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An Intelligent Method for Epilepsy Seizure Detection Based on Hybrid Nonlinear EEG Data Features Using Adaptive Signal Decomposition Methods

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Abstract

Epilepsy is a neurological disorder directly linked with brain electrical activities, which causes sudden and recurrent seizures in the patient. Epilepsy seizures can be detected by using automatic detection systems by analyzing significant features extracted from EEG recordings. In this work, we aim to focus on adaptive mode decomposition methods, namely empirical mode decomposition (EMD), empirical wavelet transform (EWT) and variational mode decomposition (VMD) methods, that decompose EEG signals into different levels of resolution and enable extracting relevant nonlinear features for accurate detection of epilepsy seizures. We propose an intelligent epilepsy seizure detection system using a neural network (NN) classifier based on nonlinear features extracted from ECG signals using adaptive mode decomposition methods. In addition, we propose to use hybrid features selected using a wrapper-based feature selection method from nonlinear features extracted using different adaptive mode decomposition methods. The experimental results prove that the proposed system can detect epilepsy seizures up to an accuracy of 99%, the sensitivity of 98%, specificity of 99% and area under ROC (AUC) of 99% using NSC_ND dataset. We also conduct nonparametric statistical significance tests, Friedman test and Wilcoxon signed ranks post hoc test for demonstrating statistical differences between the obtained results and superior performance of the proposed system. This study enables researchers and practitioners to examine the proposed method and adaptive mode decomposition methods for detecting epilepsy seizures.

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References

  1. U.R. Acharya, F. Molinari, S.V. Sree, S. Chattopadhyay, K.H. Ng, J.S. Suri, Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 7(4), 401–408 (2012)

    Google Scholar 

  2. H. Akbari, M.T. Sadiq, Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms. Phys. Eng. Sci. Med. 44(1), 157–171 (2021)

    Google Scholar 

  3. M. Alolaiwy, M. Tanik, L. Jololian, From CNNs to adaptive filter design for digital image denoising using reinforcement q-learning. in SoutheastCon 2021 (IEEE, 2021), pp. 1–8

  4. H.U. Amin, A.S. Malik, R.F. Ahmad, N. Badruddin, N. Kamel, M. Hussain, W.T. Chooi, Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australas. Phys. Eng. Sci. Med. 38(1), 139–149 (2015)

    Google Scholar 

  5. A. Bhattacharyya, R.B. Pachori, A. Upadhyay, U.R. Acharya, Tunable-q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 7(4), 385 (2017)

    Google Scholar 

  6. A. Bhattacharyya, L. Singh, R.B. Pachori, Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals. Digit. Signal Process. 78, 185–196 (2018)

    Google Scholar 

  7. C.M. Bishop et al., Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1995)

    MATH  Google Scholar 

  8. V.R. Carvalho, M.F. Moraes, A.P. Braga, E.M. Mendes, Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification. Biomed. Signal Process. Control 62, 102073 (2020)

    Google Scholar 

  9. W.A. Chaovalitwongse, O.A. Prokopyev, P.M. Pardalos, Electroencephalogram (EEG) time series classification: applications in epilepsy. Ann. Oper. Res. 148(1), 227–250 (2006)

    MATH  Google Scholar 

  10. S. Chen, X. Zhang, L. Chen, Z. Yang, Automatic diagnosis of epileptic seizure in electroencephalography signals using nonlinear dynamics features. IEEE Access 7, 61046–61056 (2019)

    Google Scholar 

  11. X. Chen, J.C. Jeong, Enhanced recursive feature elimination, in Sixth International Conference on Machine Learning and Applications (ICMLA 2007) (IEEE, 2007), pp. 429–435

  12. M. Dalal, M. Tanveer, R.B. Pachori, Automated identification system for focal EEG signals using fractal dimension of FAWT-based sub-bands signals, in Machine Intelligence and Signal Analysis (Springer, 2019), pp. 583–596

  13. A.B. Das, M.I.H. Bhuiyan, Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed. Signal Process. Control 29, 11–21 (2016)

    Google Scholar 

  14. K. Dragomiretskiy, D. Zosso, Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2013)

    MathSciNet  MATH  Google Scholar 

  15. R. Esteller, J. Echauz, T. Tcheng, B. Litt, B. Pless Line length: an efficient feature for seizure onset detection, in 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol 2 (IEEE, 2001), pp. 1707–1710

  16. O. Fasil, R. Rajesh, Time-domain exponential energy for epileptic EEG signal classification. Neurosci. Lett. 694, 1–8 (2019)

    Google Scholar 

  17. Z. Feng, D. Zhang, M.J. Zuo, Adaptive mode decomposition methods and their applications in signal analysis for machinery fault diagnosis: a review with examples. IEEE Access 5, 24301–24331 (2017)

    Google Scholar 

  18. P. Flandrin, G. Rilling, P. Goncalves, Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11(2), 112–114 (2004)

    Google Scholar 

  19. M. Friedman, A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86–92 (1940)

    MathSciNet  MATH  Google Scholar 

  20. D. George, P. Mallery, IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference (Routledge, London, 2019)

    Google Scholar 

  21. J. Gilles, G. Tran, S. Osher, 2D empirical transforms. Wavelets, ridgelets, and curvelets revisited. SIAM J. Imaging Sci. 7(1), 157–186 (2014)

    MathSciNet  MATH  Google Scholar 

  22. L. Guo, D. Rivero, J. Dorado, J.R. Rabunal, A. Pazos, Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Methods 191(1), 101–109 (2010)

    Google Scholar 

  23. S.A. Hosseini, A hybrid approach based on higher order spectra for clinical recognition of seizure and epilepsy using brain activity. Basic Clin. Neurosci. 8(6), 479 (2017)

    Google Scholar 

  24. N. Koolen, K. Jansen, J. Vervisch, V. Matic, M. De Vos, G. Naulaers, S. Van Huffel, Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings. Clin. Neurophysiol. 125(10), 1985–1994 (2014)

    Google Scholar 

  25. G. Kumar, Evaluation metrics for intrusion detection systems—a study. Evaluation 2(11), 11–7 (2014)

    Google Scholar 

  26. M.R. Kumar, Y.S. Rao, Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition. Clust. Comput. 22(6), 13521–13531 (2019)

    Google Scholar 

  27. R. Kumar, I. Saini, Empirical wavelet transform based ECG signal compression. IETE J. Res. 60(6), 423–431 (2014)

    Google Scholar 

  28. Y. Kumar, M. Dewal, Complexity measures for normal and epileptic EEG signals using ApEn, SampEn and SEN. IJCCT 2(7), 6–12 (2011)

    Google Scholar 

  29. W. Liu, S. Cao, Y. Chen, Seismic time–frequency analysis via empirical wavelet transform. IEEE Geosci. Remote Sens. Lett. 13(1), 28–32 (2015)

    Google Scholar 

  30. F. Manzouri, S. Heller, M. Dümpelmann, P. Woias, A. Schulze-Bonhage, A comparison of machine learning classifiers for energy-efficient implementation of seizure detection. Front. Syst. Neurosci. 12, 43 (2018)

    Google Scholar 

  31. H. Ocak, Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36(2), 2027–2036 (2009)

    Google Scholar 

  32. P.R. Pal, R. Panda, Classification of EEG signals for epileptic seizure evaluation, in 2010 IEEE Students Technology Symposium (TechSym) (IEEE, 2010), pp. 72–76

  33. H. Peng, C. Lei, S. Zheng, C. Zhao, C. Wu, J. Sun, B. Hu, Automatic epileptic seizure detection via stein kernel-based sparse representation. Comput. Biol. Med. 132, 104338 (2021)

    Google Scholar 

  34. S. Raghu, N. Sriraam, Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms. Expert Syst. Appl. 113, 18–32 (2018)

    Google Scholar 

  35. M.M. Rahman, M.I.H. Bhuiyan, A.B. Das, Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking. Biomed. Signal Process. Control 50, 72–82 (2019)

    Google Scholar 

  36. S. Sanei, J.A. Chambers, EEG Signal Processing (John Wiley & Sons, Hoboken, NJ, 2013)

    Google Scholar 

  37. R.S. Selvakumari, M. Mahalakshmi, P. Prashalee, Patient-specific seizure detection method using hybrid classifier with optimized electrodes. J. Med. Syst. 43(5), 1–7 (2019)

    Google Scholar 

  38. P.O. Shafer, J.I. Sirven, Epilepsy Statistics (Epilepsy Foundation, Bowie, 2014)

    Google Scholar 

  39. M. Sharma, A. Dhere, R.B. Pachori, U.R. Acharya, An automatic detection of focal EEG signals using new class of time–frequency localized orthogonal wavelet filter banks. Knowl.-Based Syst. 118, 217–227 (2017)

    Google Scholar 

  40. M.K. Siddiqui, R. Morales-Menendez, X. Huang, N. Hussain, A review of epileptic seizure detection using machine learning classifiers. Brain Inform. 7, 1–18 (2020)

    Google Scholar 

  41. P. Singh, R.B. Pachori, Classification of focal and nonfocal EEG signals using features derived from Fourier-based rhythms. J. Mech. Med. Biol. 17(07), 1740002 (2017)

    Google Scholar 

  42. Y. Song, P. Liò et al., A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J. Biomed. Sci. Eng. 3(06), 556 (2010)

    Google Scholar 

  43. P. Swami, T.K. Gandhi, B.K. Panigrahi, M. Tripathi, S. Anand, A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst. Appl. 56, 116–130 (2016)

    Google Scholar 

  44. X. Wang, G. Gong, N. Li, S. Qiu, Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization. Front. Hum. Neurosci. 13, 52 (2019)

    Google Scholar 

  45. WHO Epilepsy. https://www.who.int/news-room/fact-sheets/detail/epilepsy(2021)

  46. F. Wilcoxon, Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)

    MathSciNet  Google Scholar 

  47. Y. Zhang, Y. Zhang, J. Wang, X. Zheng, Comparison of classification methods on EEG signals based on wavelet packet decomposition. Neural Comput. Appl. 26(5), 1217–1225 (2015)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science Engineering, Guru Nanak Dev University, Regional Campus, Gurdaspur, India

    Sandeep Singh

  2. Chandigarh and Assistant Professor (CSE), CSIR-CSIO, GNDU Regional Campus, Gurdaspur, India

    Harjot Kaur

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  1. Sandeep Singh
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  2. Harjot Kaur
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Correspondence to Sandeep Singh.

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Singh, S., Kaur, H. An Intelligent Method for Epilepsy Seizure Detection Based on Hybrid Nonlinear EEG Data Features Using Adaptive Signal Decomposition Methods. Circuits Syst Signal Process 42, 2782–2803 (2023). https://doi.org/10.1007/s00034-022-02223-z

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