Author:

Keywords:

Implantable rotary blood pump, Left ventricular assist device, Suction detection, Gaussian mixture model, Science & Technology, Technology, Life Sciences & Biomedicine, Engineering, Biomedical, Medical Informatics, Engineering, ASSIST DEVICES, FLOW, CLASSIFICATION, CONTROLLER, STATES

Abstract:

In this paper, we introduce a new suction detection approach based on online learning of a Gaussian Mixture Model (GMM) with constrained parameters to model the reduction in pump flow signals baseline during suction events. A novel three-step methodology is employed: i) signal windowing, ii) GMM based classification and iii) GMM parameter adaptation. More specifically, the first 5 second segment is used for the parameter initialization and the consequent 1 second windows are classified and used for model adaptation. The proposed approach has been tested in simulation (pump flow) signals and satisfactory results have been obtained. © 2012 IEEE.