Feature Extraction of EEG Signals for Seizure Detection Using Machine Learning Algorthims
Received: 18 July 2022 | Revised: 1 August 2022 | Accepted: 2 August 2022 | Online: 2 October 2022
Corresponding author: M. A. Alsuwaiket
Abstract
Epilepsy is a central nervous system disorder in which brain activity becomes abnormal and causes periods of unusual behavior and sometimes loss of awareness. Epilepsy is a disease that may affect males or females of all ethnic groups and ages. Detecting seizures is challenging due to the difference in human behaviors and brain signals. This paper aims to automate the extraction of electroencephalogram (EEG) signals without referring to doctors using two feature extraction methods, namely Wavelet Packet decomposition (WPD) and Genetic Algorithm-Based Frequency-Domain Feature Search (GAFDS). Three machine learning algorithms were applied, namely Conventional Neural Networks (CNNs), Support Vector Machine (SVM), and Random Forest (RF) to diagnose epileptic seizures. The results achieved from the classifiers show a higher accuracy rate using CNNs as a classifier and GAFDS as feature extraction reaching 97.93% accuracy while the accuracy rate of the SVM and RF was 94.49% and 88.03% respectively.
Keywords:
EEG, CNN, SVM, seizure, feature extractionDownloads
References
Z. Wang, G. Healy, A. F. Smeaton, and T. E. Ward, "A review of feature extraction and classification algorithms for image RSVP based BCI," in Signal Processing and Machine Learning for Brain-Machine Interfaces, T. Tanaka and A. Mahnaz, Eds. Stevenage, England: The Institute of Engineering and Technology, 2018, pp. 243–270.
Y. Zhang, Y. Zhang, J. Wang, and X. Zheng, "Comparison of classification methods on EEG signals based on wavelet packet decomposition," Neural Computing and Applications, vol. 26, no. 5, pp. 1217–1225, Jul. 2015. DOI: https://doi.org/10.1007/s00521-014-1786-7
S. Raghu, N. Sriraam, and G. P. Kumar, "Effect of Wavelet Packet Log Energy Entropy on Electroencephalogram (EEG) Signals," International Journal of Biomedical and Clinical Engineering, vol. 4, no. 1, pp. 32–43, Jan. 2015. DOI: https://doi.org/10.4018/IJBCE.2015010103
D. A. Torse and V. V. Desai, "Design of adaptive EEG preprocessing algorithm for neurofeedback system," in International Conference on Communication and Signal Processing, Melmaruvathur, India, Apr. 2016, pp. 392–395. DOI: https://doi.org/10.1109/ICCSP.2016.7754164
V. Krishnan and B. Anto P, "Features of wavelet packet decomposition and discrete wavelet transform for malayalam speech recognition," International Journal of Recent Trends in Engineering, vol. 1, no. 2, pp. 93–96, 2009.
H. Shimizu, K. Yasuoka, K. Uchiyama, and S. Shioya, "Bioprocess Fault Detection by Nonlinear Multivariate Analysis: Application of an Artificial Autoassociative Neural Network and Wavelet Filter Bank," Biotechnology Progress, vol. 14, no. 1, pp. 79–87, 1998. DOI: https://doi.org/10.1021/bp9701372
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," Computers in Biology and Medicine, vol. 100, pp. 270–278, Sep. 2018. DOI: https://doi.org/10.1016/j.compbiomed.2017.09.017
S. Ruder, "An overview of gradient descent optimization algorithms." arXiv, Jun. 15, 2017.
P. Boonyakitanont, A. Lek-uthai, K. Chomtho, and J. Songsiri, "A Comparison of Deep Neural Networks for Seizure Detection in EEG Signals." bioRxiv, Jul. 15, 2019, Art. no. 702654. DOI: https://doi.org/10.1101/702654
L. Chaari, Ed., Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. New York, NY, USA: Springer, 2019. DOI: https://doi.org/10.1007/978-3-030-11800-6
Seferkurnaz and A. A. Saleh, "Comparative and Analysis Study of normal and epileptic seizure EEG signals by using various classification Algorithms," IOSR Journal of Computer Engineering, vol. 20, no. 4, pp. 23–33, Jul. 2018.
M. Zhou et al., "Epileptic Seizure Detection Based on EEG Signals and CNN," Frontiers in Neuroinformatics, vol. 12, Dec. 2018, Art. no. 95. DOI: https://doi.org/10.3389/fninf.2018.00095
A. Nandy, M. A. Alahe, S. M. Nasim Uddin, S. Alam, A.-A. Nahid, and Md. A. Awal, "Feature Extraction and Classification of EEG Signals for Seizure Detection," in International Conference on Robotics,Electrical and Signal Processing Techniques, Dhaka, Bangladesh, Jan. 2019, pp. 480–485. DOI: https://doi.org/10.1109/ICREST.2019.8644337
J. Wu, T. Zhou, and T. Li, "Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting," Entropy, vol. 22, no. 2, Feb. 2020, Art. no. 140. DOI: https://doi.org/10.3390/e22020140
H. Liu, L. Xi, Y. Zhao, and Z. Li, "Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data." arXiv, Oct. 06, 2019. DOI: https://doi.org/10.11648/j.mlr.20190403.11
A. L. Goldberger et al., "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals," Circulation, vol. 101, no. 23, pp. E215-220, Jun. 2000. DOI: https://doi.org/10.1161/01.CIR.101.23.e215
K. Ueki and T. Kobayashi, "Fusion-Based Age-Group Classification Method Using Multiple Two-Dimensional Feature Extraction Algorithms," Ieice Transactions on Information and Systems, vol. E90-D, no. 6, pp. 923–934, Jun. 2007. DOI: https://doi.org/10.1093/ietisy/e90-d.6.923
T. Wen and Z. Zhang, "Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification," Medicine, vol. 96, no. 19, May 2017, Art. no. e6879. DOI: https://doi.org/10.1097/MD.0000000000006879
J. Seo, T. H. Laine, and K.-A. Sohn, "Machine learning approaches for boredom classification using EEG," Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 10, pp. 3831–3846, Oct. 2019. DOI: https://doi.org/10.1007/s12652-019-01196-3
Y.-D. Cai, G.-P. Zhou, and K.-C. Chou, "Support Vector Machines for Predicting Membrane Protein Types by Using Functional Domain Composition," Biophysical Journal, vol. 84, no. 5, pp. 3257–3263, May 2003. DOI: https://doi.org/10.1016/S0006-3495(03)70050-2
M. Al Lababede, A. Blasi, and M. Alsuwaiket, "Mosques Smart Domes System using Machine Learning Algorithms," International Journal of Advanced Computer Science and Applications, vol. 11, no. 3, pp. 373–378, Mar. 2020. DOI: https://doi.org/10.14569/IJACSA.2020.0110347
R. Aroud, A. Blasi, and M. Alsuwaiket, "Intelligent Risk Alarm for Asthma Patients using Artificial Neural Networks," International Journal of Advanced Computer Science and Applications, vol. 11, no. 6, pp. 95–100, Jun. 2020. DOI: https://doi.org/10.14569/IJACSA.2020.0110612
A. Blasi, "Performance Increment of High School Students using ANN model and SA algorithm," Journal of Theoretical and Applied Information Technology, vol. 95, no. 11, pp. 2417–2425, Jan. 2017.
A. Blasi, "Scheduling Food Industry System using Fuzzy Logic," Journal of Theoretical and Applied Information Technology, vol. 96, no. 19, pp. 6463–6473, Oct. 2018.
A. H. Blasi, M. A. Abbadi, and R. Al-Huweimel, "Machine Learning Approach for an Automatic Irrigation System in Southern Jordan Valley," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6609–6613, Feb. 2021. DOI: https://doi.org/10.48084/etasr.3944
A. H. Blasi and M. Alsuwaiket, "Analysis of Students’ Misconducts in Higher Education using Decision Tree and ANN Algorithms," Engineering, Technology & Applied Science Research, vol. 10, no. 6, pp. 6510–6514, Dec. 2020. DOI: https://doi.org/10.48084/etasr.3927
M. Alsuwaiket, A. H. Blasi, and K. Altarawneh, "Refining Student Marks based on Enrolled Modules’ Assessment Methods using Data Mining Techniques," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5205–5210, Feb. 2020. DOI: https://doi.org/10.48084/etasr.3284
M. Alsuwaiket, A. H. Blasi, and R. A. Al-Msie’deen, "Formulating Module Assessment for Improved Academic Performance Predictability in Higher Education," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4287–4291, Jun. 2019. DOI: https://doi.org/10.48084/etasr.2794
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