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Enhanced Epileptic Seizure Detection Through Graph Spectral Analysis of EEG Signals

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Abstract

Epilepsy is a persistent health condition marked by unusual and highly synchronized electrical activity in the brain cells, resulting in recurring seizures. This paper proposes a novel real-time method to improve the detection of seizures using the spectral features of non-stationary electroencephalogram (EEG) signals. It is observed that the discrete wavelet transform (DWT)-based features do not consider the interrelationship among EEG signal components. This interrelationship has been well captured by the novel representation of EEG in the form of graph signals. Here, the spectral analysis of the graph signals is investigated by the graph-based Fourier transform (GFT). Then, GFT-based features have been selected and fed into different classifiers for analysis. The seizure detection rate in two publicly available EEG-based datasets, the University of Bonn (UB) and the Neurology Sleep Clinic New Delhi (NSC-ND), have been achieved with accuracy of 98.68% and 96.84%, respectively. The accuracy achieved is significantly better than the existing state-of-the-art techniques. This approach demonstrates the impact of utilizing the interrelationship among the EEG components, followed by enhanced feature selection based on GFT for the improved detection of seizures.

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Publicly available data has been referred to in this paper.

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. U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, H. Adeli, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2018)

    Google Scholar 

  3. B. Akbarian, A. Erfanian, A framework for seizure detection using effective connectivity, graph theory, and multi-level modular network. Biomed. Signal Process. Control 59, 101878 (2020)

    Google Scholar 

  4. R.G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, C.E. Elger, 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 64(6), 061907 (2001)

    Google Scholar 

  5. A. Anuragi, D.S. Sisodia, R.B. Pachori, Automated fbse-ewt based learning framework for detection of epileptic seizures using time-segmented EEG signals. Comput. Biol. Med. 136, 104708 (2021)

    Google Scholar 

  6. S.M. Beeraka, A. Kumar, M. Sameer, S. Ghosh, B. Gupta, Accuracy enhancement of epileptic seizure detection: a deep learning approach with hardware realization of stft. Circuits Syst. Signal Process. 41, 461–484 (2022)

    Google Scholar 

  7. W. Bomela, S. Wang, C.-A. Chou, J.-S. Li, Real-time inference and detection of disruptive EEG networks for epileptic seizures. Sci. Rep. 10(1), 8653 (2020)

    Google Scholar 

  8. M. Chakraborty, D. Mitra et al., Epilepsy seizure detection using kurtosis based vmd’s parameters selection and bandwidth features. Biomed. Signal Process. Control 64, 102255 (2021)

    Google Scholar 

  9. G. Chen, Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features. Expert Syst. Appl. 41(5), 2391–2394 (2014)

    Google Scholar 

  10. T. Chen and C. Guestrin. Xgboost: A scalable tree boosting system, in proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, (San Francisco, California, 2016) pp. 785–794

  11. M. Cheung, J. Shi, O. Wright, L.Y. Jiang, X. Liu, J.M. Moura, Graph signal processing and deep learning: convolution, pooling, and topology. IEEE Signal Process. Mag. 37(6), 139–149 (2020)

    Google Scholar 

  12. N. Darjani, H. Omranpour, Phase space elliptic density feature for epileptic EEG signals classification using metaheuristic optimization method. Knowl.-Based Syst. 205, 106276 (2020)

    Google Scholar 

  13. M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural. Inf. Process. Syst. 29 (2016)

  14. N. Fatma, P. Singh, and M. K. Siddiqui. An in-depth examination of machine learning approaches for detecting epileptic seizures, in 2024 International Conference on Automation and Computation (AUTOCOM), (IEEE, 2024) pp. 507–511

  15. P. Ghaderyan, A. Abbasi, M.H. Sedaaghi, An efficient seizure prediction method using knn-based undersampling and linear frequency measures. J. Neurosci. Methods 232, 134–142 (2014)

    Google Scholar 

  16. V. Gupta, R.B. Pachori, Epileptic seizure identification using entropy of fbse based EEG rhythms. Biomed. Signal Process. Control 53, 101569 (2019)

    Google Scholar 

  17. U. Herwig, P. Satrapi, C. Schönfeldt-Lecuona, Using the international 10–20 EEG system for positioning of transcranial magnetic stimulation. Brain Topogr. 16, 95–99 (2003)

    Google Scholar 

  18. M.-P. Hosseini, A. Hosseini, K. Ahi, A review on machine learning for EEG signal processing in bioengineering. IEEE Rev. Biomed. Eng. 14, 204–218 (2020)

    Google Scholar 

  19. M. Hossin, M.N. Sulaiman, A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 5(2), 1 (2015)

    Google Scholar 

  20. W. Hu, J. Cao, X. Lai, J. Liu, Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks. J. Ambient Intell. Hum. Comput. 14(11), 15485–15495 (2019)

    Google Scholar 

  21. S. Jang, S.-E. Moon, and J.-S. Lee. EEG-based video identification using graph signal modeling and graph convolutional neural network, in 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), (IEEE, 2018), pp. 3066–3070

  22. K. Jindal, R. Upadhyay, H.S. Singh, Application of tunable-q wavelet transform based nonlinear features in epileptic seizure detection. Analog Integr. Circ. Sig. Process 100, 437–452 (2019)

    Google Scholar 

  23. J. Jing, X. Pang, Z. Pan, F. Fan, Z. Meng, Classification and identification of epileptic EEG signals based on signal enhancement. Biomed. Signal Process. Control 71, 103248 (2022)

    Google Scholar 

  24. V. Joshi, R.B. Pachori, A. Vijesh, Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed. Signal Process. Control 9, 1–5 (2014)

    Google Scholar 

  25. A.A. Khalil, M.I. El Sayeid, F.E. Ibrahim, A.A. Khalaf, E. Gemeay, H. Kasem, S.E.A. Khamis, G.M. El-Banby, W. El-Shafai, E.-S.M. El-Rabaie et al., Efficient frameworks for statistical seizure detection and prediction. J. Supercomput. 79(16), 17824–17858 (2023)

    Google Scholar 

  26. A. Khosla, P. Khandnor, T. Chand, A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybern. Biomed. Eng. 40(2), 649–690 (2020)

    Google Scholar 

  27. R. Li, X. Yuan, M. Radfar, P. Marendy, W. Ni, T.J. O’Brien, P.M. Casillas-Espinosa, Graph signal processing, graph neural network and graph learning on biological data: a systematic review. IEEE Rev. Biomed. Eng. 16, 109–135 (2021)

    Google Scholar 

  28. Y. Liang, C. Chen, F. Li, D. Yao, P. Xu, L. Yu et al., Altered functional connectivity after epileptic seizure revealed by scalp EEG. Neural Plast. (2020). https://doi.org/10.1155/2020/8851415

    Article  Google Scholar 

  29. J. Mateo-Sotos, A. Torres, J. Santos, O. Quevedo, C. Basar, A machine learning-based method to identify bipolar disorder patients. Circuits Syst. Signal Process. 41(4), 2244–2265 (2022)

    Google Scholar 

  30. P. Mathur and V. K. Chakka. Graph signal processing of EEG signals for detection of epilepsy, in 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), (IEEE, 2020), pp. 839–843

  31. M. Nourelahi, A. Zamani, A. Talei, S. Tahmasebi, A model to predict breast cancer survivability using logistic regression. Middle East J. Cancer 10(2), 132–138 (2019)

    Google Scholar 

  32. M. Omidvar, A. Zahedi, H. Bakhshi, EEG signal processing for epilepsy seizure detection using 5-level db4 discrete wavelet transform, ga-based feature selection and ann/svm classifiers. J. Ambient Intell. Hum. Comput. 12(11), 10395–10403 (2021)

    Google Scholar 

  33. R.J. Oweis, E.W. Abdulhay, Seizure classification in EEG signals utilizing Hilbert–Huang transform. Biomed. Eng. Online 10, 1–15 (2011)

    Google Scholar 

  34. 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 

  35. S. Poorani, P. Balasubramanie, Seizure detection based on EEG signals using asymmetrical back propagation neural network method. Circuits Syst. Signal Process. 40(9), 4614–4632 (2021)

    Google Scholar 

  36. S.M. Qaisar, S.F. Hussain, Effective epileptic seizure detection by using level-crossing EEG sampling sub-bands statistical features selection and machine learning for mobile healthcare. Comput. Methods Programs Biomed. 203, 106034 (2021)

    Google Scholar 

  37. X. Qin, D. Xu, X. Dong, X. Cui, S. Zhang, EEG signal classification based on improved variational mode decomposition and deep forest. Biomed. Signal Process. Control 83, 104644 (2023)

    Google Scholar 

  38. K. Rasheed, A. Qayyum, J. Qadir, S. Sivathamboo, P. Kwan, L. Kuhlmann, T. O’Brien, A. Razi, Machine learning for predicting epileptic seizures using EEG signals: a review. IEEE Rev. Biomed. Eng. 14, 139–155 (2020)

    Google Scholar 

  39. E.A. Refaee, S. Shamsudheen, A computing system that integrates deep learning and the internet of things for effective disease diagnosis in smart health care systems. J. Supercomput. 78(7), 9285–9306 (2022)

    Google Scholar 

  40. S.K. Rout, P.K. Biswal, An efficient error-minimized random vector functional link network for epileptic seizure classification using vmd. Biomed. Signal Process. Control 57, 101787 (2020)

    Google Scholar 

  41. S. Rukmawan, F. Aszhari, Z. Rustam, J. Pandelaki, Cerebral infarction classification using the k-nearest neighbor and naive Bayes classifier. J. Phys. Conf. Ser. 1752, 012045 (2021)

    Google Scholar 

  42. J. Saeedi, K. Faez, M.H. Moradi, Hybrid fractal-wavelet method for multi-channel EEG signal compression. Circuits Syst. Signal Process. 33, 2583–2604 (2014)

    Google Scholar 

  43. N. Sairamya, S.T. George, D.N. Ponraj, M. Subathra, Detection of epileptic eeg signal using improved local pattern transformation methods. Circuits Syst. Signal Process. 37, 5554–5575 (2018)

    Google Scholar 

  44. M. Savadkoohi, T. Oladunni, L. Thompson, A machine learning approach to epileptic seizure prediction using electroencephalogram EEG signal. Biocybern. Biomed. Eng. 40(3), 1328–1341 (2020)

    Google Scholar 

  45. G. Sharma, A. Parashar, A.M. Joshi, DepHNN: a novel hybrid neural network for electroencephalogram EEG-based screening of depression. Biomed. Signal Process. Control 66, 102393 (2021)

    Google Scholar 

  46. M. Shen, P. Wen, B. Song, Y. Li, An EEG based real-time epilepsy seizure detection approach using discrete wavelet transform and machine learning methods. Biomed. Signal Process. Control 77, 103820 (2022)

    Google Scholar 

  47. D.I. Shuman, S.K. Narang, P. Frossard, A. Ortega, P. Vandergheynst, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83–98 (2013)

    Google Scholar 

  48. T. Song, W. Zheng, P. Song, Z. Cui, EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. 11(3), 532–541 (2018)

    Google Scholar 

  49. L. Stanković, M. Daković, E. Sejdić, Introduction to graph signal processing, in Vertex-frequency analysis of graph signals. ed. by L. Stanković, M. Daković, E. Sejdić (Springer, Berlin, 2019), pp.3–108

    Google Scholar 

  50. 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 

  51. 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 

  52. P. Swami, B. Panigrahi, S. Nara, M. Bhatia, T. Gandhi. EEG epilepsy datasets. (2016c). https://doi. org/10.13140/RG,2(14280.32006)

  53. E. Tuncer, E.D. Bolat, Classification of epileptic seizures from electroencephalogram EEG data using bidirectional short-term memory (bi-lstm) network architecture. Biomed. Signal Process. Control 73, 103462 (2022)

    Google Scholar 

  54. J. Wu, T. Zhou, T. Li, Detecting epileptic seizures in EEG signals with complementary ensemble empirical mode decomposition and extreme gradient boosting. Entropy 22(2), 140 (2020)

    Google Scholar 

  55. W. Zhao, W. Zhao, W. Wang, X. Jiang, X. Zhang, Y. Peng, B. Zhang, G. Zhang, A novel deep neural network for robust detection of seizures using EEG signals. Comput. Math. Methods Med. (2020). https://doi.org/10.1155/2020/9689821

    Article  Google Scholar 

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  1. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India

    Ramnivas Sharma & Hemant Kumar Meena

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  1. Ramnivas Sharma
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Ramnivas Sharma: Original draft, Software, Review and editing of the paper, Formal analysis, and Results obtained.

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Sharma, R., Meena, H.K. Enhanced Epileptic Seizure Detection Through Graph Spectral Analysis of EEG Signals. Circuits Syst Signal Process 43, 5288–5308 (2024). https://doi.org/10.1007/s00034-024-02715-0

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