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| Research article summary (published 9 Dec 2008): |
Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.
Full Abstract
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals.
Author information
Author/s: Ubeyli, Elif Derya (ED);
Affiliation: Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Universitesi, 06530 Sögütözü, Ankara, Turkey. edubeyli(-atsign-)etu.edu.tr
Journal and publication information
Publication Type: Journal Article
Journal: Computer methods and programs in biomedicine (Comput Methods Programs Biomed), published in Ireland. (Language: eng)
Reference: 2009-Mar; vol 93 (issue 3) : pp 313-21
Dates: Created 2009/02/09; Completed 2009/03/31;
PMID: 19084286, status: MEDLINE (last retrieval date: 3/31/2009, IMS Date: 31 Mar 2009 00:00:00)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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