Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6513
Title: Detecting variabilities of ECG signals by Lyapunov exponents
Authors: Übeyli, Elif Derya
Keywords: Electrocardiogram signals
Chaotic signal
Lyapunov exponents
Multilayer perceptron neural network
Training algorithms
Levenberg-Marquardt algorithm
Issue Date: 2009
Publisher: Springer
Abstract: An approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for automated diagnosis of electrocardiographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of variabilities of ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified. The computed Lyapunov exponents of the ECG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg-Marquardt algorithms. The performances of the MLPNN classifiers were evaluated in terms of classification accuracies. The results confirmed that the MLPNN trained with the Levenberg-Marquardt algorithm has potential in detecting the variabilities of the ECG signals (total classification accuracy was 95.00%).
URI: https://doi.org/10.1007/s00521-008-0229-8
https://hdl.handle.net/20.500.11851/6513
ISSN: 0941-0643
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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