NDLI: Automated Non-invasive Detection of Pumping States in an Implantable Rotary Blood Pump

Content Provider	IEEE Xplore Digital Library
Author	Karantonis, D.M. Cloherty, S.L. Mason, D.G. Salamonsen, R.F. Ayre, P.J. Lovell, N.H.
Copyright Year	2006
Description	Author affiliation: Graduate Sch. of Biomed. Eng., New South Wales Univ., Sydney, NSW (Karantonis, D.M.; Cloherty, S.L.)
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
Sponsorship	IEEE EMB
Starting Page	5386
Ending Page	5389
File Size	239288
Page Count	4
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.259725
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Blood Animals Humans Heart Signal analysis Impellers Valves Classification tree analysis Regression tree analysis State feedback
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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