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

[Shirley Entzminger]

T O D A Y . . .

 		   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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