Abstract
In recent days, local binary pattern and their variants plays a vital role in classification of EEG signals. Hence, in this paper a novel method for classification of EEG signals in accordance with local binary pattern is proposed. Initially, the EEG signal with 9 points is considered and then the average of the signal points existing at various distances such as 1, 2 and 3 are computed. Then, the computed average value for an EEG segment is compared with the center signal point of an EEG segment and thus yields the binary value 0 or 1. Later, the majority rule is employed resulting in 8 bit binary pattern. Also, the average of EEG segment is compared with center point. The decimal equivalent of the generated binary code is referred as the proposed label. Later, the histogram is generated involving the proposed label and then this histogram are used as the feature for an EEG signal. The experimental results on Bonn and Freiburg dataset shows that the proposed method achieves 75.81% and 94.43%, 72.43% and 89.30% in sensitivity and specificity, respectively. Also, the performance is evaluated in terms of classification accuracy with KNN and SVMclassifier by changing the training and testing data. The results indicate that the proposed method could distinguish the seizure and seizure free EEG signals evidently.
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Data availability
The datasets generated during and/or analysed during the current study are available in the following link http://www.uniklinik-freiburg.de/epilepsie.html. Accessed 2014 May 18.
References
Iasemidis, L. D., Shiau, D.-S., Chaovalitwongse, W., Sackellares, J. C., Pardalos, P. M., Principe, J. C., Carney, P. R., Prasad, A., Veeramani, B., & Tsakalis, K. (2003). Adaptive epileptic seizure prediction system. IEEE Transactions on Biomedical Engineering, 50(5), 616–627.
Jing, J., Pang, X., Pan, Z., Fan, F., & Meng, Z. (2022). Classification and identification of epileptic EEG signals based on signal enhancement. Biomedical Signal Processing and Control, 71, 103248.
Acharya, U. R., Sree, S. V., Swapna, G., Martis, R. J., & Suri, J. S. (2013). Automated EEG analysis of epilepsy: A review. Knowledge-Based Systems, 45, 147–165.
Guler, I., & Ubeyli, E. D. (2007). Multiclass support vector machines for EEG-signals classification. IEEE Transactions On Information Technology In Biomedicine, 11(2), 117–126.
Adeli, H., Zhou, Z., & Dadmehr, N. (2003). Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods, 123(1), 69–87.
Khakon, D., Debashis, D., Partha Pratim, R., Atri, C., & Shankar Prasad, S. (2020). Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of eeg signal. Biomedical Signal Processing and Control, 57, 101720.
Sankar, R., & Natour, J. (1992). Automatic computer analysis of transients in EEG. Computers in Biology & Medicine, 22(6), 407–422.
Kaya, Y. (2015). Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis. Australasian Physical and Engineering Sciences in Medicine, 38(3), 435–446.
Kumar, T. S., Kanhangad, V., & Pachori, R. B. (2015). Classification of seizure and seizure-free EEG signals using local binary patterns. Biomedical Signal Processing and Control, 1, 33–40.
Sairamya, N. J., Thomas George, S., Narain Ponraj, D., & Subathra, M. S. P. (2018). Detection of epileptic EEG signal using improved local pattern transformation methods. Circuits, Systems, and Signal Processing, 37(12), 5554–5575.
Samiee, K., Kovács, P., & Gabbouj, M. (2017). Epileptic seizure detection in long-term EEG records using sparse rational decomposition and local Gabor binary patterns feature extraction. Knowledge-Based Systems, 118, 228–240.
Thodoroff, P., Pineau, J., & Lim, A. (2016). Learning robust features using deep learning for automatic seizure detection. Proceedings of Machine Learning and Healthcare, 56, 178–190.
Guerrero, M. C., Parada, J. S., & Espitia, H. E. (2021). EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Heliyon, 7(6), e07258.
Jaiswal, A. K., & Banka, H. (2017). Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomedical Signal Processing and Control, 34, 81–92.
Fergus, P., Hussain, A., David Hignett, D., Al-Jumeily, K.-A., & Hamdan, H. (2016). A machine learning system for automated whole-brain seizure detection. Applied Computing and Informatics, 12(1), 70–89.
Sairamya, N. J., Thomas George, S., Balakrishnan, R., et al. (2018). An effective approach to classify epileptic EEG signal using local neighbor gradient pattern transformation methods. Australasian Physical and Engineering Sciences in Medicine, 41, 1029–1046.
Han, Y., Wang, B., Luo, J., Li, L., & Li, X. (2022). A classification method for EEG motor imagery signals based on parallel convolutional neural network. Biomedical Signal Processing and Control, 71, 103190.
Buriro, A. B., Ahmed, B., Baloch, G., Ahmed, J., Shoorangiz, R., Weddell, S. J., & Jones, R. D. (2021). Classification of alcoholic EEG signals using wavelet scattering transform-based features. Computers in Biology and Medicine, 139, 104969.
Tuncer, T., Sengul Dogan, U., & Acharya, R. (2021). Automated EEG signal classification using chaotic local binary pattern. Expert systems with Applications, 182, 115175.
Ech-Choudany, Y., Scida, D., Assarar, M., Landré, J., Bellach, B., & Morain-Nicolier, F. (2021). Dissimilarity-based time–frequency distributions as features for epileptic EEG signal classification. Biomedical signal processing and control, 64, 102268.
Khan, K. A., Shanir, P. P., Khan, Y. U., & Farooq, O. (2020). Hybrid Local Binary Pattern and wavelets based approach for EEG classification for diagnosing epilepsy. Expert Systems With Applications, 140, 112895.
Jaiswal, A. K., & Banka, H. (2018). Local transformed features for epileptic seizure detection in EEG signal. Journal of Medical and Biological Engineering, 38, 222–235.
Nithya, S., & Ramakrishnan, S. (2021). Wavelet domain majority coupled binary pattern: A new descriptor for texture classification. Pattern Analysis and Applications, 24(1), 393–408.
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6), 061907.
Klatt, J., et al. (2012). The EPILEPSIAE database: An extensive electroencephalography database of epilepsy patients. Epilepsia, 53(9), 1669–1676.
Malekzadeh, A., Zare, A., Yaghoobi, M., & Alizadehsani, R. (2021). Automatic diagnosis of epileptic seizures in EEG signals using fractal dimension features and convolutional autoencoder method. Big Data and Cognitive Computing, 5(4), 78.
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Authors would like to thank the Management, Secretary, Principal of Dr. Mahalingam College of Engineering and Technology, Pollachi for their support during the research work.
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Nithya, S., Ramakrishnan, S., Murugavel, A.S.M. et al. Detection of Epileptic Seizure from EEG Signals Using Majority Rule Based Local Binary Pattern. Wireless Pers Commun 134, 721–734 (2024). https://doi.org/10.1007/s11277-024-10916-8
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DOI: https://doi.org/10.1007/s11277-024-10916-8