Title

A Suction Detection System For Rotary Blood Pumps Based On The Lagrangian Support Vector Machine Algorithm

Keywords

Lagrangian support vector machine (LSVM); Left ventricular assist device (LVAD); Suction detection

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 flowsignal tomake a decision aboutwhether 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. © 2013 IEEE.

Publication Date

10-14-2013

Publication Title

IEEE Journal of Biomedical and Health Informatics

Volume

17

Issue

3

Number of Pages

654-663

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TITB.2012.2228877

Socpus ID

84885126712 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84885126712

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