Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6705
Title: Feature Extraction for Analysis of ECG Signals
Authors: Übeyli, Elif Derya
Keywords: Diverse features
Composite features
Electrocardiogram (ECG) signals
Mixture of experts
Modified mixture of experts
Issue Date: 2008
Publisher: IEEE
Source: 30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society -- AUG 20-24, 2008 -- Vancouver, CANADA
Series/Report no.: IEEE Engineering in Medicine and Biology Society Conference Proceedings
Abstract: The automated diagnostic systems employing diverse and composite features for electrocardiogram (ECG) signals were analyzed and their accuracies were determined. Because of the importance of making the right decision, classification procedures classifying the ECG signals with high accuracy were investigated. The classification accuracies of mixture of experts (ME) trained on composite features and modified mixture of experts (MME) trained on diverse features were compared. The inputs of these automated diagnostic systems were composed of diverse or composite features (power levels of the power spectral density estimates obtained by the eigenvector methods) and were chosen according to the network structures. The conclusions of this study demonstrated that the MME trained on diverse features achieved accuracy rates which were higher than that of the ME trained on composite features.
URI: https://doi.org/10.1109/IEMBS.2008.4649347
https://hdl.handle.net/20.500.11851/6705
ISBN: 978-1-4244-1814-5
ISSN: 1557-170X
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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