Abstract
Stroke is a medical condition which can easily affect the quality of life, depending on how extended the stroke is and what regions of the brain are involved. According to the most recent data cited in WHO, Romania is in top three of the countries with increased frequency of stroke and has the second place for having the most deaths and disabilities caused by stroke. Actually, stroke is the second death cause in Romania after cardiac arrest. Today, there are various prevention methods concerning stroke. The hypothesis of the research context is that EEG signal can provide useful information on risk related prediction for recurrent stroke and post-stroke epilepsy. Knowing that there is a certain risk on develo** secondary epilepsy after stroke, based on the EEG rhythms, may help in prevention and maybe in reconsidering a new approach in the treatment of this pathology. On the other hand Fractional Fourier Transform (FrFT), a generalization of conventional Fourier Transform, is used with success in many applications like detection of signals in noise, image compression, reduction of side lobe levels using convolutional windows, time-frequency analysis, etc. It can be used in more effective manner compared to Fourier transform with additional degrees of freedom. That was the motivation to analyze the spectra of each component of the EEG signals using FrFT in order to predict recurrent stroke and post-stroke epilepsy incidence. The results prove the efficiency of the method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ringer, A., Jimenez, L.: Unruptured Brain Aneurysm (2018). Available: https://www.mayfieldclinic.com/pe-aneurun.htm
Greving, J., Wermer, M., Brown, R., Morita, A., Juvela, S., Yanekura, M., Ishibashi, T., Torner, J., Nakayama, T., Rinkel, G., Algra, A.: Development of the phases score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies. Lancet Neurol. 13(1), 59–66 (2014)
Vladescu, C., Scintee, S.G., Olsavszky, V., Hernandez-Quevedo, C., Sagan, A.: Romania: health system review. Health Syst. Transit. 18(4), 1–170 (2016)
GHDx. Global Health Data Exchange. Global Burden of Disease Study 2015 (2015). Available: ghdx.healthdata.org/gbd-2015
Szatmari, S., Pascu, I., Mihalka, L., Mulesa, S.V., Fekete, I., Fulesdi, B., Csiba, L., Zselyuk, G., Szasz, J., Gebefugi, J., Nicolescu, S., Vasiesiu, D., Smolanka, V.I., Bereczki, D.: The Mures-Uzhgorod-Debrecen study: a comparison of hospital stroke services in central-eastern Europe. Eur J Neurol. 9(3), 293–296 (2002)
Wilkins, E.W., Wilson, L., Wickramasinghe, K., Bhatnagar, P., Leal, J., Luengo-Fernandez, R., Burns, R., Rayner, M., Townsend, N.: European Cardiovascular Disease Statistics 2017, European Heart Network (2017). Available: www.ehnheart.org
OECD. Organisation for Economic Co-operation and Development (OECD), Health Care Quality Indicators: Acute Care (2014). Available: https://stats.oecd.org/
Wong, G.Y., Warner, D.O., Schroeder, D.R., Offord, K.P., Warnerand, M.A., Maxson, P.M., Whisnant, J.P.: Risk of surgery and anesthesia for ischemic stroke. Anesthesiology. 92(2), 425–432 (2000)
Magin, R., Vinagre, B., Podlubny, I.: Can cybernetics and fractional calculus be partners? Searching new ways to solve complex problems. IEEE Syst. Man Cybern. Mag. 4(3), 23–28 (2018)
Neckebroek, M., Ionescu, C.M., van Amsterdam, K., De Smet, T., Debaets, P., Decruyenaere, J., De Keyser, R., Struys, M.: A comparison of propofol-to-bis post-operative intensive care sedation by means of target controlled infusion, Bayesian-based and predictive control methods. A feasibility study, under review. J. Clin. Monit. Pract. 33, 675–686 (2019)
Zhou, Z.B., Meng, L., Gelb, A., Lee, R., Huang, W.Q.: Cerebral ischemia during surgery: an overview. J. Biomed. Res. 30(2), 83–87 (2016)
Sejdic, E., Djurovic, I., Stankovic, L.: Fractional Fourier transform as a signal processing tool: an overview of recent developments. Signal Proc. 91, 1351–1369 (2011). https://doi.org/10.1016/j.sigpro.2010.10.008
Zhao, T., Ran, Q.: The weighted fractional Fourier transform and its application in image encryption. Math. Probl. Eng. (2019). https://doi.org/10.1155/2019/4789194
Wang, T., Liu, N., Su, Z., Li, C.: A new time–frequency feature extraction method for action detection on artificial knee by fractional Fourier transform. Micromachines. 10, 333 (2019). https://doi.org/10.3390/mi10050333
Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E. 64, 061907 (2001)
Acknowledgments
This work was supported by the ÚNKP-19-4-OE-64 New National Excellence Program of the Ministry for Innovation and Technology and by a grant of the Romanian National Authority for Scientific Research and Innovation, project number PN-III-P2-2.1-PED-2019-0844, contract no.323PED.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Dulf, EH., Ionescu, CM. (2021). Risk Related Prediction for Recurrent Stroke and Post-stroke Epilepsy Using Fractional Fourier Transform Analysis of EEG Signals. In: Awrejcewicz, J. (eds) Perspectives in Dynamical Systems II: Mathematical and Numerical Approaches. DSTA 2019. Springer Proceedings in Mathematics & Statistics, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-77310-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-77310-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77309-0
Online ISBN: 978-3-030-77310-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)