Detection of electrocardiographic changes in partial epileptic patients using local binary pattern based composite feature

  • T. Sunil KumarEmail author
  • Vivek Kanhangad
Technical Paper


In this paper, we propose a novel method for detecting electrocardiographic (ECG) changes in partial epileptic patients using a composite feature set. At the core of our approach is a local binary pattern (LBP) based feature representation containing a set of statistical features derived from the distribution of LBPs of the ECG signal. In order to enhance the discriminating power, a set of statistical features are also extracted from the original ECG signal. The composite feature is then generated by combining the two homogeneous feature sets. The discriminating ability of the proposed composite feature is investigated using two different classifiers namely, support vector machine and a bagged ensemble of decision trees. Results from the experimental evaluation on the publicly available MIT-BIH ECG dataset demonstrate the superiority of the proposed features over conventional histogram based LBP features. Our results also show that the proposed approach provides better classification accuracy than methods existing in the literature for classification of normal and partial epileptic beats in ECG.


Local binary pattern Statistical features Electrocardiograph Partial epileptic beat Normal beat 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Scott RA, Lhatoo SD, Sander JWAS (2003) Treatment of epilepsy in developing countries: where do we go from here? Bull World Health Org 79(4):344–351Google Scholar
  2. 2.
    Al-Aweel IC, Krishnamurthy KB, Hausdorff JM, Mietus JE, Ives JR, Blum AS, Schomer DL, Goldberger AL (1999) Postictal heart rate oscillations in partial epilepsy. Neurology 53(7):1590–1592CrossRefPubMedGoogle Scholar
  3. 3.
    Zijlmans M, Flanagan D, Gotman J (2002) Heart rate changes and ECG abnormalities during epileptic seizures: prevalence and definition of an objective clinical sign. Epilepsia 43(8):847–854CrossRefPubMedGoogle Scholar
  4. 4.
    Burgess RC (2003) Automatic seizure detection by ECG analysis. Handb Clin Neurophysiol 3:167–177CrossRefGoogle Scholar
  5. 5.
    Zavar M, Rahati S, Akbarzadeh-TMR, Ghasemifard H (2011) Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection. Expert Syst Appl 38(9):10751–10758CrossRefGoogle Scholar
  6. 6.
    Hitiris N, Suratman S, Kelly K, Stephen LJ, Sills GJ, Brodie MJ (2007) Sudden unexpected death in epilepsy: a search for risk factors. Epilepsy Behav 10(1):138–141CrossRefPubMedGoogle Scholar
  7. 7.
    Varon C, Jansen K, Lagae L, Huffel SV (2015) Can ECG monitoring identify seizures? J Electrocardiol 48(6):1069–1074CrossRefPubMedGoogle Scholar
  8. 8.
    Osorio I, Manly BFJ (2014) Is seizure detection based on EKG clinically relevant? Clin Neurophysiol 125(10):1946–1951CrossRefPubMedGoogle Scholar
  9. 9.
    Osorio I, Manly BFJ (2015) Probability of detection of clinical seizures using heart rate changes. Seizure 30:120–123CrossRefPubMedGoogle Scholar
  10. 10.
    Übeyli ED (2008) Support vector machines for detection of electrocardiographic changes in partial epileptic patients. Eng Appl Artif Intell 21(8):1196–1203CrossRefGoogle Scholar
  11. 11.
    Übeyli ED (2009) Features for analysis of electrocardiographic changes in partial epileptic patients. Expert Syst Appl 36(3):6780–6789CrossRefGoogle Scholar
  12. 12.
    Übeyli ED (2008) Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients. Comput Biol Med 38(3):401–410CrossRefGoogle Scholar
  13. 13.
    Güler I, Übeyli ED (2005) An expert system for detection of electrocardiographic changes in patients with partial epilepsy using wavelet based neural networks. Expert Syst 22(2):62–71CrossRefGoogle Scholar
  14. 14.
    Übeyli ED, Güler I (2004) Detection of electrocardiographic changes in partial epileptic patients using Lyapunov exponents with multilayer perceptron neural networks. Eng Appl Artif Intell 17(6):567–576CrossRefGoogle Scholar
  15. 15.
    Güler I, Übeyli ED (2004) Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction. Expert Syst Appl 27(3):323–330CrossRefGoogle Scholar
  16. 16.
    Cooman T, De, Carrette E, Boon P, Meurs A, Van Huffel S (2014) Online seizure detection in adults with temporal lobe epilepsy using single-lead ECG. Proc Eur IEEE Signal Proc Conf (EUSIPCO). 1532–1536Google Scholar
  17. 17.
    Jeppesen J, Beniczky S, Johansen P, Sidenius P, Fuglsang-Frederiksen A (2015) Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot. Seizure 24:1–7CrossRefPubMedGoogle Scholar
  18. 18.
    Ojala T, Pietikäinen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  19. 19.
    Zhu P, Chatlani N, Soraghan JJ (2012) 1-D local binary patterns based VAD used INHMM-based improved speech recognition. In Proceedings of 20th European IEEE Signal Processing Conference (EUSIPCO), pp 1633–1637Google Scholar
  20. 20.
    Kumar TS, Hussain MA, Kanhangad V (2015) Classification of voiced and non-voiced speech signals using empirical wavelet transform and multi-level local patterns. In Proceedings of 20th IEEE International Conference on Digital Signal Processing (DSP 2015), pp 163–167Google Scholar
  21. 21.
    McCool P, Chatlani N, Petropoulakis L, Soraghan JJ, Menon R, Lakany H (2012) 1D-local binary patterns for onset detection of myoelectric signals. In Proceedings of 20th European IEEE Signal Processing Conference, pp 499–503Google Scholar
  22. 22.
    Kumar TS, Kanhangad V, Pachori RB (2015) Classification of seizure and seizure-free EEG signals using local binary patterns. Biomed Signal Process Control 15:33–40CrossRefGoogle Scholar
  23. 23.
    Kumar TS, Kanhangad V (2017) Automated obstructive sleep apnoea detection using symmetrically-weighted local binary pattern. Electron Lett 53(4):212–214CrossRefGoogle Scholar
  24. 24.
    Kaya Y (2015) Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis. Australas Phys Eng Sci Med 38(3):435–446CrossRefPubMedGoogle Scholar
  25. 25.
    Louis W, Hatzinakos D, Venetsanopoulos A (2014) One dimensional multi-resolution Local Binary Patterns features (1DMRLBP) for regular electrocardiogram (ECG) waveform detection. In Proceedings 19th IEEE Conference on Digital Signal Processing (DSP 2014), pp 601–606Google Scholar
  26. 26.
    Ertuğrul ÖF, Kaya Y, Tekin R, Almalı MN (2016) Detection of Parkinson’s disease by shifted one dimensional local binary patterns from gait. Expert Syst Appl 56:156–163CrossRefGoogle Scholar
  27. 27.
    Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220CrossRefPubMedGoogle Scholar
  28. 28.
    Papoulis A (1991) Probability, random variables and stochastic processes, 3rd edn. McGraw-Hill Companies, BostonGoogle Scholar
  29. 29.
    Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev 5.1 5(1): 3–55CrossRefGoogle Scholar
  30. 30.
    Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  31. 31.
    Chang C-C, Lin C-J (2011) LIBSVM : a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–27CrossRefGoogle Scholar
  32. 32.
    Breiman L (1996) Bagging predictors. Springer Mach Learn 24(2):123–140Google Scholar
  33. 33.
    Freund RJ, Mohr D, Wilson WJ (2010) Statistical methods, 3rd edn. Academic Press, OxfordGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2017

Authors and Affiliations

  1. 1.Discipline of Electrical EngineeringIndian Institute of Technology IndoreIndoreIndia

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