QT-interval adaptation to changes in autonomic balance
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
George C. Verghese and Thomas Heldt.
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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.
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).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.