ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Belokopytov A. С., Makarova M. М., Salamatin M. I., Redkozubova O. M. Development of an algorithm for detecting slow peak-wave activity in non-convulsive forms of epilepsy. Izvestiya VUZ. Applied Nonlinear Dynamics, 2024, vol. 32, iss. 2, pp. 223-238. DOI: 10.18500/0869-6632-003088, EDN: XBFSQU

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
Article type: 

Development of an algorithm for detecting slow peak-wave activity in non-convulsive forms of epilepsy

Belokopytov Anton Сергеевич, National Research University "Higher School of Economics"
Makarova Milana Михайловна, National Research University "Higher School of Economics"
Salamatin Mikhail Igorevich, National Research University "Higher School of Economics"

The purpose of this study is to develop a classifier capable of detecting typical absence seizures in real-time using electroencephalogram (EEG) data and a Support Vector Machine (SVM) model.

Methods. Sections of the EEG, previously identified by a specialist as containing typical absences, were used to train the SVM model. Key features for classification include the number of zero crossings, cross-correlation between two consecutive windows, spectral power across various frequency bands, and the standard deviation of instantaneous signal power.

Results. Training and testing datasets were established, consisting of EEG windows with various types of artifacts. The SVM model was successfully trained and tested, achieving high performance metrics. The developed algorithm can be integrated into a mobile application and used in conjunction with a wearable EEG device with dry electrodes for real-time detection of typical absences.

Conclusion. The study results affirm the potential for using machine learning techniques for the automatic detection and logging of epileptic activity. However, additional testing on a larger dataset is needed for more conclusive results, including data acquired through a wireless EEG device using dry electrodes. Future work will involve selecting a suitable EEG device and developing a mobile application for real-time data collection and analysis.

  1. Reichsoellner J, Larch J, Unterberger I, Dobesberger J, Kuchukhidze G, Luef G, Bauer G, Trinka E. Idiopathic generalised epilepsy of late onset: a separate nosological entity? J. Neurol. Neurosurg. Psychiatry. 2010;81(11):1218–1222. DOI: 10.1136/jnnp.2009.176651.
  2. Epilepsy and Status Epilepticus in Adults and Children. Clinical Recommendations. Ministry of Health of the Russian Federation; 2022. 291 p. (in Russian).
  3. Cortez MA, Snead III OC. Pharmacologic models of generalized absence seizures in rodents. In: Pitkanen A, Schwartzkroin PA, Moshe SL, editors. Models of Seizures and Epilepsy. Burlington: Academic Press; 2006. P. 111–126. DOI: 10.1016/B978-012088554-1/50012-8.
  4. Destexhe A. Network models of absence seizures. In: Faingold CL, Blumenfeld H, editors. Neuronal Networks in Brain Function, CNS Disorders, and Therapeutics. San Diego: Academic Press; 2014. P. 11–35. DOI: 10.1016/B978-0-12-415804-7.00002-2.
  5. Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurology. 2018;17(3):279–288. DOI: 10.1016/S1474-4422(18)30038-3.
  6. Bruno E, Viana PF, Sperling MR, Richardson MP. Seizure detection at home: Do devices on the market match the needs of people living with epilepsy and their caregivers? Epilepsia. 2020;61(S1):S11–S24. DOI: 10.1111/epi.16521.
  7. Elmali AD, Begley K, Chester H, Cooper J, Moreira C, Sharma S, Whelan A, Leschziner G, Richardson MP, Stern W, Koutroumanidis M. Evaluation of absences and myoclonic seizures in adults with genetic (idiopathic) generalized epilepsy: a comparison between self-evaluation and objective evaluation based on home video-EEG telemetry. Epileptic Disorders. 2021;23(5):719– 732. DOI: 10.1684/epd.2021.1325.
  8. Tatum 4th WO, Winters L, Gieron M, Passaro EA, Benbadis S, Ferreira J, Liporace J. Outpatient seizure identification: results of 502 patients using computer-assisted ambulatory EEG. Journal of Clinical Neurophysiology. 2001;18(1):14–19. DOI: 10.1097/00004691-200101000-00004.
  9. Beniczky S, Wiebe S, Jeppesen J, Tatum WO, Brazdil M, Wang Y, Herman ST, Ryvlin P. Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology. Clinical Neurophysiology. 2021;132(5):1173–1184. DOI: 10.1016/j.clinph.2020.12.009.
  10. Wirrell EC, Camfield CS, Camfield PR, Dooley JM, Gordon KE, Smith B. Long-term psychosocial outcome in typical absence epilepsy. Sometimes a wolf in sheeps’ clothing. Arch. Pediatr. Adolesc. Med. 1997;151(2):152–158. DOI: 10.1001/archpedi.1997.02170390042008.
  11. Wirrell EC, Camfield PR, Camfield CS, Dooley JM, Gordon KE. Accidental injury is a serious risk in children with typical absence epilepsy. Arch. Neurol. 1996;53(9):929–932. DOI: 10.1001/ archneur.1996.00550090141020.
  12. Vega C, Guo J, Killory B, Danielson N, Vestal M, Berman R, Martin L, Gonzalez JL, Blumenfeld H, Spann MN. Symptoms of anxiety and depression in childhood absence epilepsy. Epilepsia. 2011;52(8):e70–e74. DOI: 10.1111/j.1528-1167.2011.03119.x.
  13. Killory BD, Bai X, Negishi M, Vega C, Spann MN, Vestal M, Guo J, Berman R, Danielson N, Trejo G, Shisler D, Novotny Jr EJ, Constable RT, Blumenfeld H. Impaired attention and network connectivity in childhood absence epilepsy. NeuroImage. 2011;56(4):2209–2217. DOI: 10.1016/ j.neuroimage.2011.03.036.
  14. Fiest KM, Birbeck GL, Jacoby A, Jette N. Stigma in epilepsy. Current Neurology and Neuroscience Reports. 2014;14(5):444. DOI: 10.1007/s11910-014-0444-x.
  15. Kjaer TW, Sorensen HBD, Groenborg S, Pedersen CR, Duun-Henriksen J. Detection of paroxysms in long-term, single-channel EEG-monitoring of patients with typical absence seizures. IEEE Journal of Translational Engineering in Health and Medicine. 2017;5:2000108. DOI: 10.1109/ JTEHM.2017.2649491.
  16. Tovar Quiroga DF, Britton JW, Wirrell EC. Patient and caregiver view on seizure detection devices: A survey study. Seizure. 2016;41:179–181. DOI: 10.1016/j.seizure.2016.08.004.
  17. Ovchinnikov A., Luttjohann A, Hramov A, van Luijtelaar G. An algorithm for real-time detection of spike-wave discharges in rodents. Journal of Neuroscience Methods. 2010;194(1):172–178. DOI: 10.1016/j.jneumeth.2010.09.017.
  18. Sitnikova E, Hramov AE, Koronovsky AA, van Luijtelaar G. Sleep spindles and spike–wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis. Journal of Neuroscience Methods. 2009;180(2): 304–316. DOI: 10.1016/j.jneumeth.2009.04.006.
  19. Nazimov AI, Pavlov AN, Khramov AE, Grubov VV, Sitnikova EY, Khramova MV. Recognition of oscillatory patterns on electroencephalogram based on adaptive wavelet-analysis. Tambov University Reports. Series: Natural and Technical sciences. 2013;18(4):1431–1434 (in Russian).
  20. Grubov VV, Koronovskii AA, Sitnikova EY, Hramov AE. Time-frequency analysis of characteristic patterns of the activity of neuron ensembles in the brain by means of continuous wavelet transform. Bulletin of the Russian Academy of Sciences: Physics. 2014;78(12):1242–1245. DOI: 10.3103/S1062873814120090.
  21. Sitnikova E, Smirnov K, Grubov V, Hramov A. Diagnostic principles of immature epileptic (proepileptic) EEG activity in rats with genetic predisposition to absence epilepsy. Information and Control Systems. 2019;(1):89–97 (in Russian). DOI: 10.31799/1684-8853-2019-1-89-97.
  22. van Luijtelaar G, Luttjohann A, Makarov VV, Maksimenko VA, Koronovskii AA, Hramov AE. Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models. Journal of Neuroscience Methods. 2016;260:144–158. DOI: 10.1016/j.jneumeth.2015.07.010.
  23. Grubov VV, Sitnikova EY, Kurovskaya MK, Koronovskii AA, Hramov AE. Prospect of using empirical mode decomposition and wavelet analysis for detecting proepileptic activity on EEG signal. Memoirs of the Faculty of Physics, Lomonosov Moscow State University. 2016;(5):165404 (in Russian).
  24. Jando G, Siegel RM, Horvath Z, Buzsaki G. Pattern recognition of the electroencephalogram by artificial neural networks. Electroencephalography and Clinical Neurophysiology. 1993;86(2): 100–109. DOI: 10.1016/0013-4694(93)90082-7.
  25. Buteneers P, Schrauwen B, Verstraeten D, Stroobandt D. Real-time epileptic seizure detection on intra-cranial rat data using reservoir computing. In: Koppen M, Kasabov N, Coghill G, editors. Advances in Neuro-Information Processing. ICONIP 2008. Vol. 5506 of Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2009. P. 56–63. DOI: 10.1007/978-3-642-02490-0_7.
  26. Xanthopoulos P, Rebennack S, Liu C-C, Zhang J, Holmes GL, Uthman BM, Pardalos PM. A novel wavelet based algorithm for spike and wave detection in absence epilepsy. In: 2010 IEEE International Conference on BioInformatics and BioEngineering. 31 May 2010 - 3 June 2010, Philadelphia, PA, USA. New York: IEEE; 2010. P. 14–19. DOI: 10.1109/BIBE.2010.12.
  27. Startceva SA, Luettjohann A, Sysoev IV, van Luijtelaar G. A new method for automatic marking epileptic spike-wave discharges in local field potential signals. In: Proc. SPIE. Vol. 9448. Saratov Fall Meeting 2014: Optical Technologies in Biophysics and Medicine XVI; Laser Physics and Photonics XVI; and Computational Biophysics. SPIE; 2015. P. 94481R. DOI: 10.1117/12.2179017.
  28. Baser O, Yavuz M, Ugurlu K, Onat F, Demirel BU. Automatic detection of the spike-and-wave discharges in absence epilepsy for humans and rats using deep learning. Biomedical Signal Processing and Control. 2022;76:103726. DOI: 10.1016/j.bspc.2022.103726.
  29. Guo L, Rivero D, Dorado J, Rabunal JR, Pazos A. Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. Journal of Neuroscience Methods. 2010;191(1):101–109. DOI: 10.1016/j.jneumeth.2010.05.020.
  30. Dan J, Vandendriessche B, Van Paesschen W, Weckhuysen D, Bertrand A. Computationally efficient algorithm for real-time absence seizure detection in wearable electroencephalography. International Journal of Neural Systems. 2020;30(11):2050035. DOI: 10.1142/S0129065720500355.
  31. Glukhova LY. Clinical significance of epileptiform activity in electroencephalogram. Russian Journal of Child Neurology. 2016;11(4):8–19 (in Russian). DOI: 10.17650/2073-8803- 2016-11-4-8-19.
  32. Volnova AB, Lenkov DN. Absence epilepsy: mechanisms of hypersynchronization of neuronal networks. Medical Academic Journal. 2012;12(1):7–19 (in Russian).
  33. Karlov VA. Absence. Neuroscience and Behavioral Physiology. 2005;3:55–60 (in Russian).
  34. Petersen EB, Duun-Henriksen J, Mazzaretto A, Kjær TW, Thomsen CE, Sorensen HBD. Generic single-channel detection of absence seizures. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 30 August 2011 - 3 September 2011, Boston, MA, USA. New York: IEEE; 2011. P. 4820–4823. DOI: 10.1109/IEMBS.2011.6091194.
  35. Chatzichristos C, Swinnen L, Macea J, Bhagubai M, Van Paesschen W, De Vos M. Multimodal detection of typical absence seizures in home environment with wearable electrodes. Frontiers in Signal Processing. 2022;2:1014700. DOI: 10.3389/frsip.2022.1014700.
  36. Japaridze G, Loeckx D, Buckinx T, Larsen SA, Proost R, Jansen K, MacMullin P, Paiva N, Kasradze S, Rotenberg A, Lagae L, Beniczky S. Automated detection of absence seizures using a wearable electroencephalographic device: a phase 3 validation study and feasibility of automated behavioral testing. Epilepsia. 2022. DOI: 10.1111/epi.17200.
  37. Makarov VV. Methods and Algorithms for Automatic Classification of Psychophysiological Characteristics of a Person. PhD Thesis. Moscow: Federal Research Center «Informatics and Management» of the Russian Academy of Sciences; 2022. 104 p. (in Russian).
  38. Sitnikova EY, Koronovskii AA, Hramov AE. Analysis of epileptic activity of brain in case of absence epilepsy: applied aspects of nonlinear dynamics. Izvestiya VUZ. Applied Nonlinear Dynamics. 2011;19(6):173–182 (in Russian). DOI: 10.18500/0869-6632-2011-19-6-173-182.
  39. Beniczky S, Rubboli G, Covanis A, Sperling MR. Absence-to-bilateral-tonic-clonic seizure. Neurology. 2020;95(14):e2009–e2015. DOI: 10.1212/WNL.0000000000010470.
  40. Shoeb A. CHB-MIT Scalp EEG Database [Electronic resource]. PhysioNet; 2010. Available from:
  41. NeuroPlay - NeuroPlay-6С [Electronic resource]. Available from:
Available online: