altering their heart rate, individuals with certain types of heart disease do not have this ability. For these individuals, cardiac pacemakers are used to alter heart rate. Cardiac pacemakers use sensors to determine the pacing frequency for the heart; however, there is no current optimum sensor. In order to discover a better sensor, this study investigated the use of parasympathetic motor activity via the vagus nerve to predict heart rate.

Vagus nerve activity and EKG signals were recorded simultaneously; two types of recordings were taken: baseline and altered heart rate recordings achieved by performing bi-lateral carotid artery occlusion. Whole vagus nerve discharges were recorded using small silicone cuff electrodes with platinum contacts. Neural activity and EKG signals obtained from these experiments were filtered for frequency content. After filtering, the vagus motor signal was calculated by using a cross correlation technique introduced by Heetderks. The vagus motor activity was integrated between successive R waves taken from the recorded EKG and correlated with instantaneous heart rate. Consistent, high inverse correlations between integrated vagus motor activity and instantaneous heart rate were found in baseline and occlusion recordings. After obtaining consistent correlations between the integrated vagal motor activity and instantaneous heart rate, a transfer function model was developed using time series analysis methods. The transfer function model whose input was integrated vagus motor activity and whose output was heart rate was capable of predicting heart rate within a 95% confidence interval.

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