A Study of Probabilistic Models for Characterizing Human Heart Beat Dynamics in Autonomic Blockade Control.
In this paper, we compare and validate different probabilistic models of human heart beat intervals for assessment of the electrocardiogram data recorded with varying conditions in posture and pharmacological autonomic blockade. The models are validated using the adaptive point process filtering paradigm and Kolmogorov-Smirnov test. The inverse Gaussian model was found to achieve the overall best performance in the analysis of autonomic control. We further improve the model by incorporating the respiratory covariate measurements and present dynamic respiratory sinus arrhythmia (RSA) analysis. Our results suggest the instantaneous RSA gain computed from our proposed model as a potential index of vagal control dynamics.
Chen
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Neuroscience Statistics Research Lab, Massachusetts General Hospital, Harvard Medical School / Harvard-MIT Division of Health Science and Technology, Boston, MA, USA.
Brown
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Barbieri
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