http://iet.metastore.ingenta.com
1887

Detection of epileptic seizure employing a novel set of features extracted from multifractal spectrum of electroencephalogram signals

Detection of epileptic seizure employing a novel set of features extracted from multifractal spectrum of electroencephalogram signals

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Here, a technique for automated detection of epilepsy is proposed, based on a novel set of features derived from the multifractal spectrum of electroencephalogram (EEG) signals. In fractal geometry, multifractal detrended fluctuation analysis (MDFA) is a technique to examine the self-similarity of a non-linear, chaotic and noisy time series. EEG signals which are representatives of complex human brain dynamics can be effectively characterised by MDFA. Here, EEG signals representing healthy, interictal and seizure activities are acquired from an available dataset. The acquired signals are at first analysed using MDFA. Based on the multifractal analysis, 14 novel features are proposed in this study, to distinguish between different types of EEG signals. The statistical significance of the selected features is evaluated using Kruskal–Wallis test and is finally served as input feature vector to a support vector machines classifier for the classification of EEG signals. Four classification problems are presented in this work and it is observed that 100% classification accuracy is obtained for three problems which validate the efficacy of the proposed model for computer-aided diagnosis of epilepsy.

References

    1. 1)
      • 1. Acharya, U.R., Sree, S.V., Chattopadhyay, S., et al: ‘Application of recurrence quantification analysis for the automated identification of epileptic EEG signals’, Int. J. Neural. Syst., 2011, 21, pp. 199211.
    2. 2)
      • 2. Sharma, M., Dhere, A., Pachori, R.B., et al: ‘An automatic detection of focal EEG signals using new class of time-frequency localized orthogonal wavelet filter banks’, Knowl Base Syst., 2017, 118, pp. 217227.
    3. 3)
      • 3. Chandaka, S., Chatterjee, A., Munshi, S.: ‘Cross-correlation aided support vector machine classifier for classification of EEG signals’, Expert Syst. Appl., 2009, 36, (2), pp. 13291336.
    4. 4)
      • 4. Polat, K., Günes, S.: ‘Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform’, Appl. Math. Comput., 2007, 187, (2), pp. 10171026.
    5. 5)
      • 5. Gandhi, T., Panigrahi, B.K., Anand, S.: ‘A comparative study of wavelet families for EEG signal classification’, Neurocomputing, 2011, 74, (17), pp. 30513057.
    6. 6)
      • 6. Chatterjee, S., Ray Choudhury, N., Bose, R.: ‘Detection of epileptic seizure and seizure-free EEG signals employing generalized S-transform’, IET Sci. Meas. Technol., 2017, 11, (7), pp. 847855.
    7. 7)
      • 7. Bhati, D., Pachori, R.B., Gadre, V.M.: ‘A novel approach for time-frequency localization of scaling functions and design of three-band biorthogonal linear phase wavelet filter banks’, Digit. Signal Process., October 2017, 69, pp. 309322.
    8. 8)
      • 8. Bhati, D., Sharma, M., Pachori, R.B., et al: ‘Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification’, Digit. Signal Process., March 2017, 62, pp. 259273.
    9. 9)
      • 9. Sharma, R.R., Pachori, R.B.: ‘Time-frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals’, IET Sci. Meas. Technol., January 2018, 12, (01), pp. 7282.
    10. 10)
      • 10. Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: ‘Automatic seizure detection based on time-frequency analysis and artificial neural networks’, Comput. Intell. Neurosci., 2007, 2007, (80510), pp. 113.
    11. 11)
      • 11. Pachori, R.B.: ‘Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition’, Res. Lett. Signal Pr., December 2008, 2008, (293056), pp. 15.
    12. 12)
      • 12. Pachori, R.B., Bajaj, V.: ‘Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition’, Comput. Meth. Prog. Bio., December 2011, 104, (3), pp. 373381.
    13. 13)
      • 13. Bajaj, V., Pachori, R.B.: ‘Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals’, Biomed. Eng. Lett., March 2013, 3, (1), pp. 1721.
    14. 14)
      • 14. Pachori, R.B., Patidar, S.: ‘Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions’, Comput. Meth. Prog. Bio., February 2014, 113, (2), pp. 494502.
    15. 15)
      • 15. Bhattacharyya, A., Singh, L., Pachori, R.B.: ‘Fourier-Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals’, Digit. Signal Pr., July 2018, 78, pp. 185196.
    16. 16)
      • 16. Bhattacharyya, A., Singh, L., Pachori, R.B.: ‘Fourier-Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals’, Digit Signal Pr., July 2018, 78, pp. 185196.
    17. 17)
      • 17. Kumar, T.S., Kanhangad, V., Pachori, R.B.: ‘Classification of seizure and seizure-free EEG signals using local binary patterns’, Bio. Signal Pr. Co., January 2015, 15, pp. 3340.
    18. 18)
      • 18. Kaya, Y., Uyar, M., Tekin, R., et al: ‘1D-local binary pattern based feature extraction for classification of epileptic EEG signals’, Appl. Math. Comput., 2014, 243, pp. 209219.
    19. 19)
      • 19. Nicolaou, N., Georgiou, J.: ‘Detection of epileptic electroencephalogram based on permutation entropy and support vector machines’, Expert Syst. Appl., 2012, 39, (1), pp. 202209.
    20. 20)
      • 20. Sharma, M., Pachori, R.B., Acharya, U.R.: ‘A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension’, Pattern Recogn. Lett., July 2017, 94, pp. 172179.
    21. 21)
      • 21. Sharma, M., Pachori, R.B.A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension’, J. Mech. Med. Biol., October 2017, 17, (4), pp. 1740003, pp. 121.
    22. 22)
      • 22. Peng, C.K., Havlin, S., Stanley, H.E., et al: ‘Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series’, Chaos An Int. J. Nonlin. Sci., 1995, 5, (1), pp. 8287.
    23. 23)
      • 23. de Moura, E.P., Vieira, A.P., Irmao, M.A.S., et al: ‘Applications of detrended-fluctuation analysis to gearbox fault diagnosis’, Mech. Syst. Signal Pr., 2009, 23, pp. 682689.
    24. 24)
      • 24. de Moura, E.P., Souto, C.R., Silva, A.A., et al: ‘Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses’, Mech. Syst. Signal Pr., 2011, 25, pp. 17651772.
    25. 25)
      • 25. Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., et al: ‘Multifractal detrended fluctuation analysis of nonstationary time series’, Physica A Stat. Mech. Appl., 2002, 316, (1), pp. 87114.
    26. 26)
      • 26. Tang, J., Wang, D., Fan, L., et al: ‘Feature parameters extraction of GIS partial discharge signal with multifractal detrended fluctuation analysis’, IEEE Trans. Dielectr. Electr. Insul., 2015, 22, (5), pp. 30373045.
    27. 27)
      • 27. Chatterjee, S., Pratiher, S., Bose, R.: ‘Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non focal EEG signals’, IET Sci. Meas Technol., 2017, 11, (8), pp. 10141021.
    28. 28)
      • 28. Dutta, S., Ghosh, D., Samanta, S., et al: ‘Multifractal parameters as an indication of different physiological and pathological states of the human brain’, Physica A Stat. Mec. Appl., 2014, 396, pp. 15563.
    29. 29)
      • 29. Andrzejak, R.G., Lehnertz, K., Mormann, F., et al: ‘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, 2001, 64, (061907).
    30. 30)
      • 30. Mishra, V.K., Bajaj, V., Kumar, A., et al: ‘Analysis of ALS and normal EMG signals based on empirical mode decomposition’, IET Sci. Meas. Technol., 2016, 10, (8), pp. 96371.
    31. 31)
      • 31. Tiwari, A.K., Pachori, R.B., Kanhangad, V., et al: ‘Automated diagnosis of epilepsy using key-point based local binary pattern of EEG signals’, IEEE J. Biomed. Health Inform., July 2017, 21, (4), pp. 888896.
    32. 32)
      • 32. Bhattacharyya, A., Pachori, R.B., Upadhyay, A., et al: ‘Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals’, Appl. Sci., April 2017, 7, (4), pp. 385, pages: 18.
    33. 33)
      • 33. Sharma, M., Bhuraneb, A.A., Acharya, U.R.: ‘MMSFL-OWFB: a novel class of orthogonal wavelet filters for epileptic seizure detection’, Knowl-Based Syst. (In Press), 2018. doi:10.1016/j.knosys.2018.07.019..
    34. 34)
      • 34. Orhan, U., Hekim, M., Ozer, M.: ‘EEG signals classification using the K-means clustering and a multilayer perceptron neural network model’, Expert Syst. Appl., 2011, 38, (10), pp. 1347513481.
    35. 35)
      • 35. Peker, M., Sen, B., Delen, D.: ‘A novel method for automated diagnosis of epilepsy using complex-valued classifiers’, IEEE J. Biomed. Health Informat., 2016, 20, (1), pp. 108118.
    36. 36)
      • 36. Acharya, U.R., Oh, S.L., Hagiwara, Y., et al: ‘Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals’, Comput. Biol. Med., September 2018, 100, (1), pp. 270278.
    37. 37)
      • 37. Thodoroff, P., Pineau, J., Lim, A.: ‘Learning robust features using deep learning for automatic seizure detection’, Proc. Mach. Learn. Healthc. Conf., 2016, pp. 178190.
    38. 38)
      • 38. Fergus, P., Hussain, A., Hignett, D., et al: ‘A machine learning system for automated whole-brain seizure detection’, Appl. Comput. Informat., 2016, 12, pp. 7089.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2018.5258
Loading

Related content

content/journals/10.1049/iet-spr.2018.5258
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address