Articles via Databases
Articles via Journals
Online Catalog
E-books
Research & Information Literacy
Interlibrary loan
Theses & Dissertations
Collections
Policies
Services
About / Contact Us
Administration
Littman Architecture Library
This site will be removed in January 2019, please change your bookmarks.
This page will redirect to https://digitalcommons.njit.edu/dissertations/822 in 5 seconds

The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title: Central and peripheral autonomic influences : analysis of cardio-pulmonary dynamics using novel wavelet statistical methods
Author: Petrock, Anne Marie
View Online: njit-etd2007-053
(xix, 143 pages ~ 14.7 MB pdf)
Department: Department of Biomedical Engineering
Degree: Doctor of Philosophy
Program: Biomedical Engineering
Document Type: Dissertation
Advisory Committee: Reisman, Stanley S. (Committee chair)
Rockland, Ronald H. (Committee member)
Foulds, Richard A. (Committee member)
Tavantzis, John (Committee member)
DeMeersman, Ronald (Committee member)
Bartels, Matthew N. (Committee member)
Date: 2007-05
Keywords: Autonomic
Heart rate variability
Cardio-pulmonary oscillations
Coif 3 wavelet
Wavelet source separation
Wavelet entropy
Availability: Unrestricted
Abstract:

The development and implementation of novel signal processing techniques, particularly with regard to applications in the clinical environment, is critical to bringing computer-aided diagnoses of disease to reality. One of the most confounding factors in the field of cardiac autonomic response (CAR) research is the influence of the coupling of respiratory oscillations with cardiac oscillations.

This research had three objectives. The first was the assessment of central autonomic influence over heart rate oscillations when the pulmonary system is damaged. The second was to assess the link between peripheral and central autonomic control schema by evaluating the heart rate variability (HRV) of people who were able or unable to adapt to the use of integrated lenses for vision, specifically acconrrmodation, correction (adaptive and non-adaptive presbyopes). The third objective was the development of a wavelet-based toolset by which the first two objectives could be achieved. The first tool is a wavelet based entropy measure that quantifies the level of information by assessing not only the entropy levels, but also the distribution of the entropy across frequency bands. The second tool is a wavelet source separation (WayS) method used to separate the respiratory component from the cardiac component, thereby allowing for analysis of the dynamics of the cardiac signal without the confounding influence of the respiratory signal that occurs when the body is perturbed.

With regard to hypothesis one, the entropy method was used to separate the COPD study populations with 93% classification accuracy at rest, and with 100% accuracy during exercise. Changes in COPD and control autonomic markers were evident after respiration is removed. Specifically, the LF/HF ratio slightly decreased on average from pre to post reconstruction for controls, increased on average for COPD. In healthy controls, respiration frequency is distributed across multiple bandwidths, causing large decreases in both LF and HF when removed. With respiration effect removed from COPD population, LE dominates autonomic response, indicating that the frequency is concentrated in the HF autonomic region. Decrease in variance of data set increases probability tat smaller changes can be detected in values.

The theory set forth in hypothesis two was validated by the quantification of a correlation between peripheral and central autonomic influences, as evidenced by differences in oculomotor adaptability correlating with differences in HRV. Standard Deviation varies with grouping, not with age. Increasing controlled respiration frequencies resulted in adaptive presbyopes and controls displaying similar sympathetic responses, diverging from non-adaptive group. WayS reduced frequency content in ranges concurrent with breathing rate, indicating a robust analysis.

The outcome of hypothesis three was the confirmation that wavelet statistical methods possess significant potential for applications in HRV. Entropy can be used in conjunction with cluster analysis to classify patient populations with high accuracy. Using the WayS analysis, the respiration effect can be removed from HRV data sets, providing new insights into autonomic alterations, both central and peripheral, in disease.


If you have any questions please contact the ETD Team, libetd@njit.edu.

 
ETD Information
Digital Commons @ NJIT
Theses and DIssertations
ETD Policies & Procedures
ETD FAQ's
ETD home

Request a Scan
NDLTD

NJIT's ETD project was given an ACRL/NJ Technology Innovation Honorable Mention Award in spring 2003