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A Gaussian Mixture Model to detect suction events in rotary blood pumps

Authors :
Libera Fresiello
Dimitrios I. Fotiadis
Alexandros T. Tzallas
Maria Giovanna Trivella
Yorgos Goletsis
Markos G. Tsipouras
George Rigas
Evaggelos C. Karvounis
Krzysztof Zieliński
Source :
BIBE
Publication Year :
2012
Publisher :
IEEE, 2012.

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.

Details

Database :
OpenAIRE
Journal :
2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)
Accession number :
edsair.doi...........03b22b0e4b34605c67512c055347b4a6