Titel:	Adaptive detection of epileptic activity in EEG signals based on morphological analysis and the Neyman-Pearson criterion
Autor(en):	Khvostivskyi, Mykola Talalai, Ihor
Bibliographic description (Ukraine):	Khvostivskyi M., Talalai I. Adaptive detection of epileptic activity in EEG signals based on morphological analysis and the neyman-pearson criterion. Collection of Scientific Papers with the Proceedings of the 4th International Scientific and Practical Conference «Achievements of Science and Applied Research» (November 10-12, 2025. Dublin, Ireland). European Open Science Space, 2025. P.367-371. DOI: 10.70286/EOSS-10.11.2025.
Bibliographic citation (APA):	Khvostivskyi, M., & Talalai, I. (2025). Adaptive detection of epileptic activity in EEG signals based on morphological analysis and the Neyman–Pearson criterion. Achievements of Science and Applied Research: Proceedings of the 4th International Scientific and Practical Conference (Dublin, Ireland, November 10–12, 2025) (pp. 367–371). European Open Science Space. DOI: 10.70286/EOSS-10.11.2025.
Erscheinungsdatum:	10-Nov-2025
Date of entry:	12-Dez-2025
Herausgeber:	European Open Science Space
Place of the edition/event:	Dublin, Ireland
Page range:	367-371
URI:	http://elartu.tntu.edu.ua/handle/lib/50397
ISBN:	979-8-89704-961-5
Copyright owner:	© Khvostivskyi M., Talalai I.
References (International):	1. Cerf R., el-Ouasdad E.H. Spectral analysis for early detection of epileptic seizures. Medical & Biological Engineering & Computing. 2008. Vol. 46, No. 4. P. 379-386. 2. Tsipouras M.G. Spectral information of EEG signals with respect to epilepsy classification. EURASIP J. Adv. Signal Process. 2019, 10 (2019). https://doi.org/10.1186/s13634-019-0606-8. 3. Tian C., Zhang F. EEG-based epilepsy detection with graph correlation analysis. Frontiers in Medicine. 2025. Vol. 12, Article 1549491. DOI: 10.3389/fmed.2025.1549491. 4. Diego Rielo, Selim R. Benbadis MD. Correlation studies in epileptic EEG patterns. Seizure. 2004. Vol. 13, No. 7. P. 475-483. 5. Roy Sucholeiki, Alarcon G., Binnie C.D., C. Elwes R.D., Polkey C.E., Starykh E.V. Spectral-correlation methods for epilepsy EEG analysis. Electroencephalography and Clinical Neurophysiology. 2002. Vol. 103, No. 6. P. 536-548. 6. A.T. Tzallas, M.G. Tsipouras, D.I. Fotiadis, Starykh E.V. Time-frequency analysis of EEG in epileptic patients. IEEE Transactions on Information Technology in Biomedicine. 2007. Vol. 11, No. 3. P. 327-335. 7. Ocak H., Bhattacharyya A., Pachori R.B., Upadhyay A., Acharya U.R. Wavelet decomposition of EEG and entropy computation for seizure classification. Computers in Biology and Medicine. 2011. Vol. 41, No. 12. P. 1090-1097. 8. Khvostivskyy M., Khvostivska L, Boyko R. Software, mathematical and algorithmic tools for the computer electroencephalography system of humans epilepsy manifestations detecting. Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia. 84 (Mar. 2021), P. 66-77. DOI: https://doi.org/10.20535/RADAP.2021.84.66-77. 9. Khvostivskyi M., Boiko R. Method and software for processing daily EEG signals for detection of epileptic seizures in humans. Scientific Journal of TNTU (Tern.), 2024. Vol 113, no 1, P. 119–130. URL: https://visnyk.tntu.edu.ua/?art=772. 10. Boyko R., Khvostivskyi M., Fuch O. Mathematical Model of the 24-hour EEG Signal of People with Manifestations of Epilepsy for Computer EEG Systems. Proceedings of the XXVII International Scientific and Practical Conference. Edmonton, Canada. 2023. Pp. 179-184. 11. Khvostivskyy M.O., Fuch O.V., Khvostivska L.V. Mathematical Model of EEG-Signals at Psycho-Emotional Influence. Science and Industry. Abstracts of the 34th International scientific and practical conference. Littera Verlag, Berlin. 2022. Pp. 167-171. ISBN 978-3-9110125-1-5.
Content type:	Conference Abstract
Enthalten in den Sammlungen:	Наукові публікації працівників кафедри біотехнічних систем

Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.