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  • Classifications of Atrial Enlargement Using Neural Networks

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    57614_1.pdf (93.16Kb)
    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
    Metadata
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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

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