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

Year 2024, , 138 - 155, 31.08.2024

Negin Melek

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

Abstract

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.

Keywords

EEG Epilepsy Rhythm Feature extraction Classification

References

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Year 2024, , 138 - 155, 31.08.2024

Negin Melek

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

Abstract

References

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  • 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.
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  • 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.
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  • 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.
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  • 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.
  • T. A. Milligan, “Epilepsy: A Clinical Overview,” Am J Med, vol. 134, no. 7, pp. 840–847, Jul. 2021, doi: 10.1016/J.AMJMED.2021.01.038.
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Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Negin Melek 0000-0001-5297-5545

Early Pub Date August 23, 2024
Publication Date August 31, 2024
Submission Date February 14, 2024
Acceptance Date May 30, 2024
Published in Issue Year 2024

Cite

IEEE N. Melek, “The Importance of Rhythm Activity in Epilepsy EEG Signal Classification (An Educational Article)”, SAUCIS, vol. 7, no. 2, pp. 138–155, 2024, doi: 10.35377/saucis...1436915.

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