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Classification of focal and non focal EEG signals using empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks

  • Wei ZengEmail author
  • Mengqing Li
  • Chengzhi Yuan
  • Qinghui Wang
  • Fenglin Liu
  • Ying Wang
Article

Abstract

Electroencephalogram (EEG) signals can be used to identify the human brain in different disease conditions. Nonetheless, it is difficult to detect the subtle and vital differences in EEG simply by visual inspection because of the non-stationary nature of EEG signals. Specifically, in order to find the epileptogenic focus for medical treatment in the case of a partial epilepsy, an intelligent system that can accurately and automatically detect and discriminate focal and non focal groups of EEG signals is required. This will assist clinicians in locating epileptogenic foci before surgery. In this study we propose a novel method for classification between focal and non focal EEG signals based upon empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) using EMD, and the third and fourth IMFs components are extracted which contain most of the EEG signals’ energy and are considered to be the predominant IMFs. Second, phase space of the two IMFs componets is reconstructed, in which the properties associated with the EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between the focal and non focal groups of EEG signals. Third, neural networks are then used as the classifier with feature vectors as the input to distinguish between focal and non focal EEG signals based on the difference of system dynamics between the two groups. Finally, experiments are carried out on the Bern Barcelona database to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved accuracy on the 50 pairs and 3750 pairs of EEG signals is reported to be \(96\%\) and \(95.37\%\), respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of focal EEG signals in the clinical application.

Keywords

Electroencephalogram (EEG) Focal and non focal EEG Empirical mode decomposition (EMD) Phase space reconstruction (PSR) Euclidean distance (ED) System dynamics Neural networks 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61773194, 61304084), by the Natural Science Foundation of Fujian Province of China (Grant No. 2018J01542), by the Program for New Century Excellent Talents in Fujian Province University and by the Training Program of Innovation and Entrepreneurship for Undergraduates (Grant No. 201811312002).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Aarabi A, He B (2017) Seizure prediction in patients with focal hippocampal epilepsy. Clin Neurophysiol 128(7):1299–1307CrossRefGoogle Scholar
  2. Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl Based Syst 45:147–165CrossRefGoogle Scholar
  3. Acharya UR, Hagiwara Y, Deshpande SN, Suren S, Koh JEW, Oh SL, Lim CM (2019) Characterization of focal EEG signals: a review. Future Gener Comput Syst 91:290–299CrossRefGoogle Scholar
  4. Andrzejak RG, Schindler K, Rummel C (2012) Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys Rev E 86(4):046206CrossRefGoogle Scholar
  5. Arunkumar N, Ramkumar K, Venkatraman V, Abdulhay E, Fernandes SL, Kadry S, Segal S (2017) Classification of focal and non focal EEG using entropies. Pattern Recognit Lett 94:112–117CrossRefGoogle Scholar
  6. Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24:1163–1177CrossRefGoogle Scholar
  7. Bajaj V, Rai K, Kumar A, Sharma D, Singh GK (2017) Rhythm-based features for classification of focal and non-focal EEG signals. IET Signal Process 11(6):743–748Google Scholar
  8. Bhattacharyya A, Pachori RB, Acharya UR (2017) Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis. Entropy 19(3):99CrossRefGoogle Scholar
  9. Bhattacharyya A, Sharma M, Pachori RB, Sircar P, Acharya UR (2018) A novel approach for automated detection of focal EEG signals using empirical wavelet transform. Neural Comput Appl 29(8):47–57CrossRefGoogle Scholar
  10. Chatterjee S, Pratiher S, Bose R (2017) Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non-focal electroencephalogram signals. IET Sci Meas Technol 11(8):1014–1021CrossRefGoogle Scholar
  11. Chen M, Fang Y, Zheng X (2014) Phase space reconstruction for improving the classification of single trial EEG. Biomed Signal Process Control 11:10–16CrossRefGoogle Scholar
  12. Chu K (1999) An introduction to sensitivity, specificity, predictive values and likelihood ratios. Emerg Med Australas 11(3):175–181Google Scholar
  13. Curry DJ, Gowda A, McNichols RJ, Wilfong AA (2012) MR-guided stereotactic laser ablation of epileptogenic foci in children. Epilepsy Behav 24(4):408–414CrossRefGoogle Scholar
  14. Das AB, Bhuiyan MIH (2016) Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed Signal Process Control 29:11–21CrossRefGoogle Scholar
  15. De Lima ER, Andrade AO, Pons JL, Kyberd P, Nasuto SJ (2006) Empirical mode decomposition: a novel technique for the study of tremor time series. Med Biol Eng Comput 44(7):569–582CrossRefGoogle Scholar
  16. Engel J, Mcdermott MP, Wiebe S, Langfitt JT, Stern JM, Dewar S, Jacobs M (2012) Early surgical therapy for drug-resistant temporal lobe epilepsy: a randomized trial. JAMA 307(9):922–930CrossRefGoogle Scholar
  17. Farrell J (1998) Stability and approximator convergence in nonparametric nonlinear adaptive control. IEEE Trans Neural Netw 9(5):1008–1020CrossRefGoogle Scholar
  18. Fisher RS, Cross JH, French JA, Higurashi N, Hirsch E, Jansen FE, Scheffer IE (2017) Operational classification of seizure types by the International League Against Epilepsy: position paper of the ILAE Commission for Classification and Terminology. Epilepsia 58(4):522–530CrossRefGoogle Scholar
  19. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8(9):700CrossRefGoogle Scholar
  20. Gehlot M, Kumar Y, Meena H, Bajaj V, Kumar A (2015) EMD based features for discrimination of focal and non-focal EEG signals. In: Information systems design and intelligent applications, pp 85–93Google Scholar
  21. Gupta V, Priya T, Yadav AK, Pachori RB, Acharya UR (2017) Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform. Pattern Recognit Lett 94:180–188CrossRefGoogle Scholar
  22. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Eng Sci 454(1971):903–995MathSciNetCrossRefzbMATHGoogle Scholar
  23. Huang NE, Wu MLC, Long SR, Shen SS, Qu W, Gloersen P, Fan KL (2003) A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc R Soc Lond Ser A Math Phys Eng Sci 459(2037):2317–2345MathSciNetCrossRefzbMATHGoogle Scholar
  24. Jia J, Goparaju B, Song J, Zhang R, Westover MB (2017) Automated identification of epileptic seizures in EEG signals based on phase space representation and statistical features in the CEEMD domain. Biomed Signal Process Control 38:148–157CrossRefGoogle Scholar
  25. 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
  26. Kafashan M, Ryu S, Hargis MJ, Laurido-Soto O, Roberts DE, Thontakudi A, Ching S (2017) EEG dynamical correlates of focal and diffuse causes of coma. BMC Neurol 17(1):197CrossRefGoogle Scholar
  27. Lee SH, Lim JS, Kim JK, Yang J, Lee Y (2014) Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance. Comput Methods Progr Biomed 116(1):10–25CrossRefGoogle Scholar
  28. Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43(7):807–816CrossRefGoogle Scholar
  29. Merigó JM, Casanovas M (2011) Induced aggregation operators in the Euclidean distance and its application in financial decision making. Expert Syst Appl 38:7603–7608CrossRefGoogle Scholar
  30. Michael S (2005) Applied nonlinear time series analysis: applications in physics, physiology and finance, vol 52. World Scientific, SingaporezbMATHGoogle Scholar
  31. Parvizi J, Kastner S (2018) Promises and limitations of human intracranial electroencephalography. Nat Neurosci 21:474–483CrossRefGoogle Scholar
  32. Raghu S, Sriraam N (2018) Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms. Expert Syst Appl 113:18–32CrossRefGoogle Scholar
  33. Rai K, Bajaj V, Kumar A (2015) Novel feature for identification of focal EEG signals with K-means and fuzzy C-means algorithms. In: IEEE international conference on digital signal processing, pp 412–416Google Scholar
  34. Sato Y, Doesburg SM, Wong SM, Ochi A, Otsubo H (2015) Dynamic preictal relations in FCD type II: potential for early seizure detection in focal epilepsy. Epilepsy Res 110:26–31CrossRefGoogle Scholar
  35. Sharma R, Pachori RB (2015) Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl 42(3):1106–1117CrossRefGoogle Scholar
  36. Sharma R, Pachori RB (2018) Automated classification of focal and non-focal EEG signals based on bivariate empirical mode decomposition. In: Biomedical signal and image processing in patient care, pp 13–33Google Scholar
  37. Sharma R, Pachori RB, Acharya UR (2015a) An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 17(8):5218–5240CrossRefGoogle Scholar
  38. Sharma R, Pachori RB, Acharya UR (2015b) Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17(2):669–691CrossRefGoogle Scholar
  39. Sharma M, Dhere A, Pachori RB, Acharya UR (2017a) An automatic detection of focal EEG signals using new class of timefrequency localized orthogonal wavelet filter banks. Knowl Based Syst 118:217–227CrossRefGoogle Scholar
  40. Sharma R, Kumar M, Pachori RB, Acharya UR (2017b) Decision support system for focal EEG signals using tunable-Q wavelet transform. J Comput Sci 20:52–60Google Scholar
  41. Shayegh F, Sadri S, Amirfattahi R, Ansari-Asl K (2014) A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals. Comput Methods Progr Biomed 113(1):323–337CrossRefzbMATHGoogle Scholar
  42. Sheintuch L, Friedman A, Efrat N, Tifeeret C, Shorer Z, Neuman I, Shallom I (2014) O16: detection of epileptiform activity using multi-channel linear prediction coefficients and localization of epileptic foci based on EEG-fMRI data. Clin Neurophysiol 125:S33CrossRefGoogle Scholar
  43. Singh P, Pachori RB (2017) Classification of focal and nonfocal EEG signals using features derived from Fourier-based rhythms. J Mech Med Biol 17(07):1740002CrossRefGoogle Scholar
  44. Sivakumar B (2002) A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. J Hydrol 258(1–4):149–162Google Scholar
  45. Som A, Krishnamurthi N, Venkataraman V, Turaga P (2016) Attractor-shape descriptors for balance impairment assessment in Parkinson’s disease. In: IEEE conference on engineering in medicine and biology society, pp 3096–3100Google Scholar
  46. Stoer J, Bulirsch R (2013) Introduction to numerical analysis. Springer, BerlinzbMATHGoogle Scholar
  47. Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666CrossRefGoogle Scholar
  48. Sun Y, Li J, Liu J, Chow C, Sun B, Wang R (2015) Using causal discovery for feature selection in multivariate numerical time series. Mach Learn 101(1–3):377–395MathSciNetCrossRefzbMATHGoogle Scholar
  49. Takens F (1980) Detecting strange attractors in turbulence. In: Dynamical Systems and Turbulence, Warwick 1980. Springer, Berlin, pp 366–381Google Scholar
  50. Tang B, Dong S, Song T (2012) Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation. Signal Process 92(1):248–258CrossRefGoogle Scholar
  51. Taran S, Bajaj V (2018) Clustering variational mode decomposition for identification of focal EEG signals. IEEE Sens Lett 2(4):1–4CrossRefGoogle Scholar
  52. Timothy LT, Krishna BM, Nair U (2017) Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis. Int J Psychophysiol 120:86–95CrossRefGoogle Scholar
  53. Venkataraman V, Turaga P (2016) Shape distributions of nonlinear dynamical systems for video-based inference. IEEE Trans Pattern Anal Mach Intell 38(12):2531–2543CrossRefGoogle Scholar
  54. Wang C, Hill DJ (2006) Learning from neural control. IEEE Trans Neural Netw 17(1):130–146CrossRefGoogle Scholar
  55. Wang C, Hill DJ (2007) Deterministic learning and rapid dynamical pattern recognition. IEEE Trans Neural Netw 18(3):617–630CrossRefGoogle Scholar
  56. Wang C, Hill DJ (2009) Deterministic learning theory for identification, recognition and control. CRC Press, Boca RatonGoogle Scholar
  57. Wang C, Chen T, Chen G, Hill DJ (2009) Deterministic learning of nonlinear dynamical systems. Int J Bifurc Chaos 19(4):1307–1328MathSciNetCrossRefzbMATHGoogle Scholar
  58. Wang L, Xue W, Li Y, Luo M, Huang J, Cui W, Huang C (2017) Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis. Entropy 19(6):222CrossRefGoogle Scholar
  59. Xu B, Jacquir S, Laurent G, Bilbault JM, Binczak S (2013) Phase space reconstruction of an experimental model of cardiac field potential in normal and arrhythmic conditions. In: 35th annual international conference of the IEEE engineering in medicine and biology society, pp 3274–3277Google Scholar
  60. Yuan Q, Cai C, Xiao H, Liu X, Wen Y (2007) Diagnosis of breast tumours and evaluation of prognostic risk by using machine learning approaches. In: Huang DS, Heutte L, Loog M (eds) Advanced intelligent computing theories and applications. With aspects of contemporary intelligent computing techniques. Springer, Berlin, pp 1250–1260CrossRefGoogle Scholar
  61. Zahra A, Kanwal N, ur Rehman N, Ehsan S, McDonald-Maier KD (2017) Seizure detection from EEG signals using multivariate empirical mode decomposition. Comput Biol Med 88:132–141CrossRefGoogle Scholar
  62. Zanzotto FM, Croce D (2010) Comparing EEG/ERP-like and fMRI-like techniques for reading machine thoughts. In: International conference on brain informatics. Springer, Berlin, pp 133–144Google Scholar
  63. Zhang Y, Zhou W, Yuan S, Yuan Q (2015) Seizure detection method based on fractal dimension and gradient boosting. Epilepsy Behav 43:30–38CrossRefGoogle Scholar
  64. Zhang T, Chen W, Li M (2017) AR based quadratic feature extraction in the VMD domain for the automated seizure detection of EEG using random forest classifier. Biomed Signal Process Control 31:550–559CrossRefGoogle Scholar
  65. Zhu G, Li Y, Wen PP, Wang S, Xi M (2013) Epileptogenic focus detection in intracranial EEG based on delay permutation entropy. In: Proceedings of AIP conference, pp 31–36Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Physics and Mechanical and Electrical EngineeringLongyan UniversityLongyanChina
  2. 2.Department of Mechanical, Industrial and Systems EngineeringUniversity of Rhode IslandKingstonUSA

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