EEG Signals Classification based on mathematical selection and cosine similarity
DOI:
https://doi.org/10.29304/jqcm.2021.13.3.837Keywords:
EEG Classification, Machine learning algorithms, Fractal, ElectroencephalogramAbstract
This paper presents a new electroencephalogram (EEG) signal classification using a fractal-cosine similarity approach for diagnosing epilepsy patients. The proposed system provides two designed models with PSO as an optimization technique and without optimization. A full classification design is achieved, including prepressing data by normalization, Particle Swarm Optimization (PSO) as optimization technique to reduce the features of EEG signals, Fractal metric computations, metric mapping, and cosine similarity for the final decision. This paper used the BONN university EEG dataset, which consists of five categories. The dataset was divided into four groups based on training set size and testing set size. First, we are used to the training and testing ratio of 90/10. The second case is 80/20, the third case is 70/30, and the final case is 60/40 respectively. The proposed model achieves high rates of accuracy up to 100%.
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References
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[2] Y. Li, X. D. Wang, M. L. Luo, K. Li, X. F. Yang, and Q. Guo, “Epileptic Seizure Classification of EEGs Using Time-Frequency Analysis Based Multiscale Radial Basis Functions,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 2, pp. 386–397, 2018, doi: 10.1109/JBHI.2017.2654479.
[3] A. V. Misiukas Misiūnas, T. Meškauskas, and R. Samaitienė, “Algorithm for automatic EEG classification according to the epilepsy type: Benign focal childhood epilepsy and structural focal epilepsy,” Biomed. Signal Process. Control, vol. 48, pp. 118–127, 2019, doi: 10.1016/j.bspc.2018.10.006.
[4] X. Hu, S. Yuan, F. Xu, Y. Leng, K. Yuan, and Q. Yuan, “Scalp EEG classification using deep Bi-LSTM network for seizure detection,” Comput. Biol. Med., vol. 124, p. 103919, 2020, doi: 10.1016/j.compbiomed.2020.103919.
[5] M. K. Siddiqui, R. Morales-Menendez, X. Huang, and N. Hussain, “A review of epileptic seizure detection using machine learning classifiers,” Brain Informatics, vol. 7, no. 1, pp. 1–18, 2020, doi: 10.1186/s40708-020-00105-1.
[6] J. T. Oliva and J. L. G. Rosa, “Classification for EEG report generation and epilepsy detection,” Neurocomputing, vol. 335, pp. 81–95, 2019, doi: 10.1016/j.neucom.2019.01.053.
[7] Y. Luo et al., “EEG-Based Emotion Classification Using Spiking Neural Networks,” IEEE Access, vol. 8, pp. 46007–46016, 2020, doi: 10.1109/ACCESS.2020.2978163.
[8] S. Lahmiri and A. Shmuel, “Accurate Classification of Seizure and Seizure-Free Intervals of Intracranial EEG Signals from Epileptic Patients,” IEEE Trans. Instrum. Meas., vol. 68, no. 3, pp. 791–796, 2019, doi: 10.1109/TIM.2018.2855518.
[9] S. Raghu, N. Sriraam, A. S. Hegde, and P. L. Kubben, “A novel approach for classification of epileptic seizures using matrix determinant,” Expert Syst. Appl., vol. 127, pp. 323–341, 2019, doi: 10.1016/j.eswa.2019.03.021.
[10] K. Samiee, P. Kovács, and M. Gabbouj, “Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform,” IEEE Trans. Biomed. Eng., vol. 62, no. 2, pp. 541–552, 2015, doi: 10.1109/TBME.2014.2360101.
[11] J. Lian, Y. Zhang, R. Luo, G. Han, W. Jia, and C. Li, “Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network,” IEEE Access, vol. 8, pp. 40008–40017, 2020, doi: 10.1109/ACCESS.2020.2976751.
[12] A. Sharmila and P. Geethanjali, “DWT Based Detection of Epileptic Seizure from EEG Signals Using Naive Bayes and k-NN Classifiers,” IEEE Access, vol. 4, no. c, pp. 7716–7727, 2016, doi: 10.1109/ACCESS.2016.2585661.
[13] E. Kabir, Siuly, J. Cao, and H. Wang, “A computer aided analysis scheme for detecting epileptic seizure from EEG data,” Int. J. Comput. Intell. Syst., vol. 11, no. 1, pp. 663–671, 2018, doi: 10.2991/ijcis.11.1.51.
[14] J. Gou, W. Qiu, Z. Yi, X. Shen, Y. Zhan, and W. Ou, “Locality constrained representation-based K-nearest neighbor classification,” Knowledge-Based Syst., vol. 167, pp. 38–52, 2019, doi: 10.1016/j.knosys.2019.01.016.
[15] Z. Abu-Aisheh, R. Raveaux, and J. Y. Ramel, “Efficient k-nearest neighbors search in graph space,” Pattern Recognit. Lett., vol. 134, pp. 77–86, 2020, doi: 10.1016/j.patrec.2018.05.001.
[16] V. Gupta and M. Mittal, “KNN and PCA classifier with Autoregressive modelling during different ECG signal interpretation,” Procedia Comput. Sci., vol. 125, pp. 18–24, 2018, doi: 10.1016/j.procs.2017.12.005.
[17] M. Savadkoohi, T. Oladunni, and L. Thompson, “A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal,” Biocybern. Biomed. Eng., vol. 40, no. 3, pp. 1328–1341, 2020, doi: 10.1016/j.bbe.2020.07.004.
[18] D. Pradhan, B. Sahoo, B. B. Misra, and S. Padhy, “A multiclass SVM classifier with teaching learning based feature subset selection for enzyme subclass classification,” Appl. Soft Comput. J., vol. 96, p. 106664, 2020, doi: 10.1016/j.asoc.2020.106664.
[19] F. Nie, W. Zhu, and X. Li, “Decision Tree SVM: An extension of linear SVM for non-linear classification,” Neurocomputing, vol. 401, pp. 153–159, 2020, doi: 10.1016/j.neucom.2019.10.051.
[20] J. Wu, “A generalized tree augmented naive Bayes link prediction model,” J. Comput. Sci., vol. 27, pp. 206–217, 2018, doi: 10.1016/j.jocs.2018.04.006.
[21] S. Chen, G. I. Webb, L. Liu, and X. Ma, “A novel selective naïve Bayes algorithm,” Knowledge-Based Syst., vol. 192, no. xxxx, p. 105361, 2020, doi: 10.1016/j.knosys.2019.105361.
[22] H. C. Kim, J. H. Park, D. W. Kim, and J. Lee, “Multilabel naïve Bayes classification considering label dependence,” Pattern Recognit. Lett., vol. 136, pp. 279–285, 2020, doi: 10.1016/j.patrec.2020.06.021.
[23] M. A. Adibi, “Single and multiple outputs decision tree classification using bi-level discrete-continues genetic algorithm,” Pattern Recognit. Lett., vol. 128, pp. 190–196, 2019, doi: 10.1016/j.patrec.2019.09.001.
[24] F. Wang, Q. Wang, F. Nie, Z. Li, W. Yu, and F. Ren, “A linear multivariate binary decision tree classifier based on K-means splitting,” Pattern Recognit., vol. 107, 2020, doi: 10.1016/j.patcog.2020.107521.
[25] S. Wang, Y. Wang, D. Wang, Y. Yin, Y. Wang, and Y. Jin, “An improved random forest-based rule extraction method for breast cancer diagnosis,” Appl. Soft Comput. J., vol. 86, p. 105941, 2020, doi: 10.1016/j.asoc.2019.105941.
[26] J. L. Speiser, M. E. Miller, J. Tooze, and E. Ip, “A comparison of random forest variable selection methods for classification prediction modeling,” Expert Syst. Appl., vol. 134, pp. 93–101, 2019, doi: 10.1016/j.eswa.2019.05.028.
[27] L. Guo, D. Rivero, J. Dorado, J. R. Rabuñal, and A. Pazos, “Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks,” J. Neurosci. Methods, vol. 191, no. 1, pp. 101–109, 2010, doi: 10.1016/j.jneumeth.2010.05.020.
[28] Al-Assadi, T., & Hussain Khalil, Z. (2017). Image Compression By Using Enhanced Fractal Methods. Journal of Al-Qadisiyah for Computer Science and Mathematics, 3(1), 231-241. Retrieved from https://qu.edu.iq/journalcm/index.php/journalcm/article/view/256
[29] Hadi, A., & Shareef, D. W. (2020). In-Situ Event Localization for Pipeline Monitoring System Based Wireless Sensor Network Using K-Nearest Neighbors and Support Vector Machine. Journal of Al-Qadisiyah for Computer Science and Mathematics, 12(3), Comp Page 1 -. https://doi.org/10.29304/jqcm.2020.12.3.705
[30] Mohammed, Y., & Saleh, E. (2020). Investigating the Applicability of Logistic Regression and Artificial Neural Networks in Predicting Breast Cancer. Journal of Al-Qadisiyah for Computer Science and Mathematics, 12(2), Math Page 63-. https://doi.org/10.29304/jqcm.2020.12.2.697
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Published
2021-08-30
How to Cite
Al-fraiji, S. S., & Al-Shammary, D. (2021). EEG Signals Classification based on mathematical selection and cosine similarity. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(3), Comp Page 57 – 67. https://doi.org/10.29304/jqcm.2021.13.3.837
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Computer Articles
