Skip to main content
SpringerLink
Log in

A novel multivariate approach for the detection of epileptic seizure using BCS-WELM

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

This paper proposes a novel weighted extreme learning machine (WELM) classifier using binary cuckoo search (BCS) optimization algorithm for a fast and efficient detection of the epileptic seizure and seizure-free epochs exploiting simple temporal features. The proposed WELM based model assigns variable weights to different classes (seizure and non-seizure) to handle the biased-data problem generally found in EEG data. Moreover, BCS optimization algorithm is incorporated to choose good features for the improvement of the accuracy of the model as well as to reduce the classification time. A comparative study of the suggested model with three benchmark classifiers [Extreme learning machine (ELM), Support vector machine (SVM), and K-Nearest Neighbours (KNN)] is shown using a popular publicly available database: CHB-MIT Scalp EEG database. The average accuracy, sensitivity, and specificity of the proposed model are 99.06%, 97.97%, and 99.6% respectively. The experimental results indicate that the suggested method has a very low false prediction rate and it outperforms existing state of art methods. Hence, this developed approach has proved itself as a fast and robust patient-specific model to detect epileptic seizure and seizure-free epochs.

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
Fig. 10

Similar content being viewed by others

Availability of data and material

The authors admit that the data supporting the study conclusions are included in this article,

Code availability (software application or custom code)

Not applicable.

References

  1. Furbass F, Ossenblok P, Hartmann M, Perko H, Skupch A, Lindinger G, Elezi L, Pataraia E, Colon A, Baumgartner C et al (2015) Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units. Clin Neurophysiol 126(6):1124–1131

    Article  Google Scholar 

  2. Samiee K, Kiranyaz S, Gabbouj M, Saramaki T (2015) Long-term epileptic eeg classification via 2d mapping and textural features. Expert Syst Appl 42(20):7175–7185

    Article  Google Scholar 

  3. Zabihi M, Kiranyaz S, Rad AB, Katsaggelos AK, Gabbouj M, Ince T (2015) Analysis of high-dimensional phase space via poincare section for patient-specific seizure detection. IEEE Trans Neural Syst Rehabil Eng 24(3):386–398

    Article  Google Scholar 

  4. Bhattacharyya A, Pachori RB (2017) A multivariate approach for patient-specific eeg seizure detection using empirical wavelet transform. IEEE Trans Biomed Eng 64(9):2003–2015

    Article  Google Scholar 

  5. Fan M, Chou CA (2018) Detecting abnormal pattern of epileptic seizures via temporal synchronization of eeg signals. IEEE Trans Biomed Eng 66(3):601–608

    Article  Google Scholar 

  6. Hossain MS, Amin SU, Alsulaiman M, Muhammad G (2019) Applying deep learning for epilepsy seizure detection and brain mapping visualization. ACM Trans Multimed Comput Commun Appl (TOMM) 15(1):1–17

    Google Scholar 

  7. Wu D, Wang Z, Jiang L, Dong F, Wu X, Wang S, Ding Y (2019) Automatic epileptic seizures joint detection algorithm based on improved multi-domain feature of ceeg and spike feature of aeeg. IEEE Access 7:41551–41564

    Article  Google Scholar 

  8. Chen Z, Lu G, Xie Z, Shang W (2020) A unified framework and method for eeg-based early epileptic seizure detection and epilepsy diagnosis. IEEE Access 8:20080–20092

    Article  Google Scholar 

  9. Chakraborti S, Choudhary A, Singh A, Kumar R, Swetapadma A (2018) A machine learning based method to detect epilepsy. Int J Inf Technol 10(3):257–263

    Google Scholar 

  10. 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):222

    Article  Google Scholar 

  11. Li Y, Cui WG, Huang H, Guo YZ, Li K, Tan T (2019) Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach. Knowl-Based Syst 164:96–106

    Article  Google Scholar 

  12. Sarwar A, Ali M, Manhas J, Sharma V (2020) Diagnosis of diabetes type-II using hybrid machine learning based ensemble model. Int J Inf Technol 12(2):419–428

    Google Scholar 

  13. Sharma N, Mangla M, Mohanty SN, Pattanaik CR (2021) Employing stacked ensemble approach for time series forecasting. Int J Inf Technol 13(5):2075–2080

    Google Scholar 

  14. Chen L-L, Zhang J, Zou J-Z, Zhao C-J, Wang G-S (2014) A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection. Biomed Signal Process Control 10:1–10

    Article  Google Scholar 

  15. Paul Y (2018) Various epileptic seizure detection techniques using biomedical signals: a review. Brain Inf 5(2):1–19

    Article  Google Scholar 

  16. JolliHe I (1986) “Principal component analysis. Springer, New York

    Book  Google Scholar 

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

    Article  Google Scholar 

  18. Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World Congress on nature & biologically inspired computing (NaBIC), pp 210–214, IEEE, 2009

  19. Mullai A, Mani K (2021) Enhancing the security in RSA and elliptic curve cryptography based on addition chain using simplified Swarm Optimization and Particle Swarm Optimization for mobile devices. Int J Inf Technol 13(2):551–564

    Google Scholar 

  20. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  21. Zong W, Huang G-B, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229–242

    Article  Google Scholar 

  22. EEG Database, CHB-MIT Scalp EEG Database. https://physionet.org/content/chbmit/1.0.0/ Accessed 20 Dec 2020.

  23. Chang N-F, Chen T-C, Chiang C-Y, Chen L-G (2012) Channel selection for epilepsy seizure prediction method based on machine learning. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 5162–5165, IEEE, 2012

  24. Lestari FP, Haekal M, Edison RE, Fauzy FR, Khotimah SN, Haryanto F (2020) Epileptic seizure detection in EEGs by using random tree forest, Naïve Bayes and KNN classification. J Phys Conf Ser 1505:012055 (IOP Publishing)

    Article  Google Scholar 

  25. Tzallas AT, Tsipouras MG, Tsalikakis DG, Karvounis EC, Astrakas L, Konitsiotis S, Tzaphlidou M (2012) Automated epileptic seizure detection methods: a review study. Epilepsy–Histol Electroencephalogr Psychol Aspects, pp 2027–2036

  26. Abbasi B, Goldenholz DM (2019) Machine learning applications in epilepsy. Epilepsia 60(10):2037–2047

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. School of Electronics Engineering, KIIT Deemed to be University, BBSR, Bhubaneswar, India

    Priya Das & Sarita Nanda

Authors
  1. Priya Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarita Nanda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarita Nanda.

Ethics declarations

Conflicts of interest

There are no conflicts of interest declared by the authors.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication statements

This research paper is used for publication after the sole consent of all the authors.

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

Das, P., Nanda, S. A novel multivariate approach for the detection of epileptic seizure using BCS-WELM. Int. j. inf. tecnol. 15, 149–159 (2023). https://doi.org/10.1007/s41870-022-01126-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-022-01126-1

Keywords

Search

Navigation