Résumé:
Artificial neuralnetworkshavebeensuccessfullyappliedtoawiderangeofproblems.Inpat-
tern recognition,theyhavebeenusedinseveraltasks,suchasfeatureextraction,dimension
reduction, andclassification.Inthiswork,weproposetwoECGheartbeatclassificationmodels
based oncollaboratingdifferenttypesofneuralnetworks.Themainaimistocombinetheir
complementaryproperties.
The firstmodelusesastackedsparseautoencoder(SSAE)asfeatureextractorandasystem
of multipleMulti-layeredperceptrons(MLP)asaclassifier.Inthismodel,theentireproblem
is dividedintosimplerparts,whichareresolvedusingdifferentMLPs.Thesecondmodelalso
uses aSSAEtoextractfeaturesinadditiontotwootherdynamicfeatures.Inthismodel,the
classification isperformedbyahybridneuralmodelbasedoncombiningrandomandRBFneural
networks.
The proposedmodelsareevaluatedontheMIT-BIHarrhythmiadataset.Thetestsarebasedon
the inter-patientparadigm,inwhichthetrainingandtestdataaretakenfromdifferentpatients.
The obtainedresultsarecomparedwithsomeofthestate-of-the-artmethods.
