Abstract
Epilepsy is a brain-related disorder of the central nervous system where the neurons of brain show the abnormal behavior at a certain instance of time. Electroencephalogram (EEG) signals play a significant role in the diagnosis of epileptic EEG signals. In the world, overall 50 million people are affected by epilepsy. It is very hard to determine the actions of brain EEG signal, because it contains artifacts or fluctuated information. EEG signal contains different artifacts like EOG, EKG, and ECG. ECG signal artifacts are produced by the function of heart. EOG signal artifacts are produced due to the movement of eyes and EMG signal artifacts are produced because of the muscles coordination. To solve these problems, this paper aims to remove the artifacts present in the EEG signal with the help of wavelet signal processing algorithms (WSA) in signal processing toolbox (Sptool). After removing the artifacts, the parameters have been calculated such as maximum peaks, mean, median, average frequency, variance, and standard deviation. Based on the number of maximum peaks of the EEG signal and with proposed parameters of EEG signal, it is possible to estimate the severity of the seizure.
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References
Adelia, H., Zhoub, Z., Dadmehrc, N.: Analysis of EEG records in an Epileptic patient using wavelet transform. J. Neurosci. Methods 123(1), 69–87 (2003 Feb 15). https://doi.org/10.1016/S0165-0270(02)00340-0
Fausta, O., Acharya, U.R., Adeli, H., Adeli, A.: Wavelet-based EEG processing for computer-aided seizure detection and Epilepsy diagnosis. Eur. J. Epilepsy (2015). https://doi.org/10.1016/j.seizure.2015.01.012
Kaya, Y., Uyar, M., Tekin, R., Yıldırım, S.: 1D-local binary pattern based feature extraction for classification of Epileptic EEG signals. Appl. Math. Comput. 243, 209–219 (2014). https://doi.org/10.1016/j.amc.2014.05.128
Gurumurthy, S., Mahit, V., Ghosh, R.: Analysis and simulation of brain signal data by EEG signal processing technique using MATLAB. Int. J. Eng. Technol. (IJET) 5(3) (2013 June–July). ISSN: 0975-4024
Das, A.B., Bhuiyan, M.I.H., Alam, S.M.S.: Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. Signal Image Video Process. 10(2), 259–266 (2016 Feb)
Ambramovich, F., Sapatinas, T., Stilverman, B.: Wavelet thresholding via a Bayesian approach. J. R. Stat. Soc. Ser B (Stat Methodol) 60(4), 725–749 (1998)
Cisar, P., Cisar, S.M.: The influence of thresholding method on 1D signal denoising using wavelet theory. Published in 2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics. https://doi.org/10.1109/sisy.2012.6339492
EEG waves classifier using wavelet transform and fourier transform. World Acad. Sci. Eng. Technol. Int. J. Bioeng. Life Sci. 1(3) (2007)
Walters-Williams, J., Li, Y.: A new approach to denoising EEG signals-merger of translation invariant wavelet and ICA. Int. J. Biom. Bioinf. 5(2) (2011)
Yu, L.: EEG de-noising based on wavelet transformation. In: 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009, pp. 1–4, June 11–13, 2009
Acknowledgements
Dr. Sasikumar Gurumoorthy, Dr. M. Naresh Babu, G. Chandra Sekhar and G. Sandhyakumari would like to thank Department of Science and Technology (DST) Cognitive Science Research Initiative(CSRI). Ref. No.: SR/CSRI/370/2016.
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Gurumoorthy, S., Muppalaneni, N.B., Chandra Sekhar, G., Sandhya Kumari, G. (2020). Implementation of Signal Processing Algorithms on Epileptic EEG Signals. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_35
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DOI: https://doi.org/10.1007/978-981-15-0135-7_35
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