Abstract
An adaptive time-frequency (t-f) representation of electroencephalographic (EEG) signals with high time and frequency resolutions using wavelet packet decomposition are introduced in this paper for automated diagnosis of epilepsy. The novel texture pattern techniques namely local neighbor descriptive pattern (LNDP) and symmetric weighted LNDP (SWLNDP) are proposed to obtain distinct features from the t-f images. Proposed texture pattern techniques are insensitive to local and global variations as the consecutive neighboring pixels are compared. SWLNDP is a modified version of LNDP which improves the computational efficiency of the system by reducing the feature vector length. The histogram based features are extracted from the texture pattern of t-f images and fed into artificial neural network (ANN) for classification of signals. The obtained results show that ANN attained an accuracy of 100% using proposed techniques for classifying epileptic and normal signal. Further the performance of the proposed system was analyzed for fifteen different cases using University of Bonn EEG dataset.
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References
Li, Y., Wang, X., Luo, L., Li, K., Yang, X., Guo, Q.: Epileptic seizure classification of EEGs using time-frequency analysis based multiscale radial basis functions. IEEE J. Biomed. Health Inform. 99, 1 (2017)
Polat, K., Güneş, S.: Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput. 187(2), 1017–1026 (2007)
Sharmila, A., Geethanjali, P.: DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers. IEEE Access 4, 7716–7727 (2016)
Riaz, F., Hassan, A., Rehman, S., Niazi, I.K., Dremstrup, K.: EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans. Neural Syst. Rehabil. Eng. 24(1), 28–35 (2016)
Zhang, T., Chen, W., Li, M.: Fuzzy distribution entropy and its application in automated seizure detection technique. Biomed. Signal Process. Control 39, 360–377 (2018)
Jaiswal, A.K., Banka, H.: Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed. Signal Process. Control 34, 81–92 (2017)
Supriya, S., Siuly, S., Zhang, Y.: Automatic epilepsy detection from EEG introducing a new edge weight method in the complex network. Electron. Lett. 52(17), 1430–1432 (2016)
Fu, K., Qu, J., Chai, Y., Dong, Y.: Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomed. Signal Process. Control 13, 15–22 (2014)
Boashash, B., Boubchir, L., Azemi, G.: A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals. EURASIP J. Adv. Signal Process. 2012(1), 117 (2012)
Şengür, A., Guo, Y., Akbulut, Y.: Time–frequency texture descriptors of EEG signals for efficient detection of epileptic seizure. Brain Inform. 3(2), 101–108 (2016)
Seo, Y., Kim, S.: River stage forecasting using wavelet packet decomposition and data-driven models. Procedia Eng. 154, 1225–1230 (2016)
Kumar, T.S., Kanhangad, V.: Automated obstructive sleep apnoea detection using symmetrically weighted local binary patterns. Electron. Lett. 53(4), 212–214 (2017)
Huang, W., Yin, H.: Robust face recognition with structural binary gradient patterns. Pattern Recogn. 68, 126–140 (2012)
Andrzejak, R.G., Lehnertz, K., Rieke, C., Mormann, F., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. 64, 061907 (2001)
Boubchir, L., Al-Maadeed, S., Bouridane, A.: Haralick feature extraction from time–frequency images for epileptic seizure detection and classification of EEG data. In: The 26th IEEE International Conference on Microelectronics, pp. 32–35 (2014)
Boubchir, L., Al-Maadeed, S., Bouridane, A., Chérif, A.A.: Classification of EEG signals for detection of epileptic seizure activities based on LBP descriptor of time-frequency images. In: IEEE International Conference on Image Processing (ICIP), Quebec City, QC, pp. 3758–3762 (2015)
Acknowledgements
This paper work was endorsed by the “Technology Systems Development Programme (TSDP)” under Department of Science and Technology (DST), Ministry of Science and Technology, Government of India (GoI), [Grant Number—DST/TSG/ICT/2015/54-G, 2015].
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Sairamya, N.J., Thomas George, S., Subathra, M.S.P., Kumar, N.M. (2019). Computer-Aided Diagnosis of Epilepsy Based on the Time-Frequency Texture Descriptors of EEG Signals Using Wavelet Packet Decomposition and Artificial Neural Network. In: Mallick, P., Balas, V., Bhoi, A., Zobaa, A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_64
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