A Classification Approach for Focal/Non-focal EEG Detection Using Cepstral Analysis

Abstract

Electroencephalogram (EEG) is a convenient neuroimaging technique due to its non-invasive setup, practical usage, and high temporal resolution. EEG allows to detect brain electrical activity to diagnose neurological disorders. Epilepsy is a crucial neurologic disorder that is reasoned from occurrence of sudden and repeated seizures. The goal of this paper is to classify the focal (epileptogenic area) and non-focal (non-epileptogenic area) EEG records with cepstral coefficients and machine learning algorithms. Analysis is carried out using publicly available Bern-Barcelona EEG dataset. Mel Frequency Cepstral Coefficients (MFCC) are calculated from EEG epochs. Feature sets are normalized with z-score and dimension reduction is realized using Principal Component Analysis. Fine Tree, Quadratic Discriminant Analysis, Logistic Regression, Gaussian Naïve Bayes, Cubic Support Vector Machine, weighted k-nearest neighbors, and Bagged Trees are applied for classification stage. A value of k=10 is used for cross validation. All focal and non-focal EEG pairs are perfectly classified with acc., sen., spe., and F1-score of 100% and AUC with 1 via. Quadratic Discriminant Analysis, Logistic Regression, Cubic SVM and Weighted k-NN. Proposed work recommends MFCCs as a single marker and this provides less computation workload, practicality, and direct processing of focal / non-focal EEG time series. Proposed methodology in this paper serves one of the highest achievements to literature and can assist neurologist and physicians to validate their diagnosis.

Keywords

• cepstrum analysis
• classification
• EEG
• focal and non-focal

References

1. [1] N. J. Sairamya, M. S. P. Subathra, E. S. Suviseshamuthu, and S. Thomas George, “A new approach for automatic detection of focal EEG signals using wavelet packet decomposition and quad binary pattern method,” Biomed. Signal Process. Control, vol. 63, p. 102096, 2021.
2. [2] L. Fraiwan and M. Alkhodari, “Classification of Focal and Non-Focal Epileptic Patients Using Single Channel EEG and Long Short-Term Memory Learning System,” IEEE Access, vol. 8, pp. 77255–77262, 2020.
3. [3] A. B. Das and M. I. H. Bhuiyan, “Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain,” Biomed. Signal Process. Control, vol. 29, pp. 11–21, 2016.
4. [4] R. San-Segundo, M. Gil-Martín, L. F. D’Haro-Enríquez, and J. M. Pardo, “Classification of epileptic EEG recordings using signal transforms and convolutional neural networks,” Comput. Biol. Med., vol. 109, no. March, pp. 148–158, 2019.
5. [5] M. Şeker, Y. Özbek, G. Yener, and M. S. Özerdem, “Complexity of EEG Dynamics for Early Diagnosis of Alzheimer’s Disease Using Permutation Entropy Neuromarker,” Comput. Methods Programs Biomed., vol. 206, p. 106116, 2021.
6. [6] Y. You, W. Chen, M. Li, T. Zhang, Y. Jiang, and X. Zheng, “Automatic focal and non-focal EEG detection using entropy-based features from flexible analytic wavelet transform,” Biomed. Signal Process. Control, vol. 57, p. 101761, 2020.
7. [7] N. Arunkumar, K. Ram Kumar, and V. Venkataraman, “Entropy features for focal EEG and non focal EEG,” J. Comput. Sci., vol. 27, pp. 440–444, 2018.
8. [8] S. Madhavan, R. K. Tripathy, and R. B. Pachori, “Time-Frequency Domain Deep Convolutional Neural Network for the Classification of Focal and Non-Focal EEG Signals,” IEEE Sens. J., vol. 20, no. 6, pp. 3078–3086, 2020.

9. [9] M. C. Tjepkema-Cloostermans, R. C. V. de Carvalho, and M. J. A. M. van Putten, “Deep learning for detection of focal epileptiform discharges from scalp EEG recordings,” Clin. Neurophysiol., vol. 129, no. 10, pp. 2191–2196, 2018.
10. [10] M. M. Rahman, M. I. Hassan Bhuiyan, and A. B. Das, “Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking,” Biomed. Signal Process. Control, vol. 50, pp. 72–82, 2019.
11. [11] S. Raghu and N. Sriraam, “Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms,” Expert Syst. Appl., vol. 113, pp. 18–32, 2018.
12. [12] N. Sriraam and S. Raghu, “Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier,” J. Med. Syst., vol. 41, no. 10, 2017.
13. [13] E. Yavuz, M. C. Kasapbaşı, C. Eyüpoğlu, and R. Yazıcı, “An epileptic seizure detection system based on cepstral analysis and generalized regression neural network,” Biocybern. Biomed. Eng., vol. 38, no. 2, pp. 201–216, 2018.
14. [14] R. G. Andrzejak, K. Schindler, and C. Rummel, “Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients.,” Phys. Rev. E. Stat. Nonlin. Soft Matter Phys., vol. 86, no. 4 Pt 2, p. 46206, Oct. 2012.
15. [15] A. V. Oppenheim and R. W. Schafer, “From frequency to quefrency: A history of the cepstrum,” IEEE Signal Process. Mag., vol. 21, no. 5, pp. 95–100, 2004.
16. [16] F. Artoni, A. Delorme, and S. Makeig, “Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition,” Neuroimage, vol. 175, no. September 2017, pp. 176–187, 2018.
17. [17] A. Navada, A. N. Ansari, S. Patil, and B. A. Sonkamble, “Overview of use of decision tree algorithms in machine learning,” in 2011 IEEE Control and System Graduate Research Colloquium, 2011, pp. 37–42.
18. [18] B. V Canizo, L. B. Escudero, R. G. Pellerano, and R. G. Wuilloud, “10 - Quality Monitoring and Authenticity Assessment of Wines: Analytical and Chemometric Methods,” in Quality Control in the Beverage Industry, A. M. Grumezescu and A. M. Holban, Eds. Academic Press, 2019, pp. 335–384.
19. [19] Siuly, H. Wang, and Y. Zhang, “Detection of motor imagery EEG signals employing Naïve Bayes based learning process,” Measurement, vol. 86, pp. 148–158, 2016.
20. [20] L. C. Djoufack Nkengfack, D. Tchiotsop, R. Atangana, V. Louis-Door, and D. Wolf, “EEG signals analysis for epileptic seizures detection using polynomial transforms, linear discriminant analysis and support vector machines,” Biomed. Signal Process. Control, vol. 62, p. 102141, 2020.
21. [21] Y. Tang, L. Jing, H. Li, and P. M. Atkinson, “A multiple-point spatially weighted k-NN method for object-based classification,” Int. J. Appl. Earth Obs. Geoinf., vol. 52, pp. 263–274, 2016.
22. [22] L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
23. [23] R. Sharma, P. Sircar, and R. B. Pachori, “Automated focal EEG signal detection based on third order cumulant function,” Biomed. Signal Process. Control, vol. 58, p. 101856, 2020.
24. [24] V. Gupta and R. B. Pachori, “Classification of focal EEG signals using FBSE based flexible time-frequency coverage wavelet transform,” Biomed. Signal Process. Control, vol. 62, no. August, p. 102124, 2020.
25. [25] T. Siddharth, R. K. Tripathy, and R. B. Pachori, “Discrimination of Focal and Non-Focal Seizures from EEG Signals Using Sliding Mode Singular Spectrum Analysis,” IEEE Sens. J., vol. 19, no. 24, pp. 12286–12296, 2019.
26. [26] S. Chatterjee, S. Pratiher, and R. Bose, “Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non-focal electroencephalogram signals,” IET Sci. Meas. Technol., vol. 11, no. 8, pp. 1014–1021, 2017.
27. [27] V. Bajaj, K. Rai, A. Kumar, D. Sharma, and G. K. Singh, “Rhythm-based features for classification of focal and non-focal EEG signals,” IET Signal Process., vol. 11, no. 6, pp. 743–748, 2017.
28. [28] A. Bhattacharyya, M. Sharma, R. B. Pachori, P. Sircar, and U. R. Acharya, “A novel approach for automated detection of focal EEG signals using empirical wavelet transform,” Neural Comput. Appl., vol. 29, no. 8, pp. 47–57, 2018.
29. [29] W. Zeng, M. Li, C. Yuan, Q. Wang, F. Liu, and Y. Wang, “Classification of focal and non focal EEG signals using empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks,” Artif. Intell. Rev., vol. 52, no. 1, pp. 625–647, 2019.