Classifications of Atrial Enlargement Using Neural Networks

Diery, A; Abbosh, Y; Thiel, DV; Cutmore, TRH; Rowlands, D

doi:10.1109/ISAPE.2008.4735506

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Author(s)

Diery, A

Abbosh, Y

Thiel, DV

Cutmore, TRH

Rowlands, D

Griffith University Author(s)

Thiel, David V.

Rowlands, David D.

Year published

2008

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Abstract

The aim of this study was to classify using a neural network LAE into mildly, moderately, and severely abnormal from a subject's 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.The aim of this study was to classify using a neural network LAE into mildly, moderately, and severely abnormal from a subject's 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.
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Conference Title

ISAPE 2008 - The 8th International Symposium on Antennas, Propagation and EM Theory Proceedings

DOI

https://doi.org/10.1109/ISAPE.2008.4735506

Copyright Statement

© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Subject

Biomedical engineering not elsewhere classified

Publication URI

http://hdl.handle.net/10072/38464

Collection

Conference outputs