Research Article
ECG Classification based on Sparse Constrained Nonnegative-Matrix Factorization and Decision Tree
@INPROCEEDINGS{10.1109/ChinaCom.2013.6694689, author={yao li and qingning zeng}, title={ECG Classification based on Sparse Constrained Nonnegative-Matrix Factorization and Decision Tree}, proceedings={8th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2013}, month={11}, keywords={sparse decomposition; nonnegative matrix factorization (nmf); eigenvector; electrocardiograph (ecg); classification method}, doi={10.1109/ChinaCom.2013.6694689} }- yao li
qingning zeng
Year: 2013
ECG Classification based on Sparse Constrained Nonnegative-Matrix Factorization and Decision Tree
CHINACOM
IEEE
DOI: 10.1109/ChinaCom.2013.6694689
Abstract
In this paper, several data dimensionality reduction methods are compared. Then an ECG classification method is proposed which, employs the sparse decomposition of Nonnegative Matrix Factorization (SCNMF) for data dimensionality reduction, and Decision Tree for signal classification. The experimental results, in which five common heart diseases in the MIT-BIH database are used, indicate that the overall accuracy by the proposed ECG classification method reaches more than 99%. In addition, the employed data dimensionality reduction method can better retain the useful raw information and can save storage space.
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