2026-09-19T15:40:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/848652022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
George C. Verghese and Thomas Heldt.
Nosakhare, Ehimwenma
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-02-10T16:56:09Z
2014-02-10T16:56:09Z
2013
http://hdl.handle.net/1721.1/84865
868327113
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 93-98).
ECG variability, as it relates to the influence of the autonomic nervous system on the heart, is primarily studied via frequency-domain and time-domain analysis of heart rate variability (HRV). HRV studies the variability of the RR intervals in the ECG; these intervals are modulated by the autonomic influence on the periodicity of the the heart's pacemaker, the sino-atrial node. The autonomic influence at this level is dominated by the parasympathetic nervous system. In order to have a robust assessment of autonomic balance, there is a need for an ECG-based approach to assess the influence of the sympathetic nervous system. In this thesis, using spectral analysis, we quantify the variability of the QT interval, which is primarily modulated by the sympathetic nervous system. We also estimate the time constant of the sympathetic nervous system by least-squares fitting of the QT time series resulting from step perturbations in autonomic balance. This study is carried out on graded head-up tilt test data. Our results demonstrate the potential of QT interval variability as a non-invasive assessment of the sympathetic nervous system activity on the heart.
by Ehimwenma Nosakhare.
S.M.
98 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
QT-interval adaptation to changes in autonomic balance
Thesis
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<Abstract>ECG variability, as it relates to the influence of the autonomic nervous system on the heart, is primarily studied via frequency-domain and time-domain analysis of heart rate variability (HRV). HRV studies the variability of the RR intervals in the ECG; these intervals are modulated by the autonomic influence on the periodicity of the the heart's pacemaker, the sino-atrial node. The autonomic influence at this level is dominated by the parasympathetic nervous system. In order to have a robust assessment of autonomic balance, there is a need for an ECG-based approach to assess the influence of the sympathetic nervous system. In this thesis, using spectral analysis, we quantify the variability of the QT interval, which is primarily modulated by the sympathetic nervous system. We also estimate the time constant of the sympathetic nervous system by least-squares fitting of the QT time series resulting from step perturbations in autonomic balance. This study is carried out on graded head-up tilt test data. Our results demonstrate the potential of QT interval variability as a non-invasive assessment of the sympathetic nervous system activity on the heart.</Abstract>
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