IEEE Transactions on Biomedical Engineering · 2000 · 548 citations · 17 references
EngineeringBiomedical EngineeringCardiologyBiomedical Signal ProcessingData MiningMachine LearningBiomedical Signal ModelingCardiac Signal ProcessingCluster DetectionElectrocardiogram AnalysisElectrocardiogram InterpretationElectrocardiographyBasis FunctionsCardiac Feature ExtractionEcg Feature ExtractionElectrocardiogram Feature ExtractionElectrocardiogram ModelingHermite FunctionsSelf-organizing MapsSelf-organizing Networks
An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NN's are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method.
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