Compensatory fuzzy neural networks-based intelligent detection of abnormal neonatal cerebral Doppler ultrasound waveforms

Huseyin Seker*, David H. Evans, Nizamettin Aydin, Ertugrul Yazgan

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    32 Citations (SciVal)
    Original languageEnglish
    Pages (from-to)187-194
    Number of pages8
    JournalIEEE Transactions on Information Technology in Biomedicine
    Volume5
    Issue number3
    DOIs
    Publication statusPublished (VoR) - Sept 2001

    Funding

    Manuscript received March 13, 2001. This work was supported by the British Council and The Scientific and Technical Research Council of Turkey. H. Seker is with the Biomedical Computing Research Group, School of Mathematical and Information Sciences, Coventry University, Coventry CV1 5FB, U.K. (e-mail: [email protected]). D. H. Evans is with the Department of Medical Physics, Leicester Royal Infirmary, Leicester LE1 5WW, U.K. (e-mail: [email protected]). N. Aydin is with the Department of Electronics and Electrical Engineering, University of Edinburgh, Edinburgh EH9 3JL, Scotland (e-mail: [email protected]). E. Yazgan is with the Department of Electronics Engineering, Istanbul Technical University, Istanbul, Turkey (e-mail: [email protected]). Publisher Item Identifier S 1089-7771(01)04575-7.

    FundersFunder number
    British Council
    Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

      Keywords

      • Backpropagation learning
      • Blood-flow velocity
      • Doppler ultrasound
      • Fuzzy neural networks
      • Neonatal cerebral arteries
      • Pattern recognition
      • Principal component analysis

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