Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method

Tri Kurnia Sari; Dian Palupi Rini; Samsuryadi Samsuryadi

doi:10.18495/comengapp.v12i2.429

[1]I. R. Dwi Saputro, N. D. Maryati, S. R. Solihati, I. Wijayanto, S. Hadiyoso, and R. Patmasari, “Seizure Type Classification on EEG Signal using Support Vector Machine,” J. Phys. Conf. Ser., vol. 1201, no. 1, 2019, doi: 10.1088/1742-6596/1201/1/012065.

[2]K. M. Almustafa, “Classification of epileptic seizure dataset using different machine learning algorithms,” Informatics Med. Unlocked, vol. 21, p. 100444, 2020, doi: 10.1016/j.imu.2020.100444.

[3]M. H. Aslam et al., “Classification of EEG Signals for Prediction of Epileptic Seizures,” Appl. Sci., vol. 12, no. 14, p. 7251, 2022, doi: 10.3390/app12147251.

[4]R. Anggara and Y. Rahayu, “Sistem Electroencephalogram ( EEG ) Untuk Analisis Sinyal Gelombang Otak Pada Pasien Depresi,” JOM FTEKNIK, vol. 7, pp. 1–6,2020.

[5]N. B. Aji and H. Tjandrasa, “Klasifikasi Eeg Epilepsi Menggunakan Singular Spectrum Analysis, Power Spectral Density Dan Convolution Neural Network,” JUTI J. Ilm. Teknol. Inf., vol. 15, no. 2, p. 185, 2017, doi: 10.12962/j24068535.v15i2.a662.

[6]W. L. Mao, H. I. K. Fathurrahman, Y. Lee, and T. W. Chang, “EEG dataset classification using CNN method,” J. Phys. Conf. Ser., vol. 1456, no. 1, 2020, doi: 10.1088/1742-6596/1456/1/012017.

[7]I. B. Santoso, Y. Adrianto, A. D. Sensusiati, D. P. Wulandari, and I. K. E. Purnama, “Epileptic EEG Signal Classification Using Convolutional Neural Network Based on Multi-Segment of EEG Signal,” Int. J. Intell. Eng. Syst., vol. 14, no. 3, pp. 160–176, 2021, doi: 10.22266/ijies2021.0630.15.

[8]Z. Wen, R. Xu, and J.Du, “A novel convolutional neural networks for emotion recognition based on EEG signal,” 2017 Int. Conf. Secur. Pattern Anal. Cybern. SPAC 2017, vol. 2018-Janua, pp. 672–677, 2018, doi: 10.1109/SPAC.2017.8304360.

[9]M. A. Saleem, N. Senan, F. Wahid, M. Aamir, A. Samad, and M. Khan, “Comparative Analysis of Recent Architecture of Convolutional Neural Network,” Math. Probl. Eng., vol. 2022, 2022, doi: 10.1155/2022/7313612.

[10]Y. Sun, B. Xue, M. Zhang, G. G. Yen, and J. Lv, “Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification,” IEEE Trans. Cybern., vol. 50, no. 9, pp. 3840–3854, 2020, doi: 10.1109/TCYB.2020.2983860.

[11]R. Abiyev, M. Arslan, J. B. Idoko, B. Sekeroglu, and A. Ilhan, “Identification of epileptic eeg signals using convolutional neural networks,” Appl. Sci., vol. 10, no. 12, 2020, doi: 10.3390/APP10124089.

[12]M. Ezar Al Rivan and A. Giovri Riyadi, “Perbandingan Arsitektur LeNet dan AlexNet Pada Metode Convolutional Neural Network Untuk Pengenalan American Sign Language,” J. Komput. Terap., vol. 7, no. Vol. 7 No. 1 (2021), pp. 53–61, 2021, doi: 10.35143/jkt.v7i1.4489.

[13]M. Kayed, A. Anter, and H. Mohamed, “Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture,” Proc. 2020 Int. Conf. Innov.
Trends Commun. Comput. Eng. ITCE 2020, no. February, pp. 238–243, 2020, doi: 10.1109/ITCE48509.2020.9047776.

[14]S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sist. dan Teknol. Inf. Indones., vol. 3, no. 2, pp. 49–56, 2018.

[15]M. Resa, A. Yudianto, and H. Al Fatta,“Analisis Pengaruh Tingkat Akurasi Klasifikasi Citra Wayang dengan Algoritma Convolutional Neural Network,” J. Teknol. Inf., vol. 4, no. 2, pp. 182–190, 2020.

[16]S. Albawi, T. A. M. Mohammed, and S. Alzawi, “Understanding of a Convolutional Neural Network,” 2017 Int. Conf. Eng. Technol., pp. 1–6, 2017.

[17]D. Irfansyah, M. Mustikasari, and A. Suroso, “Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi,” J. Inform. J. Pengemb. IT, vol. 6, no. 2, pp. 87–92, 2021, [Online]. Available: http://ejournal.poltektegal.ac.id/index.php/informatika/article/view/2802.

[18]M. R. Alwanda, R. P. K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” J. Algoritm., vol. 1, no. 1, pp. 45–56, 2020, doi: 10.35957/algoritme.v1i1.434.

[19]D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021.

Abstract viewed - 507 times
PDF downloaded - 482 times

Affiliations

Tri Kurnia Sari
Universitas Sriwijaya

Dian Palupi Rini
Universitas Sriwijaya

Samsuryadi Samsuryadi
Universitas Sriwijaya

Content Top

Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method

Tri Kurnia Sari ,
Dian Palupi Rini ,
Samsuryadi Samsuryadi

Vol 12 No 2 (2023)

DOI 10.18495/comengapp.v12i2.429

Submitted: Dec 20, 2022

Published: Jun 1, 2023

Abstract

The brain is one of the most important organs in the human body as a central nervous system which functions as a controlling center, intelligence, creativity, emotions, memories, and body movements. Epileptic seizure is one of the disorder of the brain central nervous system which has many symptoms, such as loss of awareness, unusual behavior and confusion. These symptoms lead in many cases to injuries due to falls, biting one’s tongue. Detecting a possible seizure beforehand is not an easy task. Most of the seizures occur unexpectedly, and finding ways to detect a possible seizure before it happens has been a challenging task for many researchers. Analyzing EEG signals can help us obtain information that can be used to diagnose normal brain activity or epilepsy. CNN has been demonstrated high performance on detection and classification epileptic seizure. This research uses CNN to classify the epilepsy EEG signal dataset. AlexNet and LeNet-5 are applied in CNN architecture. The result of this research is that the AlexNet architecture provides better precision, recall, and f1-score values on the epilepsy signal EEG data than the LeNet-5 architecture.