A Suction Detection System for Rotary Blood Pumps Based on the Lagrangian Support Vector Machine Algorithm

    Authors

    Y. Wang;M. A. Simaan

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    IEEE J. Biomed. Health Inform.

    Keywords

    Lagrangian support vector machine (LSVM); left ventricular assist device; (LVAD); suction detection; VENTRICULAR ASSIST DEVICE; CLASSIFICATION; FEEDBACK; STATES; FLOW; Computer Science, Information Systems; Computer Science, ; Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics

    Abstract

    The left ventricular assist device is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. However, using such a device may result in a very dangerous event, called ventricular suction, that can cause ventricular collapse due to overpumping of blood from the left ventricle when the rotational speed of the pump is high. Therefore, a reliable technique for detecting ventricular suction is crucial. This paper presents a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian support vector machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is in suction, approaching suction, or not in suction. The proposed method has been tested using in vivo experimental data based on two different pumps. The simulation results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, and good robustness compared to three other existing suction detection methods and the original support vector machine (SVM) algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the speed of the pump while at the same time ensuring that suction is avoided.

    Journal Title

    Ieee Journal of Biomedical and Health Informatics

    Volume

    17

    Issue/Number

    3

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    654

    Last Page

    663

    WOS Identifier

    WOS:000321146100017

    ISSN

    2168-2194

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