DocumentCode
2166957
Title
Classification of atrial enlargement using neural networks
Author
Diery, A. ; Abbosh, Y. ; Thiel, D.V. ; Cutmore, T.R.H. ; Rowlands, D.
Author_Institution
Griffith Sch. of Eng., Griffith Univ., Brisbane, QLD
fYear
2008
fDate
2-5 Nov. 2008
Firstpage
1462
Lastpage
1465
Abstract
The aim of this study was to classify using a neural network LAE into mildly, moderately, and severely abnormal from a subjectpsilas P-wave. Cardiological features, wavelet features, and a combination of both were used to train the neural networks. It was found features derived from the wavelet energy spectrum performed better than the cardiological features on the test cases.
Keywords
cardiology; medical computing; neural nets; wavelet transforms; cardiological feature; left atrial enlargement classification; neural network; wavelet energy spectrum; wavelet feature; Cardiology; Electrocardiography; Feature extraction; Morphology; Neural networks; Pattern matching; Performance evaluation; Testing; Ultrasonic imaging; Wavelet analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Antennas, Propagation and EM Theory, 2008. ISAPE 2008. 8th International Symposium on
Conference_Location
Kunming
Print_ISBN
978-1-4244-2192-3
Electronic_ISBN
978-1-4244-2193-0
Type
conf
DOI
10.1109/ISAPE.2008.4735506
Filename
4735506
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