SpringerLink
Log in

A Comparison of Classifiers for Epileptic Seizure Prediction Based on Heart Rate Variability

  • Conference paper
  • First Online:
IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering (CLAIB 2022, CBEB 2022)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 99))

  • 84 Accesses

Abstract

Epilepsy is a neurological disorder characterized by recurrent seizures due to abnormal discharges in cortical networks of the brain. A seizure prediction method with a low false-positive rate in a high confidence interval and without side effects may improve patients’ quality of life. Heart rate variability (HRV) analysis is among the most promising approaches for seizure prediction. This method indirectly assesses the behavior of the autonomic nervous system (ANS) through cardiac rhythm activity. Artificial intelligence (AI) classifiers may predict seizures and distinguish the different phases in ECG signals. This work evaluated several classifiers for seizure prediction and studied them in terms of computational cost for training, sensitivity, accuracy, false-positive rate (FPR), and their suitability for wearable applications using the HRV approach. Relied on the results, the Support Vector Classifier (SVC) obtained the best set of scores, including the highest accuracy, 97.57%, as well as the second-highest Sen, Spe, and NPV scores, 97.70%, 97.51%, and 98.83%, respectively for preictal periods, considering an evaluation of 14.08 h from six patients’ ECG data.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 192.59
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 246.09
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. World Health Organization: Epilepsy. https://www.who.int/en/news-room/fact-sheets/detail/epilepsy. Accessed 25 Apr 2022

  2. Jeppesen, J., Beniczky, S., Johansen, P., Sidenius, P., Fuglsang-Frederiksen, A.: Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot. Seizure 24, 1–7 (2015). https://doi.org/10.1016/j.seizure.2014.11.004

  3. Melo, H., et al.: Ultra-short heart rate variability reliability for cardiac autonomic tone assessment in mesial temporal lobe epilepsy. Epilepsy Res. 174, 106662 (2021). https://doi.org/10.1016/j.eplepsyres.2021.106662

  4. Truong, N.D., et al.: Seizure susceptibility prediction in uncontrolled epilepsy. Front. Neurol. 12 (2021). https://doi.org/10.3389/fneur.2021.721491. https://www.frontiersin.org/article/10.3389/fneur.2021.721491. ISSN 1664-2295

  5. Pavei, J., et al.: Early seizure detection based on cardiac autonomic regulation dynamics. Front. Physiol. 8 (2017). https://doi.org/10.3389/fphys.2017.00765. https://www.frontiersin.org/article/10.3389/fphys.2017.00765. ISSN 1664-042X

  6. Bhattacharya, A., Baweja, T., Karri, S.P.K.: Epileptic seizure prediction using deep transformer model. Int. J. Neural Syst. 32(02), 2150058 (2022). https://doi.org/10.1142/S0129065721500581. PMID 34720065

  7. Jacobs, D., Hilton, T., del Campo, M., Carlen, P.L., Bardakjian, B.L.: Classification of pre-clinical seizure states using scalp EEG cross-frequency coupling features. IEEE Trans. Biomed. Eng. 65(11), 2440–2449 (2018). https://doi.org/10.1109/TBME.2018.2797919

    Article  Google Scholar 

  8. Dono, F., et al.: Interictal heart rate variability analysis reveals lateralization of cardiac autonomic control in temporal lobe epilepsy. Front. Neurol. 11 (2020). https://doi.org/10.3389/fneur.2020.00842. https://www.frontiersin.org/article/10.3389/fneur.2020.00842. ISSN 1664-2295

  9. Faria, T., et al.: Does the type of seizure influence heart rate variability changes? Epilepsy Behav. 126, 108453 (2022). https://doi.org/10.1016/j.yebeh.2021.108453

  10. Shasha, Z., Chen, D., Ranjan, R., Heng**, K., Tang, Y., Zomaya, A.: A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement. J. Supercomput. 77, 1–19 (2021). https://doi.org/10.1007/s11227-020-03426-4

    Article  Google Scholar 

  11. Abdelhameed, A., Bayoumi, M.: A deep learning approach for automatic seizure detection in children with epilepsy. Front. Comput. Neurosci. 15 (2021). https://doi.org/10.3389/fncom.2021.650050. https://www.frontiersin.org/article/10.3389/fncom.2021.650050. ISSN 1662-5188

  12. Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp. 266–270 (2020). https://doi.org/10.1109/AICAS48895.2020.9073988

  13. Selim, S., Elhinamy, E., Othman, H., Abouelsaadat, W., Salem, M.A.-M.: A review of machine learning approaches for epileptic seizure prediction. In: 2019 14th International Conference on Computer Engineering and Systems (ICCES), pp. 239–244 (2019). https://doi.org/10.1109/ICCES48960.2019.9068190

  14. Burguera, A.: Using machine learning and heart rate variability features to predict epileptic seizures, October 2019

    Google Scholar 

  15. Silva, R.S., Plantes Neto, A.R., Brum Marques, J.L., Kavehei, O., Rodrigues, C.R.: A compact QRS detection system based on 0.79 W analog CMOS energy-of-derivative circuit. Microelectron. J. 113, 105097 (2021). https://doi.org/10.1016/j.mejo.2021.105097. https://www.sciencedirect.com/science/article/pii/S0026269221001087. ISSN 0026-2692

  16. Gadhoumi, K., Lina, J.-M., Gotman, J.: Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clin. Neurophysiol. Off. J. Int. Feder. Clin. Neurophysiol. 124 (2013). https://doi.org/10.1016/j.clinph.2013.04.006

  17. Chen, H.-H., Cherkassky, V.: Performance metrics for online seizure prediction. Neural Netw. 128, 22–32 (2020). https://doi.org/10.1016/j.neunet.2020.04.022. https://www.sciencedirect.com/science/article/pii/S0893608020301428. ISSN 0893-6080

  18. Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet. Circulation 101(23), e215–e220 (2000). https://doi.org/10.1161/01.CIR.101.23.e215. https://www.ahajournals.org/doi/abs/10.1161/01.CIR.101.23.e215

  19. Detti, P., Vatti, G., de Lara, G.Z.M.: EEG synchronization analysis for seizure prediction: a study on data of noninvasive recordings. Processes 8(7) (2020). https://doi.org/10.3390/pr8070846. https://www.mdpi.com/2227-9717/8/7/846. ISSN 2227-9717

  20. Höller, Y., Trinka, E., Kalss, G., Schiepek, G., Michaelis, R.: Correlation of EEG spectra, connectivity, and information theoretical biomarkers with psychological states in the epilepsy monitoring unit - a pilot study. Epilepsy Behav. 99 (2019). https://doi.org/10.1016/j.yebeh.2019.106485. https://www.epilepsybehavior.com/article/S1525-5050(19)30470-6/fulltext

  21. Romigi, A., et al.: Heart rate variability parameters during psychogenic non-epileptic seizures: comparison between patients with pure PNES and comorbid epilepsy. Front. Neurol. 11 (2020). https://doi.org/10.3389/fneur.2020.00713. https://www.frontiersin.org/article/10.3389/fneur.2020.00713. ISSN 1664-2295

  22. Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Public Health 5 (2017). https://doi.org/10.3389/fpubh.2017.00258. https://www.frontiersin.org/article/10.3389/fpubh.2017.00258. ISSN 2296-2565

  23. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  Google Scholar 

  24. Schölkopf, B., Smola, A., Müller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998). https://doi.org/10.1162/089976698300017467

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the Brazilian agencies CAPES, CNPq, and CAPES-UFSC PrInt for supporting this work.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil

    Rafael Sanchotene Silva, Cesar Ramos Rodrigues & Jefferson Luiz Brum Marques

  2. Department of Clinical Medicine, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil

    Roger Walz

Authors
  1. Rafael Sanchotene Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cesar Ramos Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roger Walz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jefferson Luiz Brum Marques
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Sanchotene Silva .

Editor information

Editors and Affiliations

  1. Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Jefferson Luiz Brum Marques

  2. Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Cesar Ramos Rodrigues

  3. Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Daniela Ota Hisayasu Suzuki

  4. Institute of Biomedical Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    José Marino Neto

  5. Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Renato García Ojeda

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Silva, R.S., Rodrigues, C.R., Walz, R., Marques, J.L.B. (2024). A Comparison of Classifiers for Epileptic Seizure Prediction Based on Heart Rate Variability. In: Marques, J.L.B., Rodrigues, C.R., Suzuki, D.O.H., Marino Neto, J., García Ojeda, R. (eds) IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering. CLAIB CBEB 2022 2022. IFMBE Proceedings, vol 99. Springer, Cham. https://doi.org/10.1007/978-3-031-49404-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-49404-8_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49403-1

  • Online ISBN: 978-3-031-49404-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation