期刊
DIGITAL SIGNAL PROCESSING
卷 19, 期 2, 页码 320-329出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.dsp.2008.09.002
关键词
Recurrent neural networks; Levenberg-Marquardt algorithm; Eigenvector methods; Electrocardiogram (ECG) beats
The purpose of this study is to evaluate the accuracy of the recurrent neural networks (RNNs) trained with Levenberg-Marquardt algorithm on the electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the RNN trained on the extracted features. The RNNs were implemented for classification of the ECG beats using the statistical features as inputs. The ability of designed and trained Elman RNNs, combined with eigenvector methods, were explored to classify the ECG beats. The classification results demonstrated that the combined eigenvector methods/RNN approach can be useful in analyzing the ECG beats. (C) 2008 Elsevier Inc. All rights reserved.
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