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
Link To Document :
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