Skip to main content
SpringerLink
Log in

Probing epileptic disorders with lightweight neural network and EEG's intrinsic geometry

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

The nonlinear dynamical systems can be stabilized on attractors in chaotic states, where the attractors depicted by dynamical trajectories may take on specific geometries. Electroencephalogram (EEG) signals are typically chaotic signals that have various nonlinear dynamic characteristics. Intrinsic geometry of EEG signals could contribute to tracking the recurrence of seizures and probing epileptic disorders, but it is ignored in most deep network-based seizure detection algorithms. Therefore, this paper presents an automatic detection framework called Recursive State-Space Neural Network (RSSNN) to infer the EEG geometry from single-channel signals and identify different epileptic patterns with a fast computational speed. RSSNN consists of a mathematical mapping module and a deep learning model. The former reconstructs EEG geometry in a high-dimensional state-space and maps it to a two-dimensional graph. The latter is a newly designed lightweight (0.68 MB) fully convolutional network that decodes geometry into brain states. We validated RSSNN on a public EEG dataset collected from epileptic patients with seizure and seizure-free conditions and healthy volunteers. A sliding window with a one-second length is utilized to verify the performance of RSSNN at the segment level. Moreover, the voting strategy is adopted to obtain the final prediction at the subject level. In the testing phase, RSSNN obtains an overall 99.79% accuracy at the EEG segment level and reaches 100% accuracy at the subject level. Notably, it takes less than 25 ms to predict one subject. This study proves the potential of EEG's intrinsic geometry as a seizure indicator for real-time monitoring by combining it with a lightweight neural network. It enriches the deep learning-based seizure prediction methodology in nonlinear dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The datasets generated during and analyzed during the current study are available in the "Klinik fűr Epileptologie, Universität Bonn" repository (http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3&changelang=3).

References

  1. Milligan, T.A.: Epilepsy: a clinical overview. Am. J. Med. 134, 840–847 (2021)

    Article  Google Scholar 

  2. Beniczky, S., Arbune, A.A., Jeppesen, J., Ryvlin, P.: Biomarkers of seizure severity derived from wearable devices. Epilepsia 61, 61–66 (2020)

    Article  Google Scholar 

  3. Nasseri, M., et al.: Non-invasive wearable seizure detection using long-short-term memory networks with transfer learning. J. Neural Eng. 18, 056017 (2021)

    Article  Google Scholar 

  4. Beniczky, S., Ryvlin, P.: Standards for testing and clinical validation of seizure detection devices. Epilepsia 59, 9–13 (2018)

    Article  Google Scholar 

  5. Craik, A., He, Y.T., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16, 031001 (2019)

    Article  Google Scholar 

  6. Widge, A.S., Moritz, C.T.: Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface. J. Neural Eng. 11, 024001 (2014)

    Article  Google Scholar 

  7. Lotte, F., et al.: A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J. Neural Eng. 15, 031005 (2018)

    Article  Google Scholar 

  8. Liu, X., Richardson, A.G.: Edge deep learning for neural implants: a case study of seizure detection and prediction. J. Neural Eng. 18, 046034 (2021)

    Article  Google Scholar 

  9. Dumpelmann, M.: Early seizure detection for closed loop direct neurostimulation devices in epilepsy. J. Neural Eng. 16, 041001 (2019)

    Article  Google Scholar 

  10. Orhan, U., Hekim, M., Ozer, M.: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38, 13475–13481 (2011)

    Article  Google Scholar 

  11. Gnatkovsky, V., et al.: Biomarkers of epileptogenic zone defined by quantified stereo-EEG analysis. Epilepsia 55, 296–305 (2014)

    Article  Google Scholar 

  12. Acharya, U.R., Fujita, H., Sudarshan, V.K., Bhat, S., Koh, J.E.W.: Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowledge-Based Syst. 88, 85–96 (2015)

    Article  Google Scholar 

  13. Namazi, H., Kulish, V.V.: Fractional diffusion based modelling and prediction of human brain response to external stimuli. Comput. Math. Methods Med. 2015, 1 (2015)

    MathSciNet  MATH  Google Scholar 

  14. Samiee, K., Kovacs, P., Gabbouj, M.: Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform. IEEE Trans. Biomed. Eng. 62, 541–552 (2015)

    Article  Google Scholar 

  15. Riaz, F., Hassan, A., Rehman, S., Niazi, I.K., Dremstrup, K.: EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 28–35 (2016)

    Article  Google Scholar 

  16. Arunkumar, N., et al.: Classification of focal and non focal EEG using entropies. Pattern Recog. Lett. 94, 112–117 (2017)

    Article  Google Scholar 

  17. Bhattacharyya, A., Pachori, R.B.: A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform. IEEE Trans. Biomed. Eng. 64, 2003–2015 (2017)

    Article  Google Scholar 

  18. Alickovic, E., Kevric, J., Subasi, A.: Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed. Signal Process. Control 39, 94–102 (2018)

    Article  Google Scholar 

  19. Subasi, A., Kevric, J., Canbaz, M.A.: Epileptic seizure detection using hybrid machine learning methods. Neural Comput. Appl. 31, 317–325 (2019)

    Article  Google Scholar 

  20. Acharya, U.R., et al.: Characterization of focal EEG signals: a review. Future Gener. Comput. Syst. Int. J. Esci. 91, 290–299 (2019)

    Article  Google Scholar 

  21. Al Ghayab, H.R., Li, Y., Siuly, S., Abdulla, S.: Epileptic seizures detection in EEGs blending frequency domain with information gain technique. Soft. Comput. 23, 227–239 (2019)

    Article  Google Scholar 

  22. Toth, E., et al.: Machine learning approach to detect focal-onset seizures in the human anterior nucleus of the thalamus. J. Neural Eng. 17, 066004 (2020)

    Article  Google Scholar 

  23. Le Trung, T., et al.: Multi-channel EEG epileptic spike detection by a new method of tensor decomposition. J. Neural Eng. 17, 016023 (2020)

    Article  Google Scholar 

  24. Zabihi, M., Kiranyaz, S., Jantti, V., Lipping, T., Gabbouj, M.: Patient-specific seizure detection using nonlinear dynamics and nullclines. IEEE J. Biomed. Health Inform. 24, 543–555 (2020)

    Article  Google Scholar 

  25. Sharma, R., Pachori, R.B., Sircar, P.: Seizures classification based on higher order statistics and deep neural network. Biomed. Signal Process. Control 59, 101921 (2020)

    Article  Google Scholar 

  26. Sharma, M., Pachori, R.B., Acharya, U.R.: A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recog. Lett. 94, 172–179 (2017)

    Article  Google Scholar 

  27. Li, Q., Gao, J.B., Huang, Q., Wu, Y., Xu, B.: Distinguishing epileptiform discharges from normal electroencephalograms using scale-dependent Lyapunov exponent. Front. Bioeng. Biotechnol. 8, 1006 (2020)

    Article  Google Scholar 

  28. Wulsin, D.F., Gupta, J.R., Mani, R., Blanco, J.A., Litt, B.: Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement. J. Neural Eng. 8, 036015 (2011)

    Article  Google Scholar 

  29. Ullah, I., Hussain, M., Aboalsamh, H.: An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst. Appl. 107, 61–71 (2018)

    Article  Google Scholar 

  30. San-Segundo, R., Gil-Martin, M., D’Haro-Enriquez, L.F., Pardo, J.M.: Classification of epileptic EEG recordings using signal transforms and convolutional neural networks. Comput. Biol. Med. 109, 148–158 (2019)

    Article  Google Scholar 

  31. Yildirim, O., Baloglu, U.B., Acharya, U.R.: A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput. Appl. 32, 15857–15868 (2020)

    Article  Google Scholar 

  32. Geng, D., et al.: Deep learning for robust detection of interictal epileptiform discharges. J. Neural Eng. 18, 056015 (2021)

    Article  Google Scholar 

  33. Vidyaratne, L.S. et al.: Deep cellular recurrent network for efficient analysis of time-series data with spatial information. IEEE Trans. on Neural Netw. Learn. Syst., Early Access (2021)

  34. Tsiouris, K.M., et al.: A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput. Biol. Med. 99, 24–37 (2018)

    Article  Google Scholar 

  35. Ozcan, A.R., Erturk, S.: Seizure prediction in scalp EEG using 3D convolutional neural networks with an image-based approach. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 2284–2293 (2019)

    Article  Google Scholar 

  36. Lian, Q., Qi, Y., Pan, G., Wang, Y.: Learning graph in graph convolutional neural networks for robust seizure prediction. J. Neural Eng. 17, 035004 (2020)

    Article  Google Scholar 

  37. Li, Y. et al. Spatio-temporal-spectral hierarchical graph convolutional network with semisupervised active learning for patient-specific seizure prediction. IEEE Trans. Cybern. PP (2021).

  38. Shoeibi, A., et al.: A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals. Expert Syst. Appl. 163, 113788 (2021)

    Article  Google Scholar 

  39. Lashkari, S., Sheikhani, A., Hashemi Golpayegan, M.R., Moghimi, A., Kobravi, H.R.: Topological feature extraction of nonlinear signals and trajectories and its application in EEG signals classification. Turk. J. Electr. Eng. Comput. Sci. 26, 1329–1342 (2018)

    Google Scholar 

  40. Sayed, K., Kamel, M., Alhaddad, M., Malibary, H.M., Kadah, Y.M.: Characterization of phase space trajectories for Brain-Computer Interface. Biomed. Signal Process. Control 38, 55–66 (2017)

    Article  Google Scholar 

  41. Jirsa, V.K., Stacey, W.C., Quilichini, P.P., Ivanov, A.I., Bernard, C.: On the nature of seizure dynamics. Brain 137, 2210–2230 (2014)

    Article  Google Scholar 

  42. Andrzejak, R.G., et al.: 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 (2001). https://doi.org/10.1103/PhysRevE.64.061907

    Article  Google Scholar 

  43. Binnie, C.D., Stefan, H.: Modern electroencephalography: its role in epilepsy management. Clin. Neurophysiol. 110, 1671–1697 (1999)

    Article  Google Scholar 

  44. Ugawa, Y., et al.: Clinical Practice Guidelines for Epilepsy 2018. Igaku-Shoin Ltd., Tokyo (2018)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Nabil, D., Benali, R., Reguig, F.B.: Epileptic seizure recognition using EEG wavelet decomposition based on nonlinear and statistical features with support vector machine classification. Biomed. Eng. Biomed. Tech. 65, 133–148 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  49. Oliva, J.T., Rosa, J.L.G.: Binary and multiclass classifiers based on multitaper spectral features for epilepsy detection. Biomed. Signal Process. Control 66, 102469 (2021)

    Article  Google Scholar 

  50. Yavuz, E., Kasapbasi, M.C., Eyupoglu, C., Yazici, R.: An epileptic seizure detection system based on cepstral analysis and generalized regression neural network. Biocybern Biomed Eng 38, 201–216 (2018)

    Article  Google Scholar 

  51. Anuragi, A., Singh Sisodia, D., Pachori, R.B.: Epileptic-seizure classification using phase-space representation of FBSE-EWT based EEG sub-band signals and ensemble learners. Biomed. Signal Process. Control 71, 103138 (2022)

    Article  Google Scholar 

  52. Hassan, A.R., Subasi, A., Zhang, Y.C.: Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowledge-Based Syst. 191, 105333 (2020)

    Article  Google Scholar 

  53. Hu, J., Shen, L., Sun, G. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7132–7141 (IEEE)

  54. Baudot, P., Tapia, M., Bennequin, D., Goaillard, J.-M.: Topological information data analysis. Entropy 21, 869 (2019)

    Article  MathSciNet  Google Scholar 

  55. Zbilut, J.P., Marwan, N.: The Wiener-Khinchin theorem and recurrence quantification. Phys. Lett. A 372, 6622–6626 (2008)

    Article  MATH  Google Scholar 

  56. Vergara, J.R., Estevez, P.A.: A review of feature selection methods based on mutual information. Neural Comput. Appl. 24, 175–186 (2014)

    Article  Google Scholar 

  57. Khademi, S., Hendriks, R.C., Kleijn, W.B.: Intelligibility enhancement based on mutual information. Ieee-Acm T Audio Spe 25, 1694–1708 (2017)

    Google Scholar 

  58. Krakovská, A., Mezeiová, K., Budáčová, H.: Use of false nearest neighbours for selecting variables and embedding parameters for state space reconstruction. J. Complex Syst 2015, 932750 (2015)

    Google Scholar 

  59. Cao, L.: Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110, 43–50 (1997)

    Article  MATH  Google Scholar 

  60. Theiler, J.: Some comments on the correlation dimension of 1/fα noise. Phys. Lett. A 155, 480–493 (1991)

    Article  MathSciNet  Google Scholar 

  61. Zou, Y., Donner, R.V., Marwan, N., Donges, J.F., Kurths, J.: Complex network approaches to nonlinear time series analysis. Phys. Rep. Rev. Sect. Phys. Lett. 787, 1–97 (2019)

    MathSciNet  Google Scholar 

  62. Webber, C.L., Zbilut, J.P.: Recurrence quantifications: feature extractions from recurrence plots. Int. J. Bifurc. Chaos 17, 3467–3475 (2007)

    Article  MATH  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (62071324) and the Natural Science Foundation of Tianjin (19JCQNJC01200, 19JCZDJC36500).

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 30072, China

    Zhenxi Song, Bin Deng, Yulin Zhu, Lihui Cai, Jiang Wang & Guosheng Yi

Authors
  1. Zhenxi Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yulin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lihui Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guosheng Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guosheng Yi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Figure 

Fig. 10
figure 10

The intrinsic geometry of Lorenz, Roessler, and Henon systems

Full size image

10 illustrates examples of classically chaotic systems with different underlying geometric inherence features. Each row represents the Lorenz, Roessler, and Henon systems, respectively. Each column indicates the state-space reconstructed under the different scales of time delay. By observing the intrinsic geometry of those chaotic systems, we proposed the hypotheses of this study (see Introduction) and developed the RSSNN method.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, Z., Deng, B., Zhu, Y. et al. Probing epileptic disorders with lightweight neural network and EEG's intrinsic geometry. Nonlinear Dyn 111, 5817–5832 (2023). https://doi.org/10.1007/s11071-022-08118-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-022-08118-7

Keywords

Search

Navigation