Skip to main content
SpringerLink
Log in

A Rule-Based Classifier to Detect Seizures in EEG Signals

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In this study, we develop a rule-based method for the detection of seizures in electroencephalogram (EEG) signals. The proposed method is based on the observations that EEG seizure can be modeled either as a train of impulses or as the summation of harmonically related frequency-modulated chirps. For detecting spike-train-type seizures, the proposed method estimates group delay-based features, whereas to detect seizures modeled as the summation of harmonically related frequency-modulated chirps, the instantaneous frequency-related features are extracted. Experimental results indicate that the proposed method achieves better performance than the machine learning approach in terms of total accuracy and sensitivity.

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

Similar content being viewed by others

Data availability

We have used the database that is described in Boashash and Ouelha [7, 8] and was made available along with relevant codes at https://github.com/ElsevierSoftwareX/SOFTX-D-17-00059.

References

  1. U.R. Acharya, F. Molinari, S.V. Sree et al., Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 7(4), 401–408 (2012)

    Article  Google Scholar 

  2. O. Alcin, S. Siuly, V. Bajaj et al., Multi-category EEG signal classification developing time–frequency texture features based Fisher Vector encoding method. Neurocomputing 218, 251–258 (2016)

    Article  Google Scholar 

  3. M.A. Awal, S. Ouelha, S. Dong et al., A robust high-resolution time–frequency representation based on the local optimization of the short-time fractional Fourier transform. Digit. Signal Process. 70, 125–144 (2017)

    Article  Google Scholar 

  4. V. Bajaj, K. Rai, A. Kumar et al., Time–frequency image based features for classification of epileptic seizures from EEG signals. Biomed. Phys. Eng. Express 3, 015012 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. B. Boashash, S. Ouelha, Automatic signal abnormality detection using time–frequency features and machine learning: a newborn EEG seizure case study. Knowl. Based Syst. 106, 38–50 (2016). https://doi.org/10.1016/j.knosys.2016.05.027.www.sciencedirect.com/science/article/pii/S0950705116301174

    Article  Google Scholar 

  7. B. Boashash, S. Ouelha, Designing high-resolution time–frequency and time–scale distributions for the analysis and classification of non-stationary signals: a tutorial review with a comparison of features performance. Digit. Signal Process. 77, 120–152 (2018)

    Article  Google Scholar 

  8. B. Boashash, S. Ouelha, Efficient software platform TFSAP 7.1 and Matlab package to compute time–frequency distributions and related time–scale methods with extraction of signal characteristics. SoftwareX 8, 48–52 (2018)

    Article  Google Scholar 

  9. B. Boashash, H. Barki, S. Ouelha, Performance evaluation of time–frequency image feature sets for improved classification and analysis of non-stationary signals: application to newborn EEG seizure detection. Knowl. Based Syst. 132, 188–203 (2017). https://doi.org/10.1016/j.knosys.2017.06.015.www.sciencedirect.com/science/article/pii/S0950705117302897

    Article  Google Scholar 

  10. P. Celka, B. Boashash, P. Colditz, Preprocessing and time–frequency analysis of newborn EEG seizures. IEEE Eng. Med. Biol. Mag. 20(5), 30–39 (2001)

    Article  Google Scholar 

  11. O.K. Cura, A. Akan. Epileptic EEG classification by using advanced signal decomposition methods. In: Epilepsy (IntechOpen, 2020), pp. 1–20

  12. B. Hunyadi, A. Siekierska, J. Sourbron et al., Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy. J. Neurosci. Methods 287, 13–24 (2017)

    Article  Google Scholar 

  13. H. Kalbkhani, M.G. Shayesteh, Stockwell transform for epileptic seizure detection from EEG signals. Biomed. Signal Process. Control 38, 108–118 (2017)

    Article  Google Scholar 

  14. N. Khan, S. Ali, Classification of EEG signals using adaptive time–frequency distributions. Metrol. Meas. Syst. 23(2), 251–260 (2016)

    Article  Google Scholar 

  15. N.A. Khan, S. Ali, A new feature for the classification of non-stationary signals based on the direction of signal energy in the time–frequency domain. Comput. Biol. Med. 100, 10–16 (2018). https://doi.org/10.1016/j.compbiomed.2018.06.018.www.sciencedirect.com/science/article/pii/S0010482518301665

    Article  Google Scholar 

  16. N.A. Khan, S. Ali, A robust and efficient instantaneous frequency estimator of multi-component signals with intersecting time–frequency signatures. Signal Process. 177(107), 728 (2020)

    Google Scholar 

  17. N.A. Khan, S. Ali, K. Choi, An instantaneous frequency and group delay based feature for classifying EEG signals. Biomed. Signal Process. Control 67(102), 562 (2021). https://doi.org/10.1016/j.bspc.2021.102562.www.sciencedirect.com/science/article/pii/S1746809421001592

    Article  Google Scholar 

  18. N.A. Khan, S. Ali, K. Choi, Modified time–frequency marginal features for detection of seizures in newborns. Sensors 22(8), 3036 (2022). https://doi.org/10.3390/s22083036

    Article  Google Scholar 

  19. T. Kumar, V. Kanhangad, R.B. Pachori, Classification of seizure and seizure-free EEG signals using local binary patterns. Biomed. Signal Process. Control 15, 33–40 (2015)

    Article  Google Scholar 

  20. Y. Kumar, M. Dewal, R. Anand, Epileptic seizure detection using dwt based fuzzy approximate entropy and support vector machine. Neurocomputing 133, 271–279 (2014)

    Article  Google Scholar 

  21. S. Li, W. Zhou, Q. Yuan et al., Feature extraction and recognition of ictal EEG using EMD and SVM. Comput. Biol. Med. 43, 807–816 (2013)

    Article  Google Scholar 

  22. S. Madhavan, R.K. Tripathy, R.B. Pachori, Time–frequency domain deep convolutional neural network for the classification of focal and non-focal EEG signals. IEEE Sens. J. 20(6), 3078–3086 (2020). https://doi.org/10.1109/JSEN.2019.2956072

    Article  Google Scholar 

  23. J.D. Martínez-Vargas, J.I. Godino-Llorente, G. Castellanos-Dominguez, Time–frequency based feature selection for discrimination of non-stationary biosignals. EURASIP J. Adv. Signal Process. 1, 219 (2012)

    Article  Google Scholar 

  24. M. Mohammadi, N. Ali Khan, H. Hassanpour et al., Spike detection based on the adaptive time–frequency analysis. Circuits Syst. Signal Process. 39(11), 5656–5680 (2020)

    Article  Google Scholar 

  25. A. Sengur, Y. Guo, Y. Akbulut, Time–frequency texture descriptors of EEG signals for efficient detection of epileptic seizure. Brain Inform. 3, 101–108 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. N. Stevenson, J.O. Toole, L. Rankine et al., A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity. Med. Eng. Phys. 34(4), 437–446 (2012). https://doi.org/10.1016/j.medengphy.2011.08.001.www.sciencedirect.com/science/article/pii/S1350453311001998

    Article  Google Scholar 

  28. M.A. Tanveer, M.J. Khan, H. Sajid et al., Convolutional neural networks ensemble model for neonatal seizure detection. J. Neurosci. Methods 358(109), 197 (2021). https://doi.org/10.1016/j.jneumeth.2021.109197.www.sciencedirect.com/science/article/pii/S0165027021001321

    Article  Google Scholar 

  29. K.T. Tapani, C. Vanhatalo, N.J. Stevenson, Time-varying EEG correlations improve automated neonatal seizure detection. Int. J. Neural Syst 29(04), 1850030 (2019)

    Article  Google Scholar 

  30. Ö. Türk, M.S. Özerdem, Epilepsy detection by using scalogram based convolutional neural network from EEG signals. Brain Sci. 9(5), 115 (2019)

    Article  Google Scholar 

  31. A. Zahra, N. Kanwal, N. ur Rehman et al., Seizure detection from EEG signals using multivariate empirical mode decomposition. Comput. Biol. Med. 88, 132–141 (2017)

    Article  Google Scholar 

  32. W. Zeng, M. Li, C. Yuan et al., Classification of focal and non focal EEG signals using empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks. Artif. Intell. Rev. 52(1), 625–647 (2019)

    Article  Google Scholar 

Download references

Funding

The research was supported by internal research Grant of Foundation University Islamabad (No. FUI/ORIC/IFP-Grant#78). This work was also partly supported by the NRF Grant funded by the Korea government (MSIT) (No. 2021R1A2C1010370).

Author information

Authors and Affiliations

  1. GMV, Triump Road, Nottingham, Nottinghamshire, NG7 2TU, UK

    Nabeel Ali Khan

  2. Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Mosul Road, Erbil, Kurdistan Region, 10587, Iraq

    Mokhtar Mohammadi

  3. Department of Information and Communication Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongsan, 38541, Republic of Korea

    Kwonhue Choi

Authors
  1. Nabeel Ali Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mokhtar Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kwonhue Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kwonhue Choi.

Ethics declarations

Conflict of interest

None.

Code availability

The code to reproduce results is given in https://github.com/nabeelalikhan1/rule_based_classifier.

Additional information

Publisher's Note

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

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

Khan, N.A., Mohammadi, M. & Choi, K. A Rule-Based Classifier to Detect Seizures in EEG Signals. Circuits Syst Signal Process 42, 3538–3551 (2023). https://doi.org/10.1007/s00034-022-02281-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-022-02281-3

Keywords

Search

Navigation