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A novel approach based on BSPCI for quantifying functional connectivity pattern of the brain’s region for the classification of epileptic seizure

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Epilepsy seizure is brain neurological abnormality which arises from the sudden deviation of the electrical interaction in the brain. Electroencephalography (EEGs) are obtained from 22 subjects with epileptic seizure and non-epileptic seizure states recorded using 23 channels with a sampling frequency of 256 Hz. The functional connectivity of the brain region can be exacted from the features obtained from EEG signals by measuring phase locking value (PLV). The neuronal connection in the brain can be expressed in terms of phase synchrony. Despite of the fact that brain states should be characterize independently based upon its extracted features. Therefore, a novel functional connectivity index (FCI) feature is proposed, namely Bi-Spectral Phase Concurrence Index (BSPCI). It is used to represent the spectral information with third cumulant order correlation functions of the EEG signal. In this paper, three FCI features were measured namely, the magnitude's mean of the bi-spectral, the normalized entropy of bi-spectrum (NE1) and the normalized entropy of squared bi-spectrum (NE2) from the BSPCI. Rank sum test based on the Wilcoxon approach is used to find the set of statistical difference between quantitative features extracted from EEG signals. The results provide evidence that the FCI will have an impact in separating the difference among seizure states of various epileptic seizure patients. On the part of reducing a large number of the feature vector, feature selection is performed by utilizing the sequential forward selection method. PLV is measured for quantifying the obtained phase synchrony of EEG signal. For the classification of epileptic seizure, Support Vector Machine is utilized which gain a large accuracy for the proposed bi-spectral analysis method when compared with Incremental Gradient Descent (IGD), Logistic Regression (LR) and Multilayer Perceptrons (MLP). The result is compared with IGD, LR, and MLP for obtaining better performance rate and the classification is 98.79% for the proposed work.

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  1. Alexandridis A (2013) Evolving RBF neural networks for adaptive soft-sensor design. Int J Neural Syst 23(06):1350029

  2. Ashokkumar SR, MohanBabu G, Anupallavi S (2019) A KSOM based neural network model for classifying the epilepsy using adjustable analytic wavelet transform. Multimed Tools Appl.

  3. Astolfi L et al (2007) Comparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum Brain Mapp 28(2):143–157

  4. Awal MA et al (2016) EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: a structured review. Clin Neurophysiol 127(1):285–296

  5. Bandarabadi M et al (2015) Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol 126(2):237–248

  6. Brazier MA (1972) Spread of seizure discharges in epilepsy: anatomical and electrophysiological considerations. Exp Neurol 36(2):263–272

  7. Brinkmann BH et al (2015) Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy. PLoS ONE 10(8):e0133900

  8. Bruni R, Bianchi G (2015) Effective classification using a small training set based on discretization and statistical analysis. IEEE Trans Knowl Data Eng 27(9):2349–2361

  9. Castillo E et al (2015) Distributed one-class support vector machine. Int J Neural Syst 25(07):1550029

  10. Celka P (2007) Statistical analysis of the phase-locking value. IEEE Signal Process Lett 14(9):577–580

  11. Chandran V, Elgar SL (1993) Pattern recognition using invariants defined from higher order spectra-one-dimensional inputs. IEEE Trans Signal Process 41(1):205–212

  12. Chang CC (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2(3):27

  13. Chávez M, Martinerie J, Le Van QM (2003) Statistical assessment of nonlinear causality: application to epileptic EEG signals. J Neurosci Methods 124(2):113–128

  14. CHB-MIT Scalp EEG Database (2018) Accessed 27 Feb 2018

  15. Chisci L et al (2010) Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Trans Biomed Eng 57(5):1124–1132

  16. Chua KC et al (2010) Application of higher order statistics/spectra in biomedical signals—a review. Med Eng Phys 32(7):679–689

  17. Cui S et al (2018) Learning EEG synchronization patterns for epileptic seizure prediction using bag-of-wave features. J Ambient Intell Hum Comput.

  18. Fisher RS et al (2014) ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55(4):475–482

  19. Kaminski M, Blinowska KJ (2014) Directed transfer function is not influenced by volume conduction—inexpedient pre-processing should be avoided. Front Comput Neurosci 8:61

  20. Kerby DS (2014) The simple difference formula: an approach to teaching nonparametric correlation. Compr Psychol.

  21. Kramer MA et al (2012) Human seizures self-terminate across spatial scales via a critical transition. Proc Natl Acad Sci 109(51):21116–21121

  22. Kumar CU, Kamalraj S (2019) Ambient intelligence architecture of MRPM context based 12-tap further desensitized half band FIR filter for EEG signal. J Ambient Intell Hum Comput.

  23. Lachaux JP et al (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8(4):194–208

  24. Leuchter AF et al (1987) Electroencephalographic spectra and coherence in the diagnosis of Alzheimer's-type and multi-infarct dementia: a pilot study. Arch Gen Psychiatry 44(11):993–998

  25. Maiwald T et al (2004) Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic. Physica D 194(3–4):357–368

  26. Martinerie J et al (1998) Epileptic seizures can be anticipated by non-linear analysis. Nat Med 4(10):1173

  27. Mirowski P et al (2009) Classification of patterns of EEG synchronization for seizure prediction. Clin Neurophysiol 120(11):1927–1940

  28. Nunez PL et al (1997) EEG coherency: I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at smultiple scales. Electroencephalogr Clin Neurophysiol 103(5):499–515

  29. Nunez PL et al (1999) EEG coherency II: experimental comparisons of multiple measures. Clin Neurophysiol 110(3):469–486

  30. Pudil P, Novovičová J, Kittler J (1994) Floating search methods in feature selection. Pattern Recogn Lett 15(11):1119–1125

  31. Sakkalis V (2011) Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput Biol Med 41(12):1110–1117

  32. Sankari Z, Adeli H, Adeli A (2011) Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer’s disease. Clin Neurophysiol 122(5):897–906

  33. Serletis D et al (2013) Phase synchronization of neuronal noise in mouse hippocampal epileptiform dynamics. Int J Neural Syst 23(01):1250033

  34. Song J et al (2013) Methods for examining electrophysiological coherence in epileptic networks. Front Neurol 4:55

  35. Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28(11):1178–1193

  36. Uhlhaas PJ, Singer W (2006) Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron 52(1):155–168

  37. Van Esbroeck A et al (2016) Multi-task seizure detection: addressing intra-patient variation in seizure morphologies. Mach Learn 102(3):309–321

  38. Van Mierlo P et al (2014) Functional brain connectivity from EEG in epilepsy: seizure prediction and epileptogenic focus localization. Prog Neurobiol 121:19–35

  39. Van Quyen ML et al (2003) Toward a neurodynamical understanding of ictogenesis. Epilepsia 44:30–43

  40. Vapnik V, Cortes C (1995) Support vector networks. Mach Learn 20:273–297

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Correspondence to S. Anupallavi.

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Anupallavi, S., MohanBabu, G. A novel approach based on BSPCI for quantifying functional connectivity pattern of the brain’s region for the classification of epileptic seizure. J Ambient Intell Human Comput (2020).

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  • Electroencephalography (EEG)
  • Feature extraction
  • PLV (phase locking value)
  • Bi-spectral phase concurrence index (BSPCI)
  • Classification