NDLI: Modeling Heart Rate Baroreflex Mechanism and its Application in Predicting Acute Hypotensive Episodes

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Jalali, Ali Nataraj, C.
Copyright Year	2011
Abstract	In this paper a new nonlinear system identification approach is developed for identification of the heart rate (HR) baroreflex mechanism. The developed model in then used in the task of acute hypotensive episodes (AHE) prediction. The AHE is defined as any period of 30 min or more during which at least 90% of the mean arterial pressure (MAP) measurements are below 60 mmHg. The proposed HR baroreflex model is based on inherent features of the autonomic nervous system for which we develop an adaptive neuro-fuzzy inference system (ANFIS) structure. The model showed significant improvement in HR prediction accuracy in terms of the normalized root mean square error (NRMSE) in comparison with previously reported results. We achieved a value of 0.191 in mean NRMSE in prediction of HR in this paper which is about 20% better than the best reported result in other papers. For the task of AHE prediction, since arterial pressure has a direct correlation with heart rate, we could simply find the periods in which HR drops blow a certain level without losing generality. The demonstrated AHE data for twenty patients are selected to validate the proposed algorithm. Results show that the proposed method could truly predict occurrence of the AHE in 9 out of the 10 cases analyzed. Results show reliable accuracy in predicting AHE in these patients.
Starting Page	525
Ending Page	529
Page Count	5
File Format	PDF
ISBN	9780791854884
DOI	10.1115/IMECE2011-64015
Volume Number	Volume 2: Biomedical and Biotechnology Engineering; Nanoengineering for Medicine and Biology
Conference Proceedings	ASME 2011 International Mechanical Engineering Congress and Exposition
Language	English
Publisher Date	2011-11-11
Publisher Place	Denver, Colorado, USA
Access Restriction	Subscribed
Subject Keyword	Errors Nervous system Algorithms Modeling Nonlinear systems Pressure
Content Type	Text
Resource Type	Article

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