Key result
A method utilizing principal component analysis and singular value decomposition optimizes an auto-associative neural network's complexity and initial weights for detecting ectopic beats.
Population
Electrocardiogram data for ectopic beat detection
Design
Other
Authors
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May enhance research ECG analysis; leaves open prospective clinical validation before practice adoption.
This paper describes a computational method to optimize auto-associative neural networks for the detection of ectopic beats in electrocardiograms.
Clifford et al. (2001) studied this question. A method utilizing principal component analysis and singular value decomposition optimizes an auto-associative neural network's complexity and initial weights for detecting ectopic beats.
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