Model for classifying EEG signals using deep learning to identify epileptic seizures

Authors

  • Department of Computer Science, College of Education for pure Sciences, University of Thi-Qar, Iraq
  • Department of Computer Science, College of Education for pure Sciences, University of Thi-Qar, Iraq

DOI:

https://doi.org/10.32792/jeps.v13i1.246

Keywords:

Artificial Neural Network (ANN), Discrete Wavelet Transform(DWT), electroencephalogram (EEG)

Abstract

The neurological condition known as epilepsy affects the brain in a way that causes recurrent problems.
As a result, seizure identification is crucial to the clinical treatment of epileptic patients. The
electroencephalogram (EEG) has become a crucial tool most of the time, a small number of very
competent professionals manually identify the epileptic EEG signal. Detecting and evaluating epileptic
seizure activity in humans. In this paper, we attempt to automate the detecting procedure. We extract
features using the wavelet transform and derive statistical parameters from the wavelet coefficients that
have been decomposed. Artificial neural network (ANN) is used for the classification. By utilizing the
University of Bonn's benchmark database.

References

H. Ocak, “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and

approximate entropy,” Expert Syst. Appl., vol. 36, no. 2, pp. 2027–2036, 2009.

F. Vecchio, F. Miraglia, and P. M. Rossini, “Connectome: Graph theory application in functional

brain network architecture,” Clin. Neurophysiol. Pract., vol. 2, pp. 206–213, 2017.

P. A. Muñoz-Gutiérrez, E. Giraldo, M. Bueno-López, and M. Molinas, “Localization of active

brain sources from EEG signals using empirical mode decomposition: A comparative study,”

Front. Integr. Neurosci., vol. 12, p. 55, 2018.

J. J. Hopfield, “Artificial neural networks,” IEEE Circuits Devices Mag., vol. 4, no. 5, pp. 3–10,

M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,”

IEEE Trans. Neural Networks, vol. 5, no. 6, pp. 989–993, 1994.

A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, “Epileptic seizure detection in EEGs using

time–frequency analysis,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 5, pp. 703–710, 2009.

P. Swami, T. K. Gandhi, B. K. Panigrahi, M. Tripathi, and S. Anand, “A novel robust diagnostic

model to detect seizures in electroencephalography,” Expert Syst. Appl., vol. 56, pp. 116–130,

L. S. Atlan and S. S. Margulies, “Frequency-dependent changes in resting state

electroencephalogram functional networks after traumatic brain injury in piglets,” J. Neurotrauma,

vol. 36, no. 17, pp. 2558–2578, 2019.

M. Mohammadpoor, A. Shoeibi, and H. Shojaee, “A hierarchical classification method for breast

tumor detection,” Iran. J. Med. Phys., vol. 13, no. 4, pp. 261–268, 2016.

A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG)

classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 31001, 2019.

A. Sharma, J. K. Rai, and R. P. Tewari, “Epileptic seizure anticipation and localisation of

epileptogenic region using EEG signals,” J. Med. Eng. Technol., vol. 42, no. 3, pp. 203–216, 2018.

D. Jacobs, T. Hilton, M. Del Campo, P. L. Carlen, and B. L. Bardakjian, “Classification of preclinical

seizure states using scalp EEG cross-frequency coupling features,” IEEE Trans. Biomed.

Eng., vol. 65, no. 11, pp. 2440–2449, 2018.

M. K. M. Rabby, A. K. M. K. Islam, S. Belkasim, and M. U. Bikdash, “Wavelet transform-based

feature extraction approach for epileptic seizure classification,” in Proceedings of the 2021 ACM

southeast conference, 2021, pp. 164–169.

A. Zahra, N. Kanwal, N. ur Rehman, S. Ehsan, and K. D. McDonald-Maier, “Seizure detection

from EEG signals using multivariate empirical mode decomposition,” Comput. Biol. Med., vol. 88,

pp. 132–141, 2017.

U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli, “Deep convolutional neural

network for the automated detection and diagnosis of seizure using EEG signals,” Comput. Biol.

Med., vol. 100, pp. 270–278, 2018.

R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “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 - Stat. Physics, Plasmas, Fluids,

Relat. Interdiscip. Top., vol. 64, no. 6, p. 8, 2001, doi: 10.1103/PhysRevE.64.061907.

T. Uktveris and V. Jusas, “Development of a modular board for EEG signal acquisition,” Sensors,

vol. 18, no. 7, p. 2140, 2018.

L. Carelli et al., “Brain-computer interface for clinical purposes: cognitive assessment and

rehabilitation,” Biomed Res. Int., vol. 2017, 2017.

V. I. Mironov et al., “Brain-Controlled Biometric Signals Employed to Operate External Technical

Devices,” in Proceedings of the Scientific-Practical Conference" Research and Development-

", 2018, pp. 59–71.

R. Mouleeshuwarapprabu and N. Kasthuri, “Nonlinear vector decomposed neural network based

EEG signal feature extraction and detection of seizure,” Microprocess. Microsyst., vol. 76, p.

, 2020.

T. Zhang, W. Chen, and M. Li, “Fuzzy distribution entropy and its application in automated

seizure detection technique,” Biomed. Signal Process. Control, vol. 39, pp. 360–377, 2018.

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Published

2023-04-10