[Crib-list] SPEAKER: DANILO SCEPANOVIC (MIT) - Computational Research in Boston and Beyond Seminar -- Friday, Dec. 3, 2010 -- Room 32-124 -- Time: 12:30 PM

[Shirley Entzminger]

 		   COMPUTATIONAL RESEARCH in BOSTON and BEYOND SEMINAR


DATE:		FRIDAY, DECEMBER 3, 2010
TIME:		12:30 PM
LOCATION:	Building 32, Room 124  (Stata Center)

Pizza and beverages will be provided at 12:10 PM outside Room 32-124.


TITLE:	   MODELING AUTONOMIC REGULATION OF SINO-ATRIAL NODE CELL ACTIVITY


SPEAKER:   DANILO SCEPANOVIC
 	   (Massachusetts Institute of Technology)


ABSTRACT:

The autonomic nervous system (ANS) regulates bodily functions that are not 
under conscious control, such as heart rate, blood pressure, digestion, 
etc.  The ANS integrates information from the body as a whole and its 
activity reflects perturbations caused by various disease processes.  We 
aim to develop a real-time method to noninvasively estimate the activity 
in the two branches of the ANS for use in diagnostics or patient 
monitoring.

The current state of the art for cardiac ANS estimation falls under the 
topic of heart rate variability (HRV) or cardiovascular system 
identification (CSI). HRV and CSI have shown promise for diagnosing and 
tracking the progression of diseases such as hypertension, diabetic 
neuropathy, heart failure, sleep apnea, and others, as well as quantifying 
the consequences of lifestyle changes such as smoking, diet, and exercise. 
An opportunity exists to improve the existing methods to increase the 
time-resolution and provide more easily interpretable results.

To improve the existing ANS estimation methods, we are incorporating more 
physiologic detail into the model of the system, and plan to use this 
model to more thoroughly constrain the estimation problem linking heart 
beat times to ANS tone.  This talk will cover the details of translating 
biological data into a mathematical model of the sino-atrial node cell 
(the pacemaker of the heart), with a focus on the compromises that must be 
made between capturing biological detail and ensuring computational 
feasibility and mathematical clarity.  The model is realized as a system 
of nonlinear ordinary differential equations (ODEs); we also describe a 
preliminary implementation using a numerical ODE integrator in serial 
(ode15s in Matlab) versus parallel (CVode in Star-P).

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Massachusetts Institute of Technology
Cambridge, MA 02139

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