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Title: Predicting orthostatic vasovagal syncope with signal processing and physiological modelling
Author: Ebden, Mark
ISNI:       0000 0001 3437 7509
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2006
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Orthostatic vasovagal syncope is the sudden loss of consciousness resulting from a temporary impairment of cerebral blood flow, within approximately an hour of standing. Patients who suffer from this problem have "vasovagal syndrome". The purpose of this thesis was to devise a method to detect the syndrome following the assumption of upright position. Data from 106 syncopal patients undergoing head-up tilt table testing (HUT) were acquired, including electrical activity of the heart (electrocardiogram), blood pressure, oxygen saturation, and cerebral perfusion parameters from near-infrared spectroscopy (NIRS). The data set was examined with the aim of generating automatic diagnoses. Comparison of the rate-pressure product (blood pressure multiplied by heart rate) during the time of syncope with a recommended threshold, in addition to comparison with monitoring the fall of systolic blood pressure during prolonged tilt, yielded an 84% accuracy rate for vasovagal syndrome. The thesis reviewed the techniques used on the aforementioned time series by previous researchers, emphasising the concepts underlying "time-frequency analysis", a method for analysing nonstationary signals. Since even healthy patients experience time-varying frequency information in their haemodynamics, a transform known as the Smoothed Pseudo-Wigner Ville Distribution (SPWVD) is well suited to their analysis. This distribution was applied to RR tachograms, plots of heart period against time. After the smoothing parameters of the SPWVD were chosen based on artificial data, the optimised transform was then applied to a second artificial tachogram to calculate the LF/HF (low- to high-frequency) ratio, an indicator of heart rate variability. The computed LF/HF ratio tracked the expected value within an error margin of 3.6%. Finally, by applying the same transform to clinical data, it was proved to offer better resolution than an alternative known as the Lomb periodogram. Classical techniques from the literature predicting vasovagal syncope were found to fail on the current data set: out of 29 tests, only two yielded statistically significant differences between the two patient groups. These were compared with the author's time-frequency analysis of RR tachograms, linear regression of heart rate, and examination of NIRS oscillations and changes on tilt. Of these, the ICFV during time period P3 was found to perform best (negative predictive value: 0.86). A linear classifier was used to combine the best four predictors; it achieved an overall accuracy of 0.88. Following the data-driven approach, an analytical modelling approach was undertaken. In order to define an appropriate model that traded off simplicity with comprehensiveness, the mechanisms of vasovagal syncope were reviewed. A model of orthostasis was developed, validated, and used toward parameter estimation from patient data. Three parameters (baroreceptor operating point, cardiac effectiveness, and baroreflex gain) were gleaned from the supine baseline recording to "normalise" the model for a given patient, before four new parameters (sympathetic and parasympathetic gains at the sino-atrial node, peripheral vasoconstriction gain, and total blood volume) were estimated from the data collected in the upright position. The expectation was that this approach would improve feature extraction (and hence prediction accuracy) as well as the clinical interpretation of the results. However, the modelling approach was found to offer no significant improvement upon the data-driven signal processing results: a linear classifier on the four post-tilt parameters yielded a negative predictive value of just 0.69. This result may have been due to inaccuracies in the time series data owing to instrumentation error. It is also possible that the modelling approach was not able to provide the quality of feature extraction necessary for predicting vasovagal syncope in the elderly. Finally, methods to predict syncope during mid- to late HUT were examined. Using information derived from heart rate and baroreflex sensitivity, a technique was developed to ease patient comfort by terminating the test approximately 2 minutes before syncope was expected to occur.
Supervisor: Tarassenko, Lionel Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Cerebral circulation ; Hypotension, Orthostatic ; Signal processing