References
- World Health Organization. (2005). Atlas: Epilepsy Care in the World. ISBN 92-4-156303-6.
- Anderson, C. W., Sijercic, Z. (1996). Classification of EEG signals from four subjects during five mental tasks. In Proceedings International Conference on Engineering Applications of Neural Networks (EANN’96). Systems Engineering Association, 407-414.
- Kalaycı, T., Özdamar, O. (1995). Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Engineering in Medicine and Biology Magazine, 14 (2), 160-166. https://doi.org/10.1109/51.376754
- Subasi A., Ismail Gursoy, M. (2010). EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Systems with Applications, 37 (12), 8659-8666. https://doi.org/10.1016/j.eswa.2010.06.065
- Oğulata, S. N., Şahin, C., Erol, R. (2009). Neural network-based computer-aided diagnosis in classification of primary generalized epilepsy by EEG signals. Journal of Medical Systems, 33 (2), 107-112. https://doi.org/10.1007/s10916-008-9170-8
- Gandhi, T. K., Chakraborty, P., Roy, G. G., Panigrahi, B. K. (2012). Discrete harmony search based expert model for epileptic seizure detection in electroencephalography. Expert Systems with Applications, 39 (4), 4055-4062. https://doi.org/10.1016/j.eswa.2011.09.093
- Krishnaveni, V, Jayaraman, S., Gunasekaran, A., Ramadoss, K. (2008). Automatic removal of ocular artifacts using JADE algorithm and neural network. International Journal of Information, Control and Computer Sciences, 1.0 (4). https://doi.org/10.5281/zenodo.1333386
- Loyek, C., Woermann, F. G., Nattkemper, T. W. (2008). Detection of focal cortical dysplasia lesions in MRI using textural features. In Bildverarbeitung für die Medizin 2008. Springer, 432-436. https://doi.org/10.1007/978-3-540-78640-5_87
- Clark, M. C., Hall, L. O., Goldgof, D. B., Clarke, L.P., Velthuizen, R. P., Silbiger, M. S. (1994). MRI segmentation using fuzzy clustering techniques. IEEE Engineering in Medicine and Biology Magazine, 13 (5), 730-742. https://doi.org/10.1109/51.334636
- Sujitha, V., Sivagami, P., Vijaya, M. S. (2011). Predicting epileptic seizure from MRI using fast single shot proximal support vector machine. In ICWET ‘11: Proceedings of the International Conference & Workshop on Emerging Trends in Technology. ACM, 525-529. https://doi.org/10.1145/1980022.1980135
- Martisius, I., Birvinskas, D., Jusas, V., Tamosevicius, Z. (2011). A 2-D DCT hardware codec based on Loeffler algorithm. Electronics and Electrical Engineering, 113 (7), 47-50. http://dx.doi.org/10.5755/j01.eee.113.7.611
- Orlowski, P. (2010). Simplified design of low-pass linear parameter-varying, finite impulse response filters. Information Technology and Control, 39 (2), 130-137. https://itc.ktu.lt/index.php/ITC/article/view/12301
- Sharif, B., Jafari, A. H. (2017). Prediction of epileptic seizures from EEG using analysis of ictal rules on Poincaré plane. Computer Methods and Programs in Biomedicine, 145, 11-22. https://doi.org/10.1016/j.cmpb.2017.04.001
- Mutlu, A. Y. (2018). Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition. Biomedical Signal Processing and Control, 40, 33-40. https://doi.org/10.1016/j.bspc.2017.08.023
- Kaleem, M., Guergachi, A., Krishnan, S. (2018). Patient-specific seizure detection in long-term EEG using wavelet decomposition. Biomedical Signal Processing and Control, 46, 157-165. https://doi.org/10.1016/j.bspc.2018.07.006
- Gupta, V., Pachori, R. B. (2019). Epileptic seizure identification using entropy of FBSE based EEG rhythms. Biomedical Signal Processing and Control, 53, 101569. https://doi.org/10.1016/j.bspc.2019.101569
- de la O Serna, J. A., Paternina, M. R. A., Zamora-Méndez, A., Tripathy, R. K., Pachori, R. B. (2020). EEG-rrhythm specific Taylor–Fourier filter bank implemented with O-splines for the detection of epilepsy using EEG signals. IEEE Sensors Journal, 20 (12), 6542-6551. https://doi.org/10.1109/JSEN.2020.2976519
- Delorme, A., Sejnowski, T., Makeig, S. (2007). Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage, 34 (4), 1443-1449. https://doi.org/10.1016/j.neuroimage.2006.11.004
- Krishnaveni, V., Jayaraman, S., Manoj Kumar, P. M., Shivakumar, K., Ramadoss, K. (2005). Comparison of independent component analysis algorithms for removal of ocular artifacts from electroencephalogram. Measurement Science Review, 5 (2), 67-78. https://www.measurement.sk/2005/S2/krishnaveni.pdf
- Demirezen Yağmur, F., Sertbaş, A. (2020). Automatic diagnosis of epilepsy from EEG signals using Discrete Cosine Transform. In 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE. https://doi.org/10.1109/SIU49456.2020.9302300
- Chandaka, S., Chatterjee, A., Munshi, S. (2009). Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Systems with Applications, 36 (2), 1329-1336. https://doi.org/10.1016/j.eswa.2007.11.017
- Kumar, Y., Dewal, M. L., Anand, R. S. (2014). Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. Signal, Image and Video Processing, 8 (7), 1323-1334. https://doi.org/10.1007/s11760-012-0362-9
- Gumaste, P. P., Jadhav, D. V. (2015). Image segmentation techniques for brain MRI images: A survey. International Journal of Modern Trends in Engineering and Research (IJMTER), 2 (7).
- Demirezen Yağmur, F., Sertbaş, A. (2018). Diagnosis of epilepsy in brain MR images using DCT-means. In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2439-2443. https://doi.org/10.1109/SIU.2018.8404758
- Meyer-Bäse, A. (2004). Pattern Recognition in Medical Imaging. Academic Press, 22-35. https://doi.org/10.1016/B978-0-12-493290-6.X5000-7
- Abramowitz, M., Stegun, I. A. (1972). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables; 9th printing. Dover Publications, ISBN 0-486-61272-4.
- Gupta, A., Singh, P., Karlekar, M. (2018). A novel signal modeling approach for classification of seizure and seizure-free EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26 (5), 925-935. https://doi.org/10.1109/TNSRE.2018.2818123
- Deivasigamani, S., Senthilpari, C., Yong, W. H. (2021). Machine learning method based detection and diagnosis for epilepsy in EEG signal. Journal of Ambient Intelligence and Humanized Computing, 12, 4215-4221. https://doi.org/10.1007/s12652-020-01816-3