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; received in revised form 22 April 2005; accepted 22 June 2005. published online 23 August 2005.

Abstract 

The electrocardiograms (ECGs) record the electrical activity of the heart and are used to diagnose many heart disorders. This paper proposes a two-stage feed forward neural network for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool that can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. A number of neural network architectures are designed and compared for their ability to classify six different heart conditions. Two network architectures based on one stage and two stage feed forward neural networks are chosen for this investigation. The training and testing ECG signals are obtained from MIT-BIH database. The network inputs are comprised of 12 ECG features and 13 compressed components of each heart beat signal. The performance of the different modules as well as the efficiency of the whole system is presented. Among different architectures, a proposed multi-stage network named NET_BST possesses the highest recognition rate of around 93%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems.

Keywords: Artificial neural network, Two-stage ANN architecture, ECG signal classification, ECG signal diagnosis

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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

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