ROC Analysis of EEG Subbands for Epileptic Seizure Detection using Naïve Bayes Classifier
Keywords:EEG subbands, Haralick features, Naive Bayes, ROC Analysis, epileptic seizures
This paper presents analysis of Electroencephalograms (EEGs) and subbands (delta, theta, alpha, beta, gamma) using image descriptors for epileptic seizure detection. Short-time Fourier transform (STFT) has been utilized to convert 1-D EEG data into image. All subbands are separated from the time-frequency (t-f) matrix and Haralick features of each subband is fed in the Naïve Bayes (NB) classifier. Receiver operating characteristic (ROC) analysis has been used for performance evaluation of classifier. Among all subbands, gamma band alone shows a maximum AUC of 0.98 to classify between ictal and healthy class, while beta band shows a maximum AUC of 0.96 to differentiate between ictal and interictal class. Significance of this work is it shows the medical advantage of different subbands for the detection process.
G. L. Birbeck, “Epilepsy Care in Developing Countries: Part II of II,” Epilepsy Curr., 2010, doi: 10.1111/j.1535-7511.2010.01372.x.
X. Zhao and S. D. Lhatoo, “Seizure detection: do current devices work? And when can they be useful?,” Current Neurology and Neuroscience Reports. 2018, doi: 10.1007/s11910-018-0849-z.
J. Gotman, “Automatic seizure detection: improvements and evaluation,” Electroencephalogr. Clin. Neurophysiol., 1990, doi: 10.1016/0013-4694(90)90032-F.
G. Widman, T. Schreiber, B. Rehberg, A. Hoeft, and C. E. Elger, “Quantification of depth of anesthesia by nonlinear time series analysis of brain electrical activity,” Phys. Rev. E - Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top., 2000, doi: 10.1103/PhysRevE.62.4898.
S. Mamli and H. Kalbkhani, “Gray-level co-occurrence matrix of Fourier synchro-squeezed transform for epileptic seizure detection,” Biocybern. Biomed. Eng., 2019, doi: 10.1016/j.bbe.2018.10.006.
M. Sameer and B. Gupta, “Detection of epileptical seizures based on alpha band statistical features,” Wirel. Pers. Commun., 2020, doi: 10.1007/s11277-020-07542-5.
H. Ocak, “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy,” Expert Syst. Appl., 2009, doi: 10.1016/j.eswa.2007.12.065.
M. Mursalin, Y. Zhang, Y. Chen, and N. V. Chawla, “Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier,” Neurocomputing, 2017, doi: 10.1016/j.neucom.2017.02.053.
A. Subasi, J. Kevric, and M. Abdullah Canbaz, “Epileptic seizure detection using hybrid machine learning methods,” Neural Comput. Appl., 2019, doi: 10.1007/s00521-017-3003-y.
A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, “Epileptic seizure detection in EEGs using time-frequency analysis,” IEEE Trans. Inf. Technol. Biomed., 2009, doi: 10.1109/TITB.2009.2017939.
G. Wang, Z. Deng, and K. S. Choi, “Detection of epilepsy with Electroencephalogram using rule-based classifiers,” Neurocomputing, 2017, doi: 10.1016/j.neucom.2016.09.080.
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., 2018, doi: 10.1016/j.compbiomed.2017.09.017.
I. Ullah, M. Hussain, E. ul H. Qazi, and H. Aboalsamh, “An automated system for epilepsy detection using EEG brain signals based on deep learning approach,” Expert Syst. Appl., 2018, doi: 10.1016/j.eswa.2018.04.021.
A. Şengür, Y. Guo, and Y. Akbulut, “Time–frequency texture descriptors of EEG signals for efficient detection of epileptic seizure,” Brain Informatics, 2016, doi: 10.1007/s40708-015-0029-8.
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, 2018, doi: 10.1109/JBHI.2017.2654479.
L. Boubchir, S. Al-Maadeed, and A. Bouridane, “Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data,” in Proceedings of the International Conference on Microelectronics, ICM, 2014, doi: 10.1109/ICM.2014.7071799.
C. Sun, H. Cui, W. Zhou, W. Nie, X. Wang, and Q. Yuan, “Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on Easy Ensemble Learning,” Int. J. Neural Syst., 2019, doi: 10.1142/S0129065719500217.
M. Li, X. Sun, W. Chen, Y. Jiang, and T. Zhang, “Classification epileptic seizures in EEG using time-frequency image and block texture features,” IEEE Access, 2020, doi: 10.1109/ACCESS.2019.2960848.
R. P. N. Rao, Brain-computer interfacing: An introduction. 2011.
M. Sameer, A. K. Gupta, C. Chakraborty, and B. Gupta, “ROC Analysis for detection of Epileptical Seizures using Haralick features of Gamma band,” 2020, doi: 10.1109/ncc48643.2020.9056027.
H. Adeli, S. Ghosh-Dastidar, and N. Dadmehr, “A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy,” IEEE Trans. Biomed. Eng., 2007, doi: 10.1109/TBME.2006.886855.
M. Sameer, B. Gupta, and R. Priyadarshi, “Classification Between Interictal and Ictal States of Epileptical Patients using Alpha Subband,” Proceeding: International Symposium on 5G & Beyond for Rural Upliftment 2020, pp. 254–258.
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., 2001, doi: 10.1103/PhysRevE.64.061907.
M. K. Kiymik, I. Güler, A. Dizibüyük, and M. Akin, “Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application,” Comput. Biol. Med., 2005, doi: 10.1016/j.compbiomed.2004.05.001.
L. Cohen, “Time-frequency distributions-a review,” Proc. IEEE, vol. 77, no. 7, pp. 941–981, Jul. 1989, doi: 10.1109/5.30749.
R. M. Haralick, I. Dinstein, and K. Shanmugam, “Textural Features for Image Classification,” IEEE Trans. Syst. Man Cybern., 1973, doi: 10.1109/TSMC.1973.4309314.
S. Marsland, Machine learning: An algorithmic perspective. 2014.
M. Sameer, A. K. Gupta, D. C. Chakraborty, and D. B. Gupta, “Epileptical Seizure Detection: Performance analysis of gamma band in EEG signal Using Short-Time Fourier Transform,” in International Symposium on Wireless Personal Multimedia Communications, WPMC, 2019, doi: 10.1109/WPMC48795.2019.9096119.
ANACONDA, “vers. 2-2.4.0, Anaconda Software Distribution. Computer software,” Anaconda Software Distribution. Computer software, 2016.
S. Altunay, Z. Telatar, and O. Erogul, “Epileptic EEG detection using the linear prediction error energy,” Expert Syst. Appl., 2010, doi: 10.1016/j.eswa.2010.02.045.