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Method and software for processing daily EEG signals for detection of epileptic seizures in humans

НазваMethod and software for processing daily EEG signals for detection of epileptic seizures in humans
Назва англійськоюMethod and software for processing daily EEG signals for detection of epileptic seizures in humans
АвториMykola Khvostivskyi, Roman Boiko
ПринадлежністьTernopil Ivan Puluj National Technical University, Ukraine
Бібліографічний описMethod and software for processing daily EEG signals for detection of epileptic seizures in humans / Mykola Khvostivskyi, Roman Boiko // Scientific Journal of TNTU. — Tern.: TNTU, 2024. — Vol 113. — No 1. — P. 119–130.
Bibliographic description:Khvostivskyi M., Boiko R. (2024) Method and software for processing daily EEG signals for detection of epileptic seizures in humans. Scientific Journal of TNTU (Tern.), vol 113, no 1, pp. 119–130.
DOI: https://doi.org/10.33108/visnyk_tntu2024.01.119
УДК

004.021:004.422.8:616.853:517.57:519.21

Ключові слова

daily EEG signal, processing method, software tool, time-shift window, mutual covariance, harmonic functions of different frequencies, Neumann-Pearson criterion, computer EEG system, MATLAB.

A method, an algorithm and a software tool for processing daily EEG signals for computer electroencephalographic systems to detect the manifestation of epileptic seizures in humans have been developed. Mathematically, the daily EEG signal is presented as a random sequence of white Gaussian noise zones and additive mixtures of different-frequency harmonic components. Harmonic functions interpret the manifestations of epileptic seizures. The core of the method of processing daily EEG signals is a time-shifted window inter-covariance processing with multiple kernels in the form of different-frequency harmonic functions. Based on the method of window processing, an algorithm and a software tool for daily EEG signal processing with a graphical user interface using the MATLAB environment have been implemented. The developed software can be used as a component of computer EEG systems. The results of daily EEG signal processing using the software are displayed in the form of averaged products of covariance results (the value is measured in power units) within each processing window, which quantitatively reflect the time points of epileptic seizures in a person. Manifestations of epileptic seizures are reflected through the increase in the averaged values of the power of covariances in relation to observation intervals without corresponding manifestations of these seizures. To ensure the authorization of the process of determining the level of decision-making regarding the moments of epileptic seizures (exceeding the normal level), the threshold algorithm and the Neumann-Pearson statistical criterion were applied.

ISSN:2522-4433
Перелік літератури
1. Cerf R, el Hassan el Ouasdad. Spectral analysis of stereo-electroencephalograms: preictal slowing in partial
epilepsies. Biological Cybernetics. Volume 83. PP. 399-405(2000). DOI: 10.1007/s004220000178.
2. Liang S. F., Wang H. C., Chang W. L. (2010). Combination of EEG complexity and spectral analysis for
epilepsy diagnosis and seizure detection. EURASIP Journal on Advances in Signal Processing, 853434.
Doi: https://doi.org/10.1155/2010/853434.
3. Tsipouras M. G. (2019). Spectral information of EEG signals with respect to epilepsy classification.
EURASIP Journal on Advances in Signal Processing. 10. Doi: https://doi.org/10.1186/s13634-019-0606-8.
4. Jeffrey D Kennedy, Elizabeth E Gerard. Continuous EEG Monitoring in the Intensive Care Unit. June 2012.
Current Neurology and Neuroscience Reports 12 (4):419-28. Doi: 10.1007/s11910-012-0289-0.
5. Friedman D., Claassen J., Hirsch L. J. Continuous electroencephalogram monitoring in the intensive care
unit. Anesthesia & Analgesia: August 2009. Volume 109. Issue 2. P. 506–523. Doi: 10.1213/ane.0b013e31
81a9d8b5.
6. Young G. B., Jordan K. G., Doig G. S. An assessment of nonconvulsive seizures in the intensive care unit
using continuous EEG monitoring: an investigation of variables associated with mortality. Neurology.
1996;47(1):83–9.
7. Lawrence J. Hirsch. Continuous EEG Monitoring in the Intensive Care Unit. October 2004. American journal of electroneurodiagnostic technology. 44(3):137–58. Doi: 10.1080/1086508X.2004.11079478.
8. Selim R. Benbadis, MD, Diego Rielo, MD (co). EEG Artifacts. eMedicine Neurology, 2019. URL: https:// emedicine.medscape.com/article/1140247-overview.
9. Roy Sucholeiki, MB, BCh, MD. Normal EEG Variants. eMedicine Neurology, 2019. URL: https:// emedicine.medscape.com/article/1139291-overview.
10. Alarcon G., Binnie C. D., C.Elwes R. D., Polkey C. E. Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. Electroencephalography and Clinical Neurophysiology. Volume 94. Issue 5. May 1995. P. 326–337. Doi: https://doi.org/10.1016/0013-4694(94)00286-T.
11. Ocak H. Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Process. 88 (7). P. 1858–1867. 2008. Doi: https://doi.org/10.1016/j.sigpro.2008.01.026.
12. Bhattacharyya A., Pachori R. B., Upadhyay A., Acharya U. R. Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 7. 385. 2017. Doi: https://doi.org/10.3390/app7040385.
13. 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. P. 179–184. ISBN 979-8-89074-573-6. Doi: 10.46299/ ISG.2023.1.27.
14. 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.
15. Hvostivska L. V., Osukhivska H. M., Hvostivskyy M. O., Shadrina H. M., Dediv I. Yu. Development of methods and algorithms for a stochastic biomedical signal period calculation in medical computer diagnostic systems. Visnyk NTUU KPI Seriia – Radiotekhnika Radioaparatobuduvannia. (79). P. 78–84. Doi: 10.20535/RADAP.2019.79.78-84.48.

 

References:
1. Cerf R, el Hassan el Ouasdad. Spectral analysis of stereo-electroencephalograms: preictal slowing in partial
epilepsies. Biological Cybernetics. Volume 83. PP. 399-405(2000). DOI: 10.1007/s004220000178.
2. Liang S. F., Wang H. C., Chang W. L. (2010). Combination of EEG complexity and spectral analysis for
epilepsy diagnosis and seizure detection. EURASIP Journal on Advances in Signal Processing, 853434.
Doi: https://doi.org/10.1155/2010/853434.
3. Tsipouras M. G. (2019). Spectral information of EEG signals with respect to epilepsy classification.
EURASIP Journal on Advances in Signal Processing. 10. Doi: https://doi.org/10.1186/s13634-019-0606-8.
4. Jeffrey D Kennedy, Elizabeth E Gerard. Continuous EEG Monitoring in the Intensive Care Unit. June 2012.
Current Neurology and Neuroscience Reports 12 (4):419-28. Doi: 10.1007/s11910-012-0289-0.
5. Friedman D., Claassen J., Hirsch L. J. Continuous electroencephalogram monitoring in the intensive care
unit. Anesthesia & Analgesia: August 2009. Volume 109. Issue 2. P. 506–523. Doi: 10.1213/ane.0b013e31
81a9d8b5.
6. Young G. B., Jordan K. G., Doig G. S. An assessment of nonconvulsive seizures in the intensive care unit
using continuous EEG monitoring: an investigation of variables associated with mortality. Neurology.
1996;47(1):83–9.
7. Lawrence J. Hirsch. Continuous EEG Monitoring in the Intensive Care Unit. October 2004. American journal of electroneurodiagnostic technology. 44(3):137–58. Doi: 10.1080/1086508X.2004.11079478.
8. Selim R. Benbadis, MD, Diego Rielo, MD (co). EEG Artifacts. eMedicine Neurology, 2019. URL: https:// emedicine.medscape.com/article/1140247-overview.
9. Roy Sucholeiki, MB, BCh, MD. Normal EEG Variants. eMedicine Neurology, 2019. URL: https:// emedicine.medscape.com/article/1139291-overview.
10. Alarcon G., Binnie C. D., C.Elwes R. D., Polkey C. E. Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. Electroencephalography and Clinical Neurophysiology. Volume 94. Issue 5. May 1995. P. 326–337. Doi: https://doi.org/10.1016/0013-4694(94)00286-T.
11. Ocak H. Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Process. 88 (7). P. 1858–1867. 2008. Doi: https://doi.org/10.1016/j.sigpro.2008.01.026.
12. Bhattacharyya A., Pachori R. B., Upadhyay A., Acharya U. R. Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 7. 385. 2017. Doi: https://doi.org/10.3390/app7040385.
13. 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. P. 179–184. ISBN 979-8-89074-573-6. Doi: 10.46299/ ISG.2023.1.27.
14. 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.
15. Hvostivska L. V., Osukhivska H. M., Hvostivskyy M. O., Shadrina H. M., Dediv I. Yu. Development of methods and algorithms for a stochastic biomedical signal period calculation in medical computer diagnostic systems. Visnyk NTUU KPI Seriia – Radiotekhnika Radioaparatobuduvannia. (79). P. 78–84. Doi: 10.20535/RADAP.2019.79.78-84.48.
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