Skip to main content
Springer Nature Link
Log in

Automatic and manual prediction of epileptic seizures based on ECG

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

This study presents a new attempt to quantify and predict changes in the ECG signal in the pre-ictal period. In the proposed approach, threshold techniques were applied to the standard deviation of two heart rate variability features (The number of heartbeats per two minutes and approximate entropy) computed to ensure prediction and quantification of the pre-ictal state. We analyzed clinical data taken from two epileptic public databases, Siena scalp EEG and post-ictal heart rate oscillations in partial epilepsy and a local database. By testing the proposed approach on the Siena scalp EEG database, we achieved a sensitivity of 100%, specificity of 95%, and an accuracy of 96.4% whereas using acquisitions from the post-ictal database, we achieved a sensitivity of 100%, specificity of 91% and an accuracy of 94% and using the local database we achieved a sensitivity of 100%, a specificity of 97% and an accuracy of 97.5%. Furthermore, the proposed approach predicted 58.7%, 57.2, and 40% of the seizures before the onset by more than 10 min for the data taken from post-ictal, local and Siena database, respectively. Using the automatic threshold technique, we were able to achieve a sensitivity, specificity, and accuracy of 85%, 81%, 82% using our local database, respectively, whereas using acquisitions take from the Siena scalp EEG database, we achieved a sensitivity of 75%, specificity of 85% and an accuracy of 82%. Besides, using the post-ictal database, we achieved a sensitivity of 90%, a specificity of 83% and an accuracy of 85%.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

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
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The datasets Siena scalp EEG and post-ictal Heart Rate Oscillations in partial epilepsy analyzed during the current study are available in the physionet platform [15, 27].The local database analyzed during the current study is available in the functional brain tractography project [28].

References

  1. Du, P., Dai, J., Li. J., Mal, Q.: Nonlinear Granger causality and its application in the analysis of epileptic EEG and ECG signal. In: 2015 6th International Conference on Manufacturing Science and Engineering (pp. 1773–1776) (2015). https://doi.org/10.2991/icmse-15.2015.320

  2. Behbahani, S., Dabanloo, N.J., Nasrabadi, A.M., Teixeira, C.A., Dourado, A.: A new algorithm for detection of epileptic seizures based on HRV signal. J. Exp. Theor. Artif. Intell. 26(2), 251–265 (2014). https://doi.org/10.1080/0952813X.2013.861874

    Article  Google Scholar 

  3. Falco-Walter, J.J., Scheffer, I.E., Fisher, R.S.: The new definition and classification of seizures and epilepsy. Epilepsy Res. 139, 73–79 (2018). https://doi.org/10.1016/j.eplepsyres.2017.11.015

    Article  Google Scholar 

  4. Choubey, H., Pandey, A.: A combination of statistical parameters for the detection of epilepsy and EEG classification using ANN and KNN classifier. Signal Image Video Process 15(3), 475–483 (2021). https://doi.org/10.1007/s11760-020-01767-4

    Article  Google Scholar 

  5. Sirven, J.I.: Epilepsy: a spectrum disorder. Cold Spring Harb. Perspect. Med. (2015). https://doi.org/10.1101/cshperspect.a022848

    Article  Google Scholar 

  6. Kwan, P., Brodie, M.J.: Emerging drugs for epilepsy. Expert Opin. Emerg. Drugs 12(3), 407–422 (2007). https://doi.org/10.1517/14728214.12.3.407

    Article  Google Scholar 

  7. Jeppesen, J., Beniczky, S., Fuglsang-Frederiksen, A., Sidenius, P., Jasemian, Y.: Detection of epileptic-seizures by means of power spectrum analysis of heart rate variability: a pilot study. Technol. Health Care Off. J. Eur. Soc. Eng. Med. 18(6), 417–426 (2010). https://doi.org/10.3233/THC-2010-0606

    Article  Google Scholar 

  8. Doyle, O.M., Greene, B.R., Marnane, W., Lightbody, G., Boylan, G.B.: Characterisation of heart rate changes and their correlation with EEG during neonatal seizures, In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug. 2008, pp. 4984–4987. https://doi.org/10.1109/IEMBS.2008.4650333

  9. Das, A.B., Bhuiyan, M.I.H., Alam, S.M.S.: Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. Signal Image Video Process. 10(2), 259–266 (2016). https://doi.org/10.1007/s11760-014-0736-2

    Article  Google Scholar 

  10. Lopez-Gordo, M.A., Sanchez-Morillo, D., Valle, F.P.: Dry EEG electrodes. Sensors (2014). https://doi.org/10.3390/s140712847

    Article  Google Scholar 

  11. Gargiulo, G., et al.: A new EEG recording system for passive dry electrodes. Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol. 121(5), 686–693 (2010). https://doi.org/10.1016/j.clinph.2009.12.025

    Article  Google Scholar 

  12. Devinsky, O.: Effects of seizures on autonomic and cardiovascular function. Epilepsy Curr. 4(2), 43–46 (2004). https://doi.org/10.1111/j.1535-7597.2004.42001.x

    Article  Google Scholar 

  13. Leal, A., et al.: Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy. Sci. Rep. 11, 5987 (2021). https://doi.org/10.1038/s41598-021-85350-y

    Article  Google Scholar 

  14. Moridani, M.K., Farhadi, H.: Heart rate variability as a biomarker for epilepsy seizure prediction. Bratisl. Lek. Listy 118(1), 3–8 (2017). https://doi.org/10.4149/BLL_2017_001

    Article  Google Scholar 

  15. Al-Aweel, I.C., et al.: Post-ictal heart rate oscillations in partial epilepsy: data and analysis. Physionet (1998). https://doi.org/10.13026/C2QC72

    Article  Google Scholar 

  16. Shamim, G., Khan, Y.U., Sarfraz, M., Farooq, O.: Epileptic seizure detection using heart rate variability. In: 2016 International Conference on Signal Processing and Communication (ICSC), pp. 250–254 (2016). https://doi.org/10.1109/ICSPCom.2016.7980585

  17. Behbahani, S., Dabanloo, N.J., Nasrabadi, A.M., Dourado, A.: Prediction of epileptic seizures based on heart rate variability. Technol. Health Care Off. J. Eur. Soc. Eng. Med. 24(6), 795–810 (2016). https://doi.org/10.3233/THC-161225

    Article  Google Scholar 

  18. Pavei, J., et al.: Early seizure detection based on cardiac autonomic regulation dynamics. Front. Physiol. 8, 765 (2017). https://doi.org/10.3389/fphys.2017.00765

    Article  Google Scholar 

  19. Detti, P., Vatti, G., de Lara, G.Z.M.: EEG synchronization analysis for seizure prediction: a study on data of noninvasive recordings. Processes (2020). https://doi.org/10.3390/pr8070846

    Article  Google Scholar 

  20. David, O., Woźniak, A., Minotti, L., Kahane, P.: Preictal short-term plasticity induced by intracerebral 1 Hz stimulation. Neuroimage 39(4), 1633–1646 (2008). https://doi.org/10.1016/j.neuroimage.2007.11.005

    Article  Google Scholar 

  21. Elgendi, M., Jonkman, M., De Boer, F.: Frequency bands effects on QRS detection.In: 3rd International Conference on Bio-Inspired Systems and Signal Processing, Biosignals 2010. vol. 1, pp. 428–431 (2010)

  22. Pincus, S.M., Gladstone, I.M., Ehrenkranz, R.A.: A regularity statistic for medical data analysis. J. Clin. Monit. 7(4), 335–345 (1991). https://doi.org/10.1007/BF01619355

    Article  Google Scholar 

  23. Vanderlei, F., Vanderlei, L.C.M., de Abreu, L.C., Garner, D.: Entropic analysis of HRV in obese children. Int. Arch. Med. (2015). https://doi.org/10.3823/1799

    Article  Google Scholar 

  24. Johnson, M.L., Straume, M.: Approximate entropy as indication of goodness-of-fit. Methods Enzymol. (2000). https://doi.org/10.1016/S0076-6879(00)21195-6

    Article  Google Scholar 

  25. Lee, D., In, J., Lee, S.: Standard deviation and standard error of the mean. Korean J. Anesthesiol. 68, 220–223 (2015). https://doi.org/10.4097/kjae.2015.68.3.220

    Article  Google Scholar 

  26. Nachar, N.: The Mann–Whitney U: a test for assessing whether two independent samples come from the same distribution. Tutor. Quant. Methods Psychol. (2008). https://doi.org/10.20982/tqmp.04.1.p013

    Article  Google Scholar 

  27. Detti, P.: Siena scalp EEG database. PhysioNet. (2021). 10.13026/5D4A-J060

  28. Functional brain tractography project—funded by the European Research Council. (2022) https://f-tract.eu/

Download references

Author information

Authors and Affiliations

  1. Laboratoire de Technologie et Imagerie Médicale, Facultéde Médecine de Monastir, Universitéde Monastir, 5019, Monastir, Tunisia

    Manef Ben Mbarek, Ines Assali, Salah Hamdi, Asma Ben Abdallah & Mohamed Hedi Bedoui

  2. Faculté des Sciences de Monastir, Université de Monastir, 90 Ave Mohamed V, 1002, Monastir, Tunisia

    Manef Ben Mbarek & Ines Assali

  3. Institut Supérieur d’Informatique et de Mathématiques, Université de Monastir, 5019, Monastir, Tunisia

    Asma Ben Abdallah

  4. Faculté de Médecine, Institut de Neurosciences des Systèmes, Inserm UMR1106, Aix-Marseille Université, 27, Boulevard Jean Moulin, 13005, Marseille, France

    Olivier David & Marcel Carrere

  5. Hopital Universitaire Fattouma Bourguiba, 5019, Monastir, Tunisia

    Mouna Aissi

Authors
  1. Manef Ben Mbarek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ines Assali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salah Hamdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Asma Ben Abdallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Olivier David
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mouna Aissi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marcel Carrere
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mohamed Hedi Bedoui
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MBM, ABA, and MC conceptualized the proposed idea. MBM, IA, SH, ABA, MC, MHB checked the analytical methods. MBM, OD and MA did the data curation. MBM and MC, IA analyzed the data. MBM and MC carried out the computations. MBM, IA, SH, ABA, MC and MHB contributed to the evaluation of the results. MBM, ABA, MC and MHB wrote the manuscript in cooperation with the other authors. All authors discussed the results, contributed to the final manuscript and have approved of the final article.

Corresponding author

Correspondence to Manef Ben Mbarek.

Ethics declarations

Conflict of interest

This work was financially supported by the “PHC-Utique” program of the French Ministry of Foreign Affairs and Ministry of Higher Education and Research and the Tunisian Ministry of Higher Education and Scientific Research in the CMCU project number 21G1402.

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

Ben Mbarek, M., Assali, I., Hamdi, S. et al. Automatic and manual prediction of epileptic seizures based on ECG. SIViP 18, 4175–4190 (2024). https://doi.org/10.1007/s11760-024-03063-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-024-03063-x

Keywords

Search

Navigation