DocumentCode
3133178
Title
Automated Non-invasive Detection of Pumping States in an Implantable Rotary Blood Pump
Author
Karantonis, Dean M. ; Cloherty, Shaun L. ; Mason, David G. ; Salamonsen, Robert F. ; Ayre, Peter J. ; Lovell, Nigel H.
Author_Institution
Graduate Sch. of Biomed. Eng., New South Wales Univ., Sydney, NSW
fYear
2006
fDate
Aug. 30 2006-Sept. 3 2006
Firstpage
5386
Lastpage
5389
Abstract
With respect to rotary blood pumps used as left ventricular assist devices (LVADs), it is clinically important to control pump flow to avoid complications associated with over-or under-pumping of the native heart. By employing only the non-invasive observer of instantaneous pump impeller speed to assess flow dynamics, a number of physiologically significant pumping states may be detected. Based on a number of acute animal experiments, five such states were identified: regurgitant pump flow (PR), ventricular ejection (VE), non-opening of the aortic valve (ANO), and partial collapse (intermittent and continuous) of the ventricle wall (PVC-I and PVC-C). Two broader states, normal (corresponding to VE, ANO) and suction (corresponding to PVC-I, PVC-C) were readily discernable in clinical data from human patients implanted with LVADs. Based on data from both the animal experiments (N=6) and the human patients (N=10), a strategy for the automated non-invasive detection of significant pumping states has been developed and validated. Employing a classification and regression tree (CART), this system detects pumping states with a high degree of accuracy: state VE -87.5/100.0% (sensitivity/specificity); state ANO - 98.1/92.5%; state PVC-I - 90.0/90.2%; state PVC-C - 61.2/98.0%. With a simplified binary scheme differentiating suction and normal states, both states were detected without error in data from the animal experiments, and with a sensitivity/specificity, for detecting suction, of 99.2/98.3% in the human patient data
Keywords
cardiology; decision trees; haemodynamics; impellers; medical control systems; prosthetics; pumps; regression analysis; aortic valve nonopening; automated noninvasive detection; classification-and-regression tree; flow dynamics; heart pumping states; implantable rotary blood pump; left ventricular assist devices; pump flow control; pump impeller speed; regurgitant pump flow; ventricle wall partial collapse; ventricular ejection; Animals; Blood; Classification tree analysis; Heart; Humans; Impellers; Regression tree analysis; Signal analysis; State feedback; Valves;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location
New York, NY
ISSN
1557-170X
Print_ISBN
1-4244-0032-5
Electronic_ISBN
1557-170X
Type
conf
DOI
10.1109/IEMBS.2006.259725
Filename
4463021
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