Recursive model identification for the analysis of the autonomic response to exercise testing in Brugada syndrome

Abstract

This paper proposes the integration and analysis of a closed-loop model of the baroreflex and cardiovascular systems, focused on a time-varying estimation of the autonomic modulation of heart rate in Brugada syndrome (BS), during exercise and subsequent recovery. Patient-specific models of 44 BS patients at different levels of risk (symptomatic and asymptomatic) were identified through a recursive evolutionary algorithm. After parameter identification, a close match between experimental and simulated signals (mean error = 0.81%) was observed. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge, enabling to observe the expected autonomic changes induced by exercise testing. In particular, symptomatic patients presented a significantly higher parasympathetic activity during exercise, and an autonomic imbalance was observed in these patients at peak effort and during post-exercise recovery. A higher vagal modulation during exercise, as well as an increased parasympathetic activity at peak effort and a decreased vagal contribution during post-exercise recovery could be related with symptoms and, thus, with a worse prognosis in BS. This work proposes the first evaluation of the sympathetic and parasympathetic responses to exercise testing in patients suffering from BS, through the recursive identification of computational models; highlighting important trends of clinical relevance that provide new insights into the underlying autonomic mechanisms regulating the cardiovascular system in BS.

Model-based estimation of left ventricular pressure and myocardial work in aortic stenosis

“…The main contributions of this study concern: i) the proposition of an integrated model of cardiovascular system model, ii) the analysis of this cardiovascular model in order to select the most sensitive parameters to be identified in a patient-specific manner, iii) a parameter identification approach able to reproduce LV pressure specifically to each patient and iv) the experimental validation of the proposed method through a cross-validation technique applied on 12 AS patients, in order to quantitatively evaluate GCW, GWW and GWE indices. The heart valve model, proposed by [22], was coupled to a CVS model that includes representations of cardiac electrical activity, cardiac cavities and the circulation, developed by our group [16][17][18][19][20][21]. The integrated model is able to predict the influence of valve motion on hemodynamics in both normal and stenosis cases.…”

“…Four main sub-models were created and coupled: i) cardiac electrical system, ii) elastancebased cardiac cavities, iii) systemic and pulmonary circulations and iv) heart valves. The first three submodels are strongly based on our previous works [16][17][18][19][20][21]. The model of the heart valves was adapted from [22].…”

“…Except for {X LV , X art }, model parameter values were selected from the publications from which each model was originally based on: ventricular and circulatory parameters were taken from [16][17][18]23], heart valve parameters were adapted from [22], and cardiac electrical conduction system from [19].…”

“…Previous works [14,15] has already shown that lumped-parameter models of ventricular-vascular coupling are able to provide a good agreement between the estimated and the measured left ventricular and aortic pressure waveforms. Based on these papers and previous works of our team [16][17][18], we proposed a model-based approach, including a multiformalism model of the cardiovascular system and a parameters identification strategy using a Monte-Carlo cross-validation method, in order to: 1) estimate LV pressure waveform from experimental LV pressure curve, systolic and diastolic aortic pressure values, 2) assess LV pressure waveform from only systolic and diastolic aortic pressure values.The second objective of the paper was to propose a novel tool to estimate myocardial work in AS. Work indices, as proposed in [9, 10] and validated in [12], were calculated from noninvasive model-based LV pressure and compared with indices evaluated from experimental signals.…”

Recursive model identification for the analysis of cardiovascular autonomic modulation during epileptic seizures

“…The proposed computational model is focused on efferent nervous activity and is based on a widely used approach [8][9][10]. Both sympathetic and vagal branches are modeled by first-order filters, characterized by gains (K v , K s ) and time constants (T v , T s ) (Fig.…”