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The Importance of Rhythm Activity in Epilepsy EEG Signal Classification (An Educational Article)

Yıl 2024, Cilt: 7 Sayı: 2, 138 - 155, 31.08.2024

Negin Melek

https://doi.org/10.35377/saucis...1436915

Öz

Electroencephalography (EEG), used to record the random electrical activity in brain, is a known medical test. In this test, a graphical waveform is obtained by measuring the electrical activity of the cells. In the medical world, the relationship between epilepsy and EEG can be understood by examining changes in brain activity during or between epileptic seizures. EEG is a useful tool in the early treatment and diagnosis of epilepsy. Whether seizures, generally known as abnormal electrical discharges in brain cells, are of epileptic origin, comes to light through EEG. The main goal of our study was to demonstrate the EEG rhythm effectiveness for the diagnosis of epilepsy in EEG data obtained from the epilepsy center of Bonn Freiburg University Hospital. Time domain feature extraction of EEG band classification results was examined in detail against the classification results of frequency domain feature extraction of EEG rhythms in healthy subjects and subjects with epilepsy. By extracting effective features from EEG data in both time and frequency domains, the k nearest neighbor (KNN) algorithm was used for the time and frequency domain. It cannot be overlooked that among the four methods used for performance evaluation in the designed model, the classification success of frequency domain features was more successful than that of time domain features. Using the KNN algorithm, healthy individuals and epilepsy patients with seizures were classified with 100% success.

Anahtar Kelimeler

EEG Epilepsy Rhythm Feature extraction Classification

Kaynakça

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Yıl 2024, Cilt: 7 Sayı: 2, 138 - 155, 31.08.2024

Negin Melek

https://doi.org/10.35377/saucis...1436915

Öz

Kaynakça

  • N. Manshouri, M. Maleki, and T. Kayikcioglu, “An EEG-based stereoscopic research of the PSD differences in pre and post 2D&3D movies watching,” Biomed Signal Process Control, vol. 55, Jan. 2020, doi: 10.1016/j.bspc.2019.101642.
  • M. Soufineyestani, D. Dowling, and A. Khan, “Electroencephalography (EEG) Technology Applications and Available Devices,” Applied Sciences 2020, Vol. 10, Page 7453, vol. 10, no. 21, p. 7453, Oct. 2020, doi: 10.3390/APP10217453.
  • M. Melek, N. Manshouri, and T. Kayikcioglu, “Low-Cost Brain-Computer Interface Using the Emotiv Epoc Headset Based on Rotating Vanes,” Traitement du Signal, vol. 37, no. 5, pp. 831–837, Nov. 2020, doi: 10.18280/ts.370516.
  • E. Maiorana, “Deep learning for EEG-based biometric recognition,” Neurocomputing, vol. 410, pp. 374–386, Oct. 2020, doi: 10.1016/J.NEUCOM.2020.06.009.
  • P. Arnau-Gonzalez, S. Katsigiannis, M. Arevalillo-Herraez, and N. Ramzan, “BED: A New Data Set for EEG-Based Biometrics,” IEEE Internet Things J, vol. 8, no. 15, pp. 12219–12230, Aug. 2021, doi: 10.1109/JIOT.2021.3061727.
  • M. Maleki and T. Kayikçioglu, “A new brain-computer interface system using the gaze on rotating vane.,” Biomedical Research-tokyo, 2016.
  • B. T. Klassen et al., “Quantitative EEG as a predictive biomarker for Parkinson disease dementia,” Neurology, vol. 77, no. 2, pp. 118–124, Jul. 2011, doi: 10.1212/WNL.0B013E318224AF8D/SUPPL_FILE/KLASSEN.PDF.
  • C. Melissant, A. Ypma, E. E. E. Frietman, and C. J. Stam, “A method for detection of Alzheimer’s disease using ICA-enhanced EEG measurements,” Artif Intell Med, vol. 33, no. 3, pp. 209–222, Mar. 2005, doi: 10.1016/J.ARTMED.2004.07.003.
  • E. Cainelli, L. Vedovelli, B. Carretti, and P. Bisiacchi, “EEG correlates of developmental dyslexia: a systematic review,” Annals of Dyslexia 2022 73:2, vol. 73, no. 2, pp. 184–213, Nov. 2022, doi: 10.1007/S11881-022-00273-1.
  • A. R. Clarke, R. J. Barry, S. J. Johnstone, R. McCarthy, and M. Selikowitz, “EEG development in Attention Deficit Hyperactivity Disorder: From child to adult,” Clinical Neurophysiology, vol. 130, no. 8, pp. 1256–1262, Aug. 2019, doi: 10.1016/J.CLINPH.2019.05.001.
  • R. J. Barry, A. R. Clarke, S. J. Johnstone, R. McCarthy, and M. Selikowitz, “Electroencephalogram theta/beta ratio and arousal in attention-deficit/hyperactivity disorder: evidence of independent processes,” Biol Psychiatry, vol. 66, no. 4, pp. 398–401, Aug. 2009, doi: 10.1016/J.BIOPSYCH.2009.04.027.
  • C. Baumgartner and J. P. Koren, “Seizure detection using scalp-EEG,” Epilepsia, vol. 59, pp. 14–22, Jun. 2018, doi: 10.1111/EPI.14052.
  • S. Nasehi and H. Pourghassem, “Seizure detection algorithms based on analysis of EEG and ECG signals: A survey,” Neurophysiology, vol. 44, no. 2, pp. 174–186, Jun. 2012, doi: 10.1007/S11062-012-9285-X/METRICS.
  • T. Rowberry et al., “Implementation and Early Evaluation of a Quantitative Electroencephalography Program for Seizure Detection in the PICU*,” Pediatric Critical Care Medicine, vol. 21, no. 6, pp. 543–549, Jun. 2020, doi: 10.1097/PCC.0000000000002278.
  • E. Milne, R. Gomez, A. Giannadou, and M. Jones, “Atypical EEG in autism spectrum disorder: Comparing a dimensional and a categorical approach,” J Abnorm Psychol, vol. 128, no. 5, pp. 442–452, Jul. 2019, doi: 10.1037/ABN0000436.
  • M. E. Santarone et al., “EEG Features in Autism Spectrum Disorder: A Retrospective Analysis in a Cohort of Preschool Children,” Brain Sciences 2023, Vol. 13, Page 345, vol. 13, no. 2, p. 345, Feb. 2023, doi: 10.3390/BRAINSCI13020345.
  • M. M. Siddiqui, G. Srivastava, and S. H. Saeed, “Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC,” Sleep Science, vol. 9, no. 3, pp. 186–191, Jul. 2016, doi: 10.1016/J.SLSCI.2016.07.002.
  • C. Spironelli, M. Manfredi, and A. Angrilli, “Beta EEG band: A measure of functional brain damage and language reorganization in aphasic patients after recovery,” Cortex, vol. 49, no. 10, pp. 2650–2660, Nov. 2013, doi: 10.1016/J.CORTEX.2013.05.003.
  • S. Ballanti et al., “EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review,” Clinical Neurophysiology, vol. 144, pp. 98–114, Dec. 2022, doi: 10.1016/J.CLINPH.2022.09.017.
  • J. A. Micoulaud-Franchi, C. Jeunet, A. Pelissolo, and T. Ros, “EEG Neurofeedback for Anxiety Disorders and Post-Traumatic Stress Disorders: A Blueprint for a Promising Brain-Based Therapy,” Curr Psychiatry Rep, vol. 23, no. 12, pp. 1–14, Dec. 2021, doi: 10.1007/S11920-021-01299-9/FIGURES/5.
  • D. A. Moscovitch, D. L. Santesso, V. Miskovic, R. E. McCabe, M. M. Antony, and L. A. Schmidt, “Frontal EEG asymmetry and symptom response to cognitive behavioral therapy in patients with social anxiety disorder,” Biol Psychol, vol. 87, no. 3, pp. 379–385, Jul. 2011, doi: 10.1016/J.BIOPSYCHO.2011.04.009.
  • E. Netzer, A. Frid, and D. Feldman, “Real-time EEG classification via coresets for BCI applications,” Eng Appl Artif Intell, vol. 89, p. 103455, Mar. 2020, doi: 10.1016/J.ENGAPPAI.2019.103455.
  • M. Shen, P. Wen, B. Song, and Y. Li, “Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network,” Biomed Signal Process Control, vol. 82, p. 104566, Apr. 2023, doi: 10.1016/J.BSPC.2022.104566.
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  • W. Garcia-González, W. Flores-Fuentes, O. Sergiyenko, J. C. Rodríguez-Quiñonez, J. E. Miranda-Vega, and D. Hernández-Balbuena, “Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring,” Entropy 2023, Vol. 25, Page 1207, vol. 25, no. 8, p. 1207, Aug. 2023, doi: 10.3390/E25081207.
  • A. Mert and A. Akan, “Seizure onset detection based on frequency domain metric of empirical mode decomposition,” Signal Image Video Process, vol. 12, no. 8, pp. 1489–1496, Nov. 2018, doi: 10.1007/S11760-018-1304-Y/TABLES/2.
  • M. L. Vicchietti, F. M. Ramos, L. E. Betting, and A. S. L. O. Campanharo, “Computational methods of EEG signals analysis for Alzheimer’s disease classification,” Scientific Reports 2023 13:1, vol. 13, no. 1, pp. 1–14, May 2023, doi: 10.1038/s41598-023-32664-8.
  • M. M. Lansbergen, M. Van Dongen-Boomsma, J. K. Buitelaar, and D. Slaats-Willemse, “ADHD and EEG-neurofeedback: A double-blind randomized placebo-controlled feasibility study,” J Neural Transm, vol. 118, no. 2, pp. 275–284, Feb. 2011, doi: 10.1007/S00702-010-0524-2/FIGURES/1.
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  • A. Malekzadeh, A. Zare, M. Yaghoobi, H. R. Kobravi, and R. Alizadehsani, “Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features,” Sensors 2021, Vol. 21, Page 7710, vol. 21, no. 22, p. 7710, Nov. 2021, doi: 10.3390/S21227710.
  • W. Chen et al., “An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy,” BMC Med Inform Decis Mak, vol. 23, no. 1, pp. 1–17, Dec. 2023, doi: 10.1186/S12911-023-02180-W/TABLES/5.
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Toplam 68 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Negin Melek 0000-0001-5297-5545

Erken Görünüm Tarihi 23 Ağustos 2024
Yayımlanma Tarihi 31 Ağustos 2024
Gönderilme Tarihi 14 Şubat 2024
Kabul Tarihi 30 Mayıs 2024
Yayımlandığı Sayı Yıl 2024Cilt: 7 Sayı: 2

Kaynak Göster

IEEE N. Melek, “The Importance of Rhythm Activity in Epilepsy EEG Signal Classification (An Educational Article)”, SAUCIS, c. 7, sy. 2, ss. 138–155, 2024, doi: 10.35377/saucis...1436915.

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