title: Classification of Implantable Rotary Blood Pump States With Class Noise.
abstract: A medical case study related to implantable rotary blood pumps is examined. Five classifiers and two ensemble classifiers are applied to process the signals collected from the pumps for the identification of the aortic valve nonopening pump state. In addition to the noise-free datasets, up to 40% class noise has been added to the signals to evaluate the classification performance when mislabeling is present in the classifier training set. In order to ensure a reliable diagnostic model for the identification of the pump states, classifications performed with and without class noise are evaluated. The multilayer perceptron emerged as the best performing classifier for pump state detection due to its high accuracy as well as robustness against class noise.
authors.forename: Hui-Lee
authors.initials: HL
authors.lastname: Ooi
authors.forename: Manjeevan
authors.initials: M
authors.lastname: Seera
authors.forename: Siew-Cheok
authors.initials: SC
authors.lastname: Ng
authors.forename: Chee Peng
authors.initials: CP
authors.lastname: Lim
authors.forename: Chu Kiong
authors.initials: CK
authors.lastname: Loo
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Redmond
authors.forename: Einly
authors.initials: E
authors.lastname: Lim
mesh_terms: D000818:Animals; D004285:Dogs; D006353:Heart-Assist Devices / Q000145:classification*; D018056:Hemorheology; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted