ECG Beats Extraction and Classification Using Radial Basis Function Neural Networks

  • Mohammed Belkheiri
  • Zineb Douidi
  • Ahmed Belkheiri
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)

Abstract

This paper aims the design of an ECG diagnosis system that helps physicians in the interpretation of ECG signals. This system preprocesses and extracts the ECG beats of an ECG record and some feature extraction techniques are invoked to get a feature vector that represents the main characteristics of the ECG wave. After that a well trained RBF artificial neural network is used as a classifier for four different ECG heart conditions selected from MIT-BIH arrhythmia database. The ECG samples were processed and normalized to produce a set of reduced feature vectors. The results of sensitivity, specificity accuracy and recognition rate of the system are analyzed to find the best RBF neural network for ECG classification. Different ECG feature vectors composed of averaged amplitude values, DCT coefficients, DFT coefficients, and wavelet coefficients were used as inputs to the neural network. Among different feature sets, it was found that an RBF which has one layer and the feature vector 61 inputs, and 20 neurons possessed the best performance with highest recognition rate of 95 % for four cardiac conditions.

Keywords

ECG Classification DWT DCT DFT RBF neural networks Feature extraction 

References

  1. 1.
    Ge D, Xiao Q (2006) Feature extraction based on optimal discrimination plane in ECG signal classification. In: ADMA 2006.Lecture notes on artificial intelligence, vol 4093, pp. 143–149Google Scholar
  2. 2.
    Olmez T, Dokur Z (2003) Application of InP neural network to ECG beat classification. Neural Comput Appl 11:144–155CrossRefGoogle Scholar
  3. 3.
    Acir N (2005) Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm. Neural Comput Appl 14(4):299–309CrossRefGoogle Scholar
  4. 4.
    Faezipour M, Saeed A, Nourani M (2010) Automated ECG profiling and beat classification. In: IEEE international conference on acoustics speech and signal processing, pp 2198–2201Google Scholar
  5. 5.
    Hendel M, Benyettou A, Hendel F, Khelil H (2010) Automatic heartbeats classification based on discrete wavelet transform and on a fusion of probabilistic neural networks. J Appl Sci 15:1554–1562Google Scholar
  6. 6.
    MIT-BIH Arrhythmia Database, Physiobank Archieve. http://www.physionet.org/physiobank/database
  7. 7.
    Korurek M, Dogan B (2010) ECG beat classification using particle swarm optimization and radial basis function neural network. Expert Syst Appl 33:7563–7569CrossRefGoogle Scholar
  8. 8.
    Haykin S (1998) Neural networks: a comprehensive foundation. Prentice Hall, New JerseyGoogle Scholar
  9. 9.
    Karpagachelvi S, Arthanari M, Sivakumar M (2011) Classification of electrocardiogram signals with support vector machines and extreme learning machine. Neural Comput Appl 20:1043–1053Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Mohammed Belkheiri
    • 1
  • Zineb Douidi
    • 1
  • Ahmed Belkheiri
    • 1
  1. 1.Laboratoire de Télécommunications, Signaux et Systèmes, Department of Electronics, Faculty of TechnologieUniversity Amar Telidji of LaghouatLaghouatAlgeria

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