Comparison of Various Empirical-Mode Decomposition Techniques of EEG for the Diagnostics of Epilepsy

M. S. J. Solaija, S. Saleem, K. Khurshid, et al., “Dynamic mode decomposition based epileptic seizure detection from scalp EEG,” IEEE Access, 6, 38683–38692 (2018); doi: https://doi.org/10.1109/ACCESS.2018.2853125.

Article  Google Scholar 

U. Acharya, S. V. Sree, G. Swapna, et al., “Automated EEG analysis of epilepsy: A review,” Knowledge Based Syst., 45, 147–165 (2013); doi: https://doi.org/10.1016/j.knosys.2013.02.014.

Article  Google Scholar 

W. A. Mir, M. Anjum, Izharuddin, and S. Shahab, “Deep-EEG: an optimized and robust framework and method for EEG-based diagnosis of epileptic seizure,” Diagnostics (Basel), 13, No. 4, 773 (2023); doi: https://doi.org/10.3390/diagnostics13040773.

J. E. Jacob, V. V. Sreelatha, T. Iype, et al., “Diagnosis of epilepsy from interictal EEGs based on chaotic and wavelet transformation,” Analog Integr. Circ. Sig. Process, 89 131–138 (2016); doi: https://doi.org/10.1007/s10470-016-0810-5.

Article  Google Scholar 

V. S. Vijith, J. E. Jacob, T. Iype, et al., “Epileptic seizure detection using non linear analysis of EEG,” Intern. Conf. Invent. Comp. Techn. (ICICT), 3, 1–6 (2016); doi: https://doi.org/10.1109/INVENTIVE.2016.7830193.

Article  Google Scholar 

A. Shoeibi, N. Ghassemi, R. Alizadehsani, et al., “A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals,” Exp. Sys. Appl., 163, 113788 (2021); doi: https://doi.org/10.1016/j.eswa.2020.113788.

Article  Google Scholar 

E. Tuncer and E. D. Bolat, “Channel based epilepsy seizure type detection from electroencephalography (EEG) signals with machine learning techniques,” Biocybern. Eng., 42, No. 2, 575–595 (2022); doi: https://doi.org/10.1016/j.bbe.2022.04.004.

Article  Google Scholar 

R. G. Andrzejak, K. Lehnertz, F. Mormann, et al., “Indications of nonlinear deterministic and finitedimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” 64, No. 6, Pt., 1, 061907 (2001); doi: https://doi.org/10.1103/PhysRevE.64.061907.

J. Jacob, K. Gopakumar, T. Iype, and A. Cherian, “Automated diagnosis of encephalopathy based on empirical mode EEG decomposition,” 50, 278–285 (2018); doi: https://doi.org/10.1007/s11062-018-9749-8.

A.-M. Brouwer, M. A. Hogervorst, J. B. F. van Erp, et al., “Estimating workload using EEG spectral power and ERPs in the n-back task,” J. Neural. Eng., 9, No. 4, 045008 (2012); doi: https://doi.org/10.1088/1741-2560/9/4/045008.

A. Maksimow, M. Särkelä, J. W. Långsjö, et al., “Increase in high frequency EEG activity explains the poor performance of EEG spectral entropy monitor during S-ketamine anesthesia,” Clin. Neurophysiol., 117, No. 8, 1660–1668 (2006); doi: https://doi.org/10.1016/j.clinph.2006.05.011.

Article  CAS  PubMed  Google Scholar 

A. Zhang, B. Yang, and L. Huang, “Feature extraction of EEG signals using power spectral entropy,” in: 2008 International Conference on BioMedical Engineering and Informatics, Sanya, China, 435–439 (2008); doi: https://doi.org/10.1109/BMEI.2008.254.

M. S. A. Megat Ali, M. N. Taib, N. Md. Tahir, and A. H. Jahidin, “EEG spectral centroid amplitude and band power features: A correlation analysis,” in: 2014 IEEE 5th Control and System Graduate Research Colloquium, Shah Alam, Malaysia, 223–226 (2014); doi: https://doi.org/10.1109/ICSGRC.2014.6908726.

M. S. Safi and S. M. M. Safi, “Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters,” Biomed Sign. Proc. Contr., 65, 102338 (2021); doi: https://doi.org/10.1016/j.bspc.2020.102338.

Article  Google Scholar 

V. Bajaj and R. B. Pachori, “Classification of seizure and non-seizure EEG signals using empirical mode decomposition,” IEEE Trans. Inf. Technol. Biomed., 16, No. 6, 1135–1142 (2011); doi: https://doi.org/10.1109/TITB.2011.2181403.

Article  PubMed  Google Scholar 

K. Rai, V. Bajaj, and A. Kumar, “Novel feature for identification of focal EEG signals with K-means and fuzzy C-means algorithms,” in: 2015 IEEE International Conference on Digital Signal Processing (DSP), Singapore, 2015, pp. 412-416, doi: https://doi.org/10.1109/ICDSP.2015.7251904.

E. Gysels, P. Renevey, and P. Celka, “SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in brain–computer interfaces,” Sign. Proc., 85 2178–2189 (2005); doi: https://doi.org/10.1016/j.sigpro.2005.07.008.

Article  Google Scholar 

Z. Yin, Y. Wang, L. Liu, et al., “Cross-subject EEG feature selection for emotion recognition using transfer recursive feature elimination,” Front. Neurorobot., 11, 19 (2017); doi: https://doi.org/10.3389/fnbot.2017.00019.

Article  PubMed  PubMed Central  Google Scholar 

D. K. Thara, B. G. PremaSudha, and F. Xiong, “Autodetection of epileptic seizure events using deep neural network with different feature scaling techniques,” Pattern Rec. Let., 128, 544–550 (2019); doi: https://doi.org/10.1016/j.patrec.2019.10.029.

Article  Google Scholar 

N. E. Huang, Z. Shen, S. R. Long, et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. Math. Phys. Eng. Sci., 454, No. 1971, 903–995 (1998).

Article  Google Scholar 

C. M. Sweeney-Reed, S. J. Nasuto, M. F. Vieira, and A. O. Andrade, “Empirical mode decomposition and its extensions applied to EEG analysis: a review,” Adv. Data Sci. Adapt. Analys., 10, No. 02, 184000 (2018); doi: https://doi.org/10.1142/S2424922X18400016 1.

Z. Xiao-Jun, L. Shi-qin, L.-j. Fan, and X.-l. Yu, “The EEG signal process based on EEMD,” in: 2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing, Wuhan, China, 222–225 (2011); doi: https://doi.org/10.1109/IPTC.2011.67.

Z. Wu and N. E. J. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Adv. Adapt. Data Analys., 1, No. 1, 1–41 (2009); doi: https://doi.org/10.1142/S1793536909000047.

Article  Google Scholar 

M. A. Colominas, G. Schlotthauer, and M. E. Torres, “Improved complete ensemble EMD: A suitable tool for biomedical signal processing,” Biomed. Sign. Proc. Contr., 14, No. 1, 19–29 (2014); doi: https://doi.org/10.1016/j.bspc.2014.06.009.

Article  Google Scholar 

J. E. Jacob, G. K. Nair, T. Iype, and A. Cherian, “Diagnosis of encephalopathy based on energies of EEG subbands using discrete wavelet transform and support vector machine,” Neurol. Res. Int., 2018, No. 1, 1–9 (2018); doi: https://doi.org/10.1155/2018/1613456.

Article  Google Scholar 

S.-H. Oh, Y.-R. Lee, and H.-N. Kim, “A novel EEG feature extraction method using Hjorth parameter,” Int. J. Electron. Electr. Eng., 2, No. 2, 106–110 (2014); doi: https://doi.org/10.12720/ijeee.2.2.106-110.

Article  Google Scholar 

S. Singh and H. Kaur, “An intelligent method for epilepsy seizure detection based on hybrid nonlinear EEG data features using adaptive signal decomposition methods, Circuits Syst. Sign. Proc., 42, 2782–2803 (2023); doi: https://doi.org/10.1007/s00034-022-02223-z.

Article  Google Scholar 

K. Yan and D. Zhang, “Feature selection and analysis on correlated gas sensor data with recursive feature elimination,” Sens. Actuators B Chem., 212, 353–363 (2015); doi: https://doi.org/10.1016/j.snb.2015.02.025.

Article  CAS  Google Scholar 

S. K. R. Chirasani and S. Manikandan, “A deep neural network for the classification of epileptic seizures using hierarchical attention mechanism,” Soft. Comput., 26, No. 11, 5389–5397 (2022); doi: https://doi.org/10.1007/s00500-022-07122-8.

Article  PubMed  PubMed Central  Google Scholar 

A. Vaswani, N. Shazeer, N. Parmar, et al., “Attention is all you need,” Part of Advances in Neural Information Processing Systems 30 (NIPS 2017), 30 (2017); doi: https://doi.org/10.48550/arXiv.1706.03762.

J. E. Jacob, A. Cherian, K. Gopakumar, et al., “Can chaotic analysis of electroencephalogram aid the diagnosis of encephalopathy?,” Neurol. Res. Int., 2018, 8192820 (2018); doi: https://doi.org/10.1155/2018/8192820.

Article  PubMed  PubMed Central  Google Scholar 

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