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Performance Analysis of Fuzzy Rough Assisted Classification and Segmentation of Paper ECG Using Mutual Information and Dependency Metric

  • Archana Ratnaparkhi
  • Dattatraya Bormane
  • Rajesh Ghongade
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

The paper aims at the development of fuzzy rough set-based dimensionality reduction for discrimination of electrocardiogram into six classes. ECG acquired by the offline method is in the form of coloured strips. Morphological features are estimated using eigenvalues of Hessian matrix in order to enhance the characteristic points, which are seen as peaks in ECG images. Binarization of the image is carried out using a threshold that maximizes entropy for appropriate extraction of the fiducial features from the background. Various image processing algorithms enhance the image which is utilized for feature extraction. The dataset produced comprises the feature vector consisting of 79 features and 1 decision class for 6 classes of ECG. Extensive analysis of dimensionality reduction has been done to have relevant and nonredundant attributes. Fuzzy rough domain has been explored to take into account the extreme variability and vagueness in the ECG. Optimal feature set is subjected to fuzzification using Gaussian membership function. Further, fuzzy rough set concepts help in defining a consistent rule set to obtain the appropriate decision class. Classification accuracy of unfuzzified dataset is compared with the fuzzified dataset. Semantics of the data are well preserved using fuzzy rough sets and are seen from the performance metrics like accuracy, sensitivity and specificity. The proposed model is named as Fuzzy Rough ECG Image Classifier (FREIC) which can be deployed easily for clinical use as well as experimental use.

Keywords

Fuzzy Fuzzy rough ECG strips Image processing 

References

  1. 1.
    Tan WW, Foo CL, Chua TW (2007) Type-2 fuzzy system for ecg arrhythmic classification. In: IEEE International Fuzzy systems conference, 2007. FUZZ-IEEE 2007. IEEE, pp 1–6Google Scholar
  2. 2.
    Christov II, Daskalov IK (1999) Filtering of electromyogram artifacts from the electrocardiogram. Med Eng Phys 21(10):731–736CrossRefGoogle Scholar
  3. 3.
    Thakor NV, Zhu Y-S (1991) Applications of adaptive filtering to ecg analysis: noise cancellation and arrhythmia detection. IEEE Trans Biomed Eng 38(8):785–794CrossRefGoogle Scholar
  4. 4.
    Laguna P, Jané R, Meste O, Poon PW, Caminal P, Rix H, Thakor NV (1992) Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques. IEEE Trans Biomed Eng 39(10):1032–1044CrossRefGoogle Scholar
  5. 5.
    Zhang Q, Xie Q, Wang G (2016) A survey on rough set theory and its applications. CAAI Trans Intell Technol 1Google Scholar
  6. 6.
    Polkowski L (2002) Topology of rough sets. In Rough Sets. Springer, Berlin, pp 331–360CrossRefGoogle Scholar
  7. 7.
    Duangsoithong R, Windeatt T (2011) Hybrid correlation and causal feature selection for ensemble classifiers. In: Ensembles in machine learning applications. Springer, Berlin, pp 97–115CrossRefGoogle Scholar
  8. 8.
    Chan YT (2012) Wavelet basics. Springer Science & Business MediaGoogle Scholar
  9. 9.
    Ghongade R et al (2007) A brief performance evaluation of ecg feature extraction techniques for artificial neural network based classification. In TENCON 2007-2007 IEEE region 10 conference. IEEE, pp 1–4Google Scholar
  10. 10.
    Martínez JP, Almeida R, Olmos S, Rocha AP, Laguna P (2004) A wavelet-based ecg delineator: evaluation on standard databases. IEEE Trans Biomed Eng 51(4):570–581CrossRefGoogle Scholar
  11. 11.
    Chevalier P et al (2001) Non-invasive testing of acquired long QT syndrome: evidence for multiple arrhythmogenic substrates. Cardiovasc Res 50(2):386–398MathSciNetCrossRefGoogle Scholar
  12. 12.
    Dokur Z, Ölmez T (2001) ECG beat classification by a novel hybrid neural network. Comput Methods Programs Biomed 66(2–3):167–181CrossRefGoogle Scholar
  13. 13.
    Addison PS (2005) Wavelet transforms and the ecg: a review. Physiol Measur 26(5):R155CrossRefGoogle Scholar
  14. 14.
    Mitra S, Mitra M, Chaudhuri BB (2006) A rough-set-based inference engine for ecg classification. IEEE Trans Instrum Measur 55(6):2198–2206CrossRefGoogle Scholar
  15. 15.
    Jensen R, Shen Q (2007) Fuzzy-rough sets assisted attribute selection. IEEE Trans Fuzzy Syst 15(1):73–89CrossRefGoogle Scholar
  16. 16.
    Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17(2–3):191–209CrossRefGoogle Scholar
  17. 17.
    Jensen R, Cornelis C (2011) Fuzzy-rough nearest neighbour classification and prediction. Theoret Comput Sci 412(42):5871–5884MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Archana Ratnaparkhi
    • 1
  • Dattatraya Bormane
    • 2
  • Rajesh Ghongade
    • 3
  1. 1.AISSMS-IOIT, SPPUPuneIndia
  2. 2.AISSMS COE, SPPUPuneIndia
  3. 3.BVCOE, Bharti VidyapeethPuneIndia

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