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