An effective approach to classify epileptic EEG signal using local neighbor gradient pattern transformation methods

  • N. J. Sairamya
  • S. Thomas GeorgeEmail author
  • R. Balakrishnan
  • M. S. P. Subathra
Technical Paper


Electroencephalographic (EEG) signal records the neuronal activity in the brain and it is used in the diagnosis of epileptic seizure activities. Human inspection of non-stationary EEG signal for diagnosing epilepsy is cumbersome, time-consuming and inaccurate. In this paper an effective automatic approach to detect epilepsy using two feature extraction techniques namely local neighbor gradient pattern (LNGP) and symmetrically weighted local neighbor gradient pattern (SWLNGP) are proposed. Extracted features are fed into machine learning algorithms like k-nearest neighbor (k-NN), quadratic linear discriminant analysis, support vector machine, ensemble classifier and artificial neural network (ANN) to classify the EEG signals. In this study, the classification performance for 17 different cases using 10-fold cross validation with the following classification problems are executed (i) healthy-ictal, (ii) interictal-ictal, (iii) healthy-interictal, (iv) seizure free-ictal and (v) healthy-interictal-ictal. The experimental result shows that in all the cases LNGP and SWLNGP attained higher classification accuracy using ANN. Further, the computational performance and the classification accuracy of the proposed methods are compared with the recently proposed techniques for epileptic detection. It shows that the performance of LNGP and SWLNGP method with ANN classifier are superior over other recently proposed techniques for the aforesaid problems. Hence, the proposed methods are simple, fast, reliable and easily implementable for real-time epileptic detection.


Local neighbor gradient pattern (LNGP) Symmetrically weighted local neighbor gradient pattern (SWLNGP) Electroencephalographic (EEG) Epileptic detection Artificial neural network (ANN) 



This study was funded by the Department of Science and Technology (TSDP), Ministry of Science and Technology, Government of India [Grant Numbers DST/TSG/ICT/2015/54-G, 2015].

Compliance with ethical standards

Conflict of interest

Corresponding author has received research grants from Department of Science and Technology (TSDP), Ministry of Science and Technology, Government of India.

Ethical approval

For this type of study formal consent is not required.


  1. 1.
    Ghosh-Dastidar S, Adeli H, Dadmehr N (2008) Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans Biomed Eng 55(2):512–518CrossRefGoogle Scholar
  2. 2.
    Joshi V, Pachori RB, Vijesh A (2014) Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed Signal Process Control 9:1–5CrossRefGoogle Scholar
  3. 3.
    Altunay S, Telatar Z, Erogul O (2010) Epileptic EEG detection using the linear prediction error energy. Expert Syst Appl 37(8):5661–5665CrossRefGoogle Scholar
  4. 4.
    Murro AM, King DW, Smith JR, Gallagher BB, Flanigin HF, Meador K (1991) Computerized seizure detection of complex partial seizures. Electroencephalogr Clin Neurophysiol 79(4):330–333CrossRefGoogle Scholar
  5. 5.
    Polat K, Güneş S (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 187(2):1017–1026Google Scholar
  6. 6.
    Faust O, Acharya UR, Min LC, Sputh BH (2010) Automatic identification of epileptic and background EEG signals using frequency domain parameters. Int J Neural Syst 20(02):159–176CrossRefGoogle Scholar
  7. 7.
    Srinivasan V, Eswaran C, Sriraam AN (2005) Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Syst 29(6):647–660CrossRefGoogle Scholar
  8. 8.
    Tzallas AT, Tsipouras MG, Fotiadis DI (2009) Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Trans Inf Technol Biomed 13(5):703–710CrossRefGoogle Scholar
  9. 9.
    Faust O, Acharya UR, Adeli H, Adeli A (2015) Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26:56–64CrossRefGoogle Scholar
  10. 10.
    Peker M, Sen B, Delen D (2016) A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE J Biomed Health Inform 20(1):108–118CrossRefGoogle Scholar
  11. 11.
    Tao Z, Wan-Zhong C, Ming-Yang L (2016) Automatic seizure detection of electroencephalogram signals based on frequency slice wavelet transform and support vector machine. Acta Physica Sinica 65(3):1550040Google Scholar
  12. 12.
    Zhang T, Chen W, Li M (2018) Fuzzy distribution entropy and its application in automated seizure detection technique. Biomed Signal Process Control 39:360–377CrossRefGoogle Scholar
  13. 13.
    Guo L, Rivero D, Pazos A (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193(1):156–163CrossRefGoogle Scholar
  14. 14.
    Kalbkhani H, Shayesteh MG (2017) Stockwell transform for epileptic seizure detection from EEG signals. Biomed Signal Process Control 38:108–118CrossRefGoogle Scholar
  15. 15.
    Pachori RB, Patidar S (2014) Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput Methods Prog Biomed 113(2):494–502CrossRefGoogle Scholar
  16. 16.
    Riaz F, Hassan A, Rehman S, Niazi IK, Dremstrup K (2016) EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans Neural Syst Rehab Eng 24(1):28–35CrossRefGoogle Scholar
  17. 17.
    Güler NF, Übeyli ED, Güler I (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst Appl 29(3):506–514CrossRefGoogle Scholar
  18. 18.
    Niknazar M, Mousavi SR, Vahdat BV, Sayyah BM (2013) A new framework based on recurrence quantification analysis for epileptic seizure detection. IEEE J Biomed Health Inf 17(3):572–578CrossRefGoogle Scholar
  19. 19.
    Ghosh-Dastidar S, Adeli H, Dadmehr N (2007) Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans Biomed Eng 54(9):1545–1551CrossRefGoogle Scholar
  20. 20.
    Nicolaou N, Georgiou J Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl 39(1):202–209CrossRefGoogle Scholar
  21. 21.
    Song JL, Hu W, Zhang R (2016) Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine. Neurocomputing 175:383–391CrossRefGoogle Scholar
  22. 22.
    Kumar Y, Dewal ML, Anand RS (2014) Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. SIViP 8(7):1323–1334CrossRefGoogle Scholar
  23. 23.
    Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh, JE (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl-Based Syst 88:85–96CrossRefGoogle Scholar
  24. 24.
    Tao Z, Wan-Zhong C, Ming-Yang L (2015) Recognition of epilepsy electroencephalography based on Ada Boost algorithm. Acta Physica Sinica 64(12):128701Google Scholar
  25. 25.
    Mohammadpoory Z, Nasrolahzadeh M, Haddadnia J (2017) Epileptic seizure detection in EEGs signals based on the weighted visibility graph entropy. Seizure 50:202–208CrossRefGoogle Scholar
  26. 26.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Tran Pattern Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  27. 27.
    Chatlani N, Soraghan JJ (2010) Local binary patterns for 1-D signal processing in: 18th European Signal Processing Conference (EUSIPCO-2010), pp. 95–99Google Scholar
  28. 28.
    Kumar TS, Kanhangad V (2017) Automated obstructive sleep apnoea detection using symmetrically weighted local binary patterns. Electron Lett 53(4):212–214CrossRefGoogle Scholar
  29. 29.
    Kaya Y, Uyar M, Tekin R, Yıldırım S (2014) 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl Math Comput 243:209–219Google Scholar
  30. 30.
    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
  31. 31.
    Tiwari AK, Pachori RB, Kanhangad V, Panigrahi BK (2017) Automated diagnosis of epilepsy using key-point-based local binary pattern of EEG signals. IEEE J Biomed Health Inf 21(4):888–896CrossRefGoogle Scholar
  32. 32.
    Jaiswal AK, Banka H (2017) Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed Signal Process Control 34:81–92CrossRefGoogle Scholar
  33. 33.
    Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E 64(6):061907CrossRefGoogle Scholar
  34. 34.
    Sharmila A, Geethanjali P (2016) DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers. IEEE Access 4:7716–7727CrossRefGoogle Scholar
  35. 35.
    Jun B, Kim D (2012) Robust face detection using local gradient patterns and evidence accumulation. Pattern Recogn 45(9):3304–3316CrossRefGoogle Scholar
  36. 36.
    Sharma M, Pachori RB, Acharya UR (2017) A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn Lett 94:172–179CrossRefGoogle Scholar
  37. 37.
    Hassan AR, Subasi A (2016) Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput Methods Prog Biomed 136:65–77CrossRefGoogle Scholar
  38. 38.
    Swami P, Gandhi TK, Panigrahi BK, Tripathi M, Anand S (2016) A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst Appl 56:116–130CrossRefGoogle Scholar
  39. 39.
    Tawfik NS, Youssef SM, Kholief M (2016) A hybrid automated detection of epileptic seizures in EEG records. Comput Electron Eng 53:177–190CrossRefGoogle Scholar
  40. 40.
    Kumar Y, Dewal ML, Anand RS (2014) Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 133:271–279CrossRefGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2018

Authors and Affiliations

  • N. J. Sairamya
    • 1
  • S. Thomas George
    • 1
    Email author
  • R. Balakrishnan
    • 2
  • M. S. P. Subathra
    • 1
  1. 1.Department of Electrical SciencesKarunya Institute of Technology and SciencesCoimbatoreIndia
  2. 2.Department of NeurologyPSG Institute of Medical Sciences and ResearchCoimbatoreIndia

Personalised recommendations