Classification of ECG Images Using Probabilistic Neural Network Based on Statistical Feature Analysis

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)

Abstract

Research on the analysis of ECG is mostly used for automating the diagnosis of different cardiac diseases. The ECG waveforms may differ for same patient to such extent that they are unlike each other and at same time alike for different types of beats. Many algorithms have been developed for the classification and detection of the ECG beat. In order to improve the accuracy of the ECG image feature extraction and classification system, the present research work proposes the use of different feature extraction methods. ECG image feature extraction and classification system, uses five feature extraction methods, namely wavelet decomposition, edge detection, gray level histogram, fast fourior transform and mean–variance, and probabilistic neural network classification. The objective of this present research work is to achieve the high accuracy and simplest classifiers related to extract the input features. An ECG image is classified by PNN using various feature extraction. The experimental results shows that wavelet decomposition gives a maximum accuracy compared to other feature extraction methods (Tayel MB, El-Bouridy ME (2008) ECG images classification using artificial neural network based on several feature extraction methods. International conference on computer engineering and system pp 113–115).

Keywords

Wavelet decomposition (WT) Edge detection (ED) Fast fourier transform (FFT) Gray level histogram (GLH) Mean–variance (MV) Probabilistic neural network (PNN). 

References

  1. 1.
    Tayel MB, El-Bouridy ME (2008) ECG images classification using artificial neural network based on several feature extraction methods. International conference on computer engineering and system pp 113–115Google Scholar
  2. 2.
    Tayel MB, El-Bouridy ME (2006) ECG images classification using feature extraction based on wavelet transformation and neural network. 6th International conference on computer engineering and system, pp 105–107Google Scholar
  3. 3.
    Martis RJ, Chakraborty C, Ray AK (2009) An integrated ECG feature extraction scheme using PCA and wavelet transform. Indian conference on digital object identifier, pp 1–4Google Scholar
  4. 4.
    Subashini P, Jansi S (2011) A study on detection of focal cortical dysplasia using MRI brain images. J Comp Appl JCA, vol 4, issue 2, ISSN Number: 0974-1925, pp 23–28Google Scholar
  5. 5.
    Santis AD, Sinisgalli C (1999) A bayesian approach to edge detection in noisy images. IEEE Trans Circ Syst I Fundam Theory Appl 46(6):686–699Google Scholar
  6. 6.
    Wu J, Yin Z, Xiong Y (2007) The fast multilevel fuzzy edge detection of blurry images. IEEE Signal Process Lett 14(5):344–347CrossRefGoogle Scholar
  7. 7.
    Srinivasan GN, Shobha G (2008) Statistical texture analysis. Proceedings of world academy of science, engineering and technology, vol 36Google Scholar
  8. 8.
    Ibrahiem MM, Emary El, Ramakrishnan S (2008) On the application of various probabilistic neural networks in solving different pattern classification problems. World applied sciences journal, pp 772–780Google Scholar
  9. 9.
    Rutkowski L (2004) Adaptive probabilistic neural networks for pattern classification in time-varying environment. IEEE Trans Neural Netw (15) pp 811–827Google Scholar
  10. 10.
    Chen Y-H, Yu S-N (2006) Comparison of different wavelet subband features in the classification of ECG beats using probabilistic neural network. 28th annual international conference on engineering medicine and biology society pp 1398–1401Google Scholar
  11. 11.
    Kim T, Yang HS (2006) A multidimensional histogram equalization by fitting an isotropic gaussian mixture to uniform distribution. International conference on image processing, AtlantaGoogle Scholar
  12. 12.
    Banerjee S, Mitra M (2010) ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform. International conference on systems in medicine and biology, pp 55–60Google Scholar
  13. 13.
    I˜nesta JM, Calera-Rubio J (2002) Robust gray-level histogram gaussian characterization. Caelli T et al (eds) SSPR&SPR 2002, LNCS 2396, pp 833–841Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceAvinashilingam Institute for Home Science and Higher Education for Women UniversityCoimbatoreIndia

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