Medical Engineering & Physics
Volume 28, Issue 4 , Pages 372-378 , May 2006

The comparison of different feed forward neural network architectures for ECG signal diagnosis

  • H. Gholam Hosseini

      Affiliations

    • Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New Zealand Private Bag 92006, Auckland 1020, New Zealand
    • School of Informatics and Engineering, Flinders University, Adelaide, Australia
    • Tel.: +64 9 917 9999x8755, fax: +64 9 917 9973.
    • ,
  • D. Luo

      Affiliations

    • Department of Measurement and Control Technology, School of Information Engineering, Guangdong University of Technology, Guang Zhou 510643, China
    • Corresponding Author InformationCorresponding author. Tel.: +86 20 3845 8826.
    • ,
  • K.J. Reynolds

      Affiliations

    • School of Informatics and Engineering, Flinders University, Adelaide, Australia

Received 8 November 2004 ,Revised 22 April 2005 ,Accepted 22 June 2005.

References 

  1. Gholam Hosseini H. Computer-aided diagnosis of cardiac events, Ph.D. thesis, Flinders University, Adelaide, Australia, 2001.
  2. Dokur Z, Olmez T. ECG beat classification by a novel hybrid neural network. Comput Methods Programs Biomed. 2001;66:167–181
  3. Chazal P, Celler B. Selecting a neural network structure for ECG diagnosis. In: Proceedings of the 20th Annual International Conference of IEEE/Engineering in Medicine and Biology Society, 29 October–1 November. Hong Kong. 1998;p. 1422–1425
  4. Han S. Classification of cardiac arrhythmias using Fuzzy ARTMAP, Ph.D. thesis, Florida Inst. of Tech., Melbourne, FL, 1993.
  5. Hu YH, Palreddy S, Tompkins WJ. A patient adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng. 1997;44:891–900
  6. Looney C. Pattern recognition using neural networks. Oxford University Press; 1997;http://www.oup-usa.org
  7. Miller AS, Blott BH, Hames TK. Review of neural network applications in medical imaging and signal processing. Med Biol Eng Comput. 1992;30:449–464
  8. Moody G, Mark R. MIT-BIH Arrhythmia Database Directory, MIT-BIH Database Distribution. Massachusetts Inst. Technology. 1992;
  9. Gholam Hosseini H, Nazeran H, Moran B. ECG compression. Evaluation of FFT, DCT, and WT performance. Aust Phys Eng Sci Med. 1998;21(4):186–192
  10. Lagerholm M, Peterson C, Braccini G, Edenbrandt L, Sörnmo L. Clustering ECG complexes using hermite functions and self-organizing maps. IEEE Trans. Biomed. Eng. 2000;47:838–848
  11. Simon BP, Eswaran C. An ECG classifier designed using modified decision based neural networks. Comput Biomed Res. 1997;30:257–272
  12. Watrous R, Towell G. A patient-adaptive neural network ECG patient monitoring algorithm. Comput Cardiol. 1995;
  13. Celler B, Chazal P. Low computational cost classifiers for ECG diagnosis using neural networks. In: Proceedings of the 20th Annual International Conference of IEEE/Engineering in Medicine and Biology Society, 29 October–1 November. Hong Kong. 1998;p. 1337–1340
  14. Hu YH, Tompkins WJ, Urrusti JL, Afonso VX. Application of artificial neural networks for ECG signal detection and classification. J. Electrocardiol. 1993;26:66–73

PII: S1350-4533(05)00147-5

doi: 10.1016/j.medengphy.2005.06.006

Medical Engineering & Physics
Volume 28, Issue 4 , Pages 372-378 , May 2006

Article Tools

* Image Usage & Resolution