DocumentCode :
3064545
Title :
Feature extraction for analysis of ECG signals
Author :
Ubeyli, Elif Derya
Author_Institution :
TOBB Economics and Technology University, Faculty of Engineering, Department of Electrical and Electronics Engineering, 06530 Sö¿ÿtözÿ, Ankara, Turkey
fYear :
2008
fDate :
20-25 Aug. 2008
Firstpage :
1080
Lastpage :
1083
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.
Keywords :
Electrocardiography; Feature extraction; Frequency estimation; Heart; Multiple signal classification; Pattern recognition; Polynomials; Power generation; Signal analysis; Signal to noise ratio; Composite features; Diverse features; Electrocardiogram (ECG) signals; Mixture of experts; Modified mixture of experts; Algorithms; Artificial Intelligence; Diagnosis, Computer-Assisted; Electrocardiography; Humans; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE
Conference_Location :
Vancouver, BC
ISSN :
1557-170X
Print_ISBN :
978-1-4244-1814-5
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2008.4649347
Filename :
4649347
Link To Document :
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