• 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