2013
DOI: 10.1109/titb.2012.2228877
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A Suction Detection System for Rotary Blood Pumps Based on the Lagrangian Support Vector Machine Algorithm

Abstract: The Left Ventricular Assist Device (LVAD) 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 pa… Show more

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Cited by 27 publications
(14 citation statements)
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“…Integration of suction prevention and physiologic control algorithm(s) for continuous-flow LVAD is in development. 12,13 Centrifugal-flow LVADs produce a higher aortic pressure pulsatility compared with axial-flow LVADs at the same mean LVAD flow rate (Figure 2). However, pressure and flow waveform pulsatility are diminished for all continuous-flow devices.…”
Section: Clinical Significancementioning
confidence: 99%
“…Integration of suction prevention and physiologic control algorithm(s) for continuous-flow LVAD is in development. 12,13 Centrifugal-flow LVADs produce a higher aortic pressure pulsatility compared with axial-flow LVADs at the same mean LVAD flow rate (Figure 2). However, pressure and flow waveform pulsatility are diminished for all continuous-flow devices.…”
Section: Clinical Significancementioning
confidence: 99%
“…The LSVM is a modified standard Support Vector Machine (SVM) [19], which is a reliable and powerful classification technology and has been successfully applied to various pattern recognition problems [20]- [22], especially to the problems in the LVAD field [23], [24]. Like SVM, the main idea of LSVM is to find the optimal separating hyperplane (with the maximum margin) between two different classes of the data points.…”
Section: The Lagrangian Support Vector Machine Approachmentioning
confidence: 99%
“…The classifier is trained on a randomly selected set of 50% of the data and then tested on the remaining 50% of the data. The training and testing procedures are similar to those described in [23], [24]. Due to the random selection of data samples, the classification is repeated 100 times.…”
Section: The Lagrangian Support Vector Machine Approachmentioning
confidence: 99%
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