Advertisement

Detection of focal epilepsy in brain maps through a novel pattern recognition technique

  • Eric Ceballos Dominguez
  • M. S. P. Subathra
  • N. J. Sairamya
  • S. Thomas GeorgeEmail author
Original Article
  • 23 Downloads

Abstract

Focal epilepsy is a common neurological disorder of the brain. It is symbolized by recurring seizure activities in particular regions of the brain. Electroencephalogram (EEG) signals contain information about the brain activity that can be used to identify areas affected by seizures. For this, however, neurologists have the challenging task to analyze the EEG signal to imply where the source of epilepsy is located. A novel approach to detect regions of focal epilepsy is proposed in this paper. By using an adaptive mixture of independent component analysis as a decomposition algorithm and information about the position of the measuring electrodes, EEG signals are transformed into two-dimensional brain maps that are further altered by a symmetric-weighted scale-invariant local ternary pattern technique. Features from this altered brain map are used as input in a three-layered artificial neural network for classification between epileptic and artifact brain maps. The brain maps classified as artifact are eliminated, and the epileptic components are given as output for doctors to investigate the regions of the brain affected by focal epilepsy. With the proposed methodology, an accuracy of 99.53% to detect epileptic components in EEG signals is reached.

Keywords

Electroencephalography Classification Artificial neural network Symmetric-weighted scale-invariant local ternary pattern (SWSILTP) technique 

Notes

Funding

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.

References

  1. 1.
    Andrzejak RG, Lehnertz K, Mormann F et al (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 Stat Phys Plasmas Fluids Relat Interdiscip Top 64:061907.  https://doi.org/10.1103/PhysRevE.64.061907 CrossRefGoogle Scholar
  2. 2.
    Fisher RS, Van Emde BW, Blume W et al (2005) Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46:470–472.  https://doi.org/10.1111/j.0013-9580.2005.66104.x CrossRefGoogle Scholar
  3. 3.
    Noachtar S, Rémi J (2009) The role of EEG in epilepsy: a critical review. Epilepsy Behav 15:22–33.  https://doi.org/10.1016/j.yebeh.2009.02.035 CrossRefGoogle Scholar
  4. 4.
    Radüntz T, Scouten J, Hochmuth O, Meffert B (2015) EEG artifact elimination by extraction of ICA-component features using image processing algorithms. J Neurosci Methods 243:84–93.  https://doi.org/10.1016/j.jneumeth.2015.01.030 CrossRefGoogle Scholar
  5. 5.
    Harpale V, Bairagi V (2018) An adaptive method for feature selection and extraction for classification of epileptic EEG signal in significant states. J King Saud Univ Comput Inf Sci.  https://doi.org/10.1016/j.jksuci.2018.04.014 CrossRefGoogle Scholar
  6. 6.
    Callaway E, Harris PR (1974) Coupling between cortical potentials from different areas. Science 183:873–875.  https://doi.org/10.1126/science.183.4127.873 CrossRefGoogle Scholar
  7. 7.
    Meier R, Dittrich H, Schulze-Bonhage A, Aertsen A (2008) Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns. J Clin Neurophysiol 25:119–131.  https://doi.org/10.1097/WNP.0b013e3181775993 CrossRefGoogle Scholar
  8. 8.
    Minasyan GR, Chatten JB, Chatten MJ, Harner RN (2010) Patient-specific early seizure detection from scalp electroencephalogram. J Clin Neurophysiol Off Publ Am Electroencephalogr Soc 27:163.  https://doi.org/10.1097/WNP.0b013e3181e0a9b6 CrossRefGoogle Scholar
  9. 9.
    Garcés Correa A, Laciar E, Patĩo HD, Valentinuzzi ME (2007) Artifact removal from EEG signals using adaptive filters in cascade. J Phys Conf Ser 90:012081.  https://doi.org/10.1088/1742-6596/90/1/012081 CrossRefGoogle Scholar
  10. 10.
    Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32:1084–1093.  https://doi.org/10.1016/j.eswa.2006.02.005 CrossRefGoogle Scholar
  11. 11.
    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 187:1017–1026.  https://doi.org/10.1016/j.amc.2006.09.022 MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Niederhauser JJ, Esteller R, Echauz J et al (2003) Detection of seizure precursors from depth-EEG using a sign periodogram transform. IEEE Trans Biomed Eng 50:449–458.  https://doi.org/10.1109/TBME.2003.809497 CrossRefGoogle Scholar
  13. 13.
    Yuan Q, Zhou W, Li S, Cai D (2011) Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res 96:29–38.  https://doi.org/10.1016/j.eplepsyres.2011.04.013 CrossRefGoogle Scholar
  14. 14.
    Onton J, Westerfield M, Townsend J, Makeig S (2006) Imaging human EEG dynamics using independent component analysis. Neurosci Biobehav Rev 30:808–822.  https://doi.org/10.1016/j.neubiorev.2006.06.007 CrossRefGoogle Scholar
  15. 15.
    Selvan SE, George ST, Balakrishnan R (2015) Range-based ICA using a nonsmooth quasi-Newton optimizer for electroencephalographic source localization in focal epilepsy. Neural Comput 27:628–671.  https://doi.org/10.1162/NECO_a_00700 MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Makeig S, Bell AJ, Jung T-P, Sejnowski TJ (1996) Independent component analysis of electroencephalographic data. Adv Neural Inf Process Syst.  https://doi.org/10.1109/ICOSP.2002.1180091 CrossRefGoogle Scholar
  17. 17.
    Jung TP, Makeig S, Humphries C et al (2000) Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37:163–178.  https://doi.org/10.1017/S0048577200980259 CrossRefGoogle Scholar
  18. 18.
    Artoni F, Delorme A, Makeig S (2018) Applying dimension reduction to EEG data by principal component analysis reduces the quality of its subsequent independent component decomposition. Neuroimage 175:176–187.  https://doi.org/10.1016/j.neuroimage.2018.03.016 CrossRefGoogle Scholar
  19. 19.
    Fitzgibbon SP, Powers DMW, Pope KJ, Clark CR (2007) Removal of EEG noise and artifact using blind source separation. J Clin Neurophysiol 24:232–243.  https://doi.org/10.1097/WNP.0b013e3180556926 CrossRefGoogle Scholar
  20. 20.
    Romero S, Mañanas MA, Barbanoj MJ (2008) A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case. Comput Biol Med 38:348–360.  https://doi.org/10.1016/j.compbiomed.2007.12.001 CrossRefGoogle Scholar
  21. 21.
    Cınar S, Acır N (2017) A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis. Expert Syst Appl 68:36–44.  https://doi.org/10.1016/j.eswa.2016.10.009 CrossRefGoogle Scholar
  22. 22.
    Hyvärinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9:1483–1492.  https://doi.org/10.1162/neco.1997.9.7.1483 CrossRefGoogle Scholar
  23. 23.
    Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159CrossRefGoogle Scholar
  24. 24.
    Cardoso JF, Souloumiac A (1993) Blind beamforming for non-gaussian signals. IEE Proc F Radar Signal Process 140:362–370.  https://doi.org/10.1049/ip-f-2.1993.0054 CrossRefGoogle Scholar
  25. 25.
    Belouchrani A, Abed-Meraim K, Cardoso JF, Moulines E (1993) Second order blind separation of temporally correlated sources. In: Proceedings of the international conference on digital signal processing, pp 346–351Google Scholar
  26. 26.
    George ST, Balakrishnan R, Johnson JS, Jayakumar J (2017) Application and evaluation of independent component analysis methods to generalized seizure disorder activities exhibited in the brain. Clin EEG Neurosci 48:295–300.  https://doi.org/10.1177/1550059416677915 CrossRefGoogle Scholar
  27. 27.
    Hassan N, Ramli DA (2018) A comparative study of blind source separation for bioacoustics sounds based on FastICA, PCA and NMF. Procedia Comput Sci 126:363–372CrossRefGoogle Scholar
  28. 28.
    Leutheuser H, Gabsteiger F, Hebenstreit F et al (2013) Comparison of the AMICA and the InfoMax algorithm for the reduction of electromyogenic artifacts in EEG data. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS, pp 6804–6807Google Scholar
  29. 29.
    Palmer J, Kreutz-Delgado K, Makeig S (2011) AMICA: an adaptive mixture of independent component analyzers with shared components. Technical report, Swart Centre for Computational Neuroscience, San Diego, CAGoogle Scholar
  30. 30.
    Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21.  https://doi.org/10.1016/j.jneumeth.2003.10.009 CrossRefGoogle Scholar
  31. 31.
    Sairamya NJ, George ST, Balakrishnan R et al (2018) Classification of EEG signals for detection of epileptic seizure activities based on feature extraction from brain maps using image processing algorithms. IET Image Process 12:2153–2162CrossRefGoogle Scholar
  32. 32.
    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19:1635–1650.  https://doi.org/10.1109/TIP.2010.2042645 MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Liao S, Zhao G, Kellokumpu V et al (2010) Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 1301–1306Google Scholar
  34. 34.
    Guo L, Rivero D, Pazos A (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193:156–163.  https://doi.org/10.1016/j.jneumeth.2010.08.030 CrossRefGoogle Scholar
  35. 35.
    Guo L, Rivero D, Dorado J et al (2011) Automatic feature extraction using genetic programming: an application to epileptic EEG classification. Expert Syst Appl 38:10425–10436.  https://doi.org/10.1016/j.eswa.2011.02.118 CrossRefGoogle Scholar
  36. 36.
    Orhan U, Hekim M, Ozer M (2011) EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst Appl 38:13475–13481.  https://doi.org/10.1016/j.eswa.2011.04.149 CrossRefGoogle Scholar
  37. 37.
    Kocadagli O, Langari R (2017) Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations. Expert Syst Appl 88:419–434CrossRefGoogle Scholar
  38. 38.
    Islam MK, Rastegarnia A, Yang Z (2015) A wavelet-based artifact reduction from scalp EEG for epileptic seizure detection. IEEE J Biomed Health Inform 20(5):1321–1332CrossRefGoogle Scholar
  39. 39.
    Mayeli A, Zotev V, Refai H, Bodurka J (2016) Real-time EEG artifact correction during fMRI using ICA. J Neurosci Methods 274:27–37.  https://doi.org/10.1016/j.jneumeth.2016.09.012 CrossRefGoogle Scholar
  40. 40.
    Zhou W, Gotman J (2009) Automatic removal of eye movement artifacts from the EEG using ICA and the dipole model. Prog Nat Sci 19:1165–1170.  https://doi.org/10.1016/j.pnsc.2008.11.013 CrossRefGoogle Scholar
  41. 41.
    Michelmann S, Treder MS, Griffiths B et al (2018) Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA). J Neurosci Methods 307:125–137.  https://doi.org/10.1016/j.jneumeth.2018.06.021 CrossRefGoogle Scholar
  42. 42.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987.  https://doi.org/10.1109/TPAMI.2002.1017623 CrossRefzbMATHGoogle Scholar
  43. 43.
    Tan W, Li B, Zhang W (2010) Research on background modeling method based on center-symmetric local binary patterns. J Univ Sci Technol China 11:004Google Scholar
  44. 44.
    Zhang Z, Xiao B, Wang C et al (2011) Background modeling by exploring multi-scale fusion of texture and intensity in complex scenes. In: The first Asian conference on pattern recognition. IEEE, pp 402–406Google Scholar
  45. 45.
    Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22:717–727.  https://doi.org/10.1016/S0731-7085(99)00272-1 CrossRefGoogle Scholar
  46. 46.
    Puri M, Solanki A, Padawer T et al (2016) Introduction to artificial neural network (ANN) as a predictive tool for drug design, discovery, delivery, and disposition. Artif Neural Netw Drug Des Deliv Dispos.  https://doi.org/10.1016/B978-0-12-801559-9.00001-6 CrossRefGoogle Scholar
  47. 47.
    Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. In: Anderson JA, Rosenfeld E (eds) Neurocomputing: foundations of research. MIT Press, Cambridge, pp 696–699Google Scholar
  48. 48.
    Hopfield JJ (1988) Artificial neural networks. IEEE Circuits Devices Mag 4:3–10.  https://doi.org/10.1109/101.8118 CrossRefGoogle Scholar
  49. 49.
    Mehdy MM, Ng PY, Shair EF et al (2017) Artificial neural networks in image processing for early detection of breast cancer. Comput Math Methods Med 2017:1–15.  https://doi.org/10.1155/2017/2610628 CrossRefGoogle Scholar
  50. 50.
    Okumura E, Kawashita I, Ishida T (2017) Computerized classification of pneumoconiosis on digital chest radiography artificial neural network with three stages. J Digit Imaging.  https://doi.org/10.1007/s10278-017-9942-0 CrossRefGoogle Scholar
  51. 51.
    Lo SCB, Chan HP, Lin JS et al (1995) Artificial convolution neural network for medical image pattern recognition. Neural Netw 8:1201–1214.  https://doi.org/10.1016/0893-6080(95)00061-5 CrossRefGoogle Scholar
  52. 52.
    Whelan CD, Altmann A, Botía JA et al (2018) Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study. Brain 141:391–408.  https://doi.org/10.1093/brain/awx341 CrossRefGoogle Scholar
  53. 53.
    Coan AC, Campos BM, Beltramini GC et al (2014) Distinct functional and structural MRI abnormalities in mesial temporal lobe epilepsy with and without hippocampal sclerosis. Epilepsia 55:1187–1196.  https://doi.org/10.1111/epi.12670 CrossRefGoogle Scholar
  54. 54.
    Boughorbel S, Jarray F, El-Anbari M (2017) Optimal classifier for imbalanced data using Matthews correlation coefficient metric. PLoS ONE 12:e0177678.  https://doi.org/10.1371/journal.pone.0177678 CrossRefGoogle Scholar
  55. 55.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings—2005 IEEE computer society conference on computer vision and pattern recognition, CVPR 2005, pp 886–893Google Scholar
  56. 56.
    Zhang B, Gao Y, Zhao S, Liu J (2010) Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19:533–544.  https://doi.org/10.1109/TIP.2009.2035882 MathSciNetCrossRefzbMATHGoogle Scholar
  57. 57.
    Behnam M, Pourghassem H (2016) Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search. Comput Methods Programs Biomed 132:115–136.  https://doi.org/10.1016/j.cmpb.2016.04.014 CrossRefGoogle Scholar
  58. 58.
    Patidar S, Panigrahi T (2017) Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomed Signal Process Control 34:74–80.  https://doi.org/10.1016/j.bspc.2017.01.001 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Eric Ceballos Dominguez
    • 1
  • M. S. P. Subathra
    • 2
  • N. J. Sairamya
    • 3
  • S. Thomas George
    • 4
    Email author
  1. 1.Dresden University of TechnologyDresdenGermany
  2. 2.Department of Electrical and Electronics Engineering, School of Engineering and TechnologyKarunya Institute of Technology and SciencesCoimbatoreIndia
  3. 3.Department of Electronics and Communication Engineering, School of Engineering and TechnologyKarunya Institute of Technology and SciencesCoimbatoreIndia
  4. 4.Department of Instrumentation Engineering, School of Engineering and TechnologyKarunya Institute of Technology and SciencesCoimbatoreIndia

Personalised recommendations