Comparison of seven approaches for holter ECG clustering and classification

In this work we present a comparative study, testing selected methods for clustering and classification of Holter electrocardiogram (ECG). More specifically we focus on the task of discriminating between normal 'N' beats and premature ventricular 'V' beats. Some of the tested met...

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Bibliographic Details
Main Authors: Chudacek, V., Petrik, M., Georgoulas, G., Cepek, M., Lhotska, L., Stylios, C.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Computational complexity
Cybernetics
Decision trees
Electrocardiography
Heart rate variability
Laboratories
Sensitivity and specificity
Spatial databases
Testing
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Summary:In this work we present a comparative study, testing selected methods for clustering and classification of Holter electrocardiogram (ECG). More specifically we focus on the task of discriminating between normal 'N' beats and premature ventricular 'V' beats. Some of the tested methods represent the state of the art in pattern analysis, while others are novel algorithms developed by us. All the algorithms were tested on the same datasets, namely the MIT-BIH and the AHA databases. The results for all the employed methods are compared and evaluated using the measures of sensitivity and specificity.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2007.4353171