Implementation of Bagged SVM Ensemble Model for Classification of Epileptic States Using EEG

Author(s): Arshpreet Kaur*, Karan Verma, Amol P. Bhondekar, Kumar Shashvat.

Journal Name: Current Pharmaceutical Biotechnology

Volume 20 , Issue 9 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble model distinguishes between two different sets of problems which are group 1: inter-ictal and ictal, group 2: controlled and inter-ictal using approximate entropy as a parameter.

Methods: This work addresses the classification problem for two groups; Group 1: “inter-ictal vs. ictal” for which case 1(C-E), and case 2(D-E) are included and Group 2; “activity from controlled vs. inter-ictal activity” considering four cases which are case 3 (A-C), case 4(B-C), case 5 (A-D) and case 6(B-D) respectively. To divide the EEG into sub-bands, DWT (Discrete Wavelet Transform) was used and approximate Entropy was extracted out of all the five sub-bands of EEG for each case. Bagged SVM was used to classify the different groups considered.

Results: The highest accuracy for Group 1 using Bagged SVM Ensemble model for case 1 was observed to be 96.83% with testing data; which was similar to 97% achieved by using training data. For case 2 (D-E) 93.92% accuracy with training and 84.83% with testing data were obtained. For Group 2, there was a large disparity between SVM and Bagged Ensemble model, where 76%, 81.66%, 72.835% and 71.16% for case 3, case 4, case 5 and case 6 were obtained. While for training data set, 92.87%, 91.74%, 92% and 92.64% accuracy was attained, respectively. The results obtained by SVM for Group 2 showed a huge difference from the highest accuracy achieved by bagged SVM for both the training and the test data.

Conclusion: Bagged Ensemble model outperformed SVM model for every case with a huge difference with both training as well as test dataset for Group 2 and marginally better for Group 1.

Keywords: EEG classification, approximate entropy, discrete wavelet transform, bagged SVM, ensemble model, epileptic states.

[1]
Fisher, R.S.; van Emde Boas, W.; Blume, W.; Elger, C.; Genton, P.; Lee, P.; Engel, J., Jr Epileptic seizures and epilepsy: Definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia, 2005, 46(4), 470-472.
[http://dx.doi.org/10.1111/j.0013-9580.2005.66104.x] [PMID: 15816939]
[2]
Amudhan, S.; Gururaj, G.; Satishchandra, P. Epilepsy in India I: Epidemiology and public health. Ann. Indian Acad. Neurol., 2015, 18(3), 263-277.
[http://dx.doi.org/10.4103/0972-2327.160093] [PMID: 26425001]
[3]
Santhosh, N.S.; Sinha, S.; Satishchandra, P. Epilepsy: Indian perspective. Ann. Indian Acad. Neurol., 2014, 17(Suppl. 1), S3-S11.
[http://dx.doi.org/10.4103/0972-2327.128643] [PMID: 24791085]
[4]
Tomson, T.; Beghi, E.; Sundqvist, A.; Johannessen, S.I. Medical risks in epilepsy: A review with focus on physical injuries, mortality, traffic accidents and their prevention. Epilepsy Res., 2004, 60(1), 1-16.
[http://dx.doi.org/10.1016/j.eplepsyres.2004.05.004] [PMID: 15279865]
[5]
Berger, H. U¨ ber das Elektroenkephalogram des Menschen. Arch. f. Psychiatry, 1929, 87, 527-570.
[6]
Andrzejak, R.G.; Lehnertz, K.; Mormann, F.; Rieke, C.; 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. E Stat. Nonlin. Soft Matter Phys., 2001, 64(6 Pt 1), 061907.
[7]
Ocak, H. Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Processing, 2008, 88, 1858-1867.
[http://dx.doi.org/10.1016/j.sigpro.2008.01.026]
[8]
Ocak, H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl., 2009, 36(2), 2027-2036.
[http://dx.doi.org/10.1016/j.eswa.2007.12.065]
[9]
Guo, L.; Rivero, D.; Pazos, A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods, 2010, 193(1), 156-163.
[http://dx.doi.org/10.1016/j.jneumeth.2010.08.030] [PMID: 20817036]
[10]
Kumar, Y.M.L.; Dewal, R.S. Epileptic seizures detection in EEG using Dwt-Based Apen and artificial neural network. Signal Image Video Process., 2012, 8(7), 1323-1334.
[http://dx.doi.org/10.1007/s11760-012-0362-9]
[11]
Kumar, Y.; Dewal, M.L.; Anand, R.S. Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing, 2014, 133, 271-279.
[http://dx.doi.org/10.1016/j.neucom.2013.11.009]
[12]
Pincus, S.M. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA, 1991, 88(6), 2297-2301.
[http://dx.doi.org/10.1073/pnas.88.6.2297] [PMID: 11607165]
[13]
Agrawal, R.K. An introductory study on time series modeling and forecasting arXiv Preprint arXiv: 1302.6613, 1302.6613 2013, 1-68.
[14]
Kim, H-C.; Pang, S.; Je, H-M.; Kim, D.; Bang, S-Y. Support vector machine ensemble with bagging; Patt. Recognit. Support Vector Machin, 2002, pp. 397-408.
[15]
Valentini, G.; Muselli, M.; Ruffino, F. Bagged ensembles of support vector machines for gene expression data analysis. IEEE International Joint Conference on Neural Networks, Portland, OR, USA2003.
[http://dx.doi.org/10.1109/IJCNN.2003.1223688]
[16]
Parikh, R.; Mathai, A.; Parikh, S.; Chandra Sekhar, G.; Thomas, R. Understanding and using sensitivity, specificity and predictive values. Indian J. Ophthalmol., 2008, 56(1), 45-50.
[http://dx.doi.org/10.4103/0301-4738.37595] [PMID: 18158403]
[17]
Acharya, U.R.; Sree, S.V.; Alvin, A.P.; Yanti, R.; Suri, J.S. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst., 2012, 22(2) 1250002 b
[http://dx.doi.org/10.1142/S0129065712500025] [PMID: 23627588]
[18]
Acharya, U.R.; Sree, S.V.; Suri, J.S.; Alvin, A.P. Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework. Expert Syst. Appl., 2012, 10(39), 9072-9078. c
[http://dx.doi.org/10.1016/j.eswa.2012.02.040]
[19]
Acharya, U.R.; Molinari, F.; Vinitha, S.S.; Chattopadhyay, S.; Kwan-Hoong, N.; Suri, J.S. Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control, 2012, 7(4), 401-408.
[http://dx.doi.org/10.1016/j.bspc.2011.07.007]
[20]
Zhou, W.; Liu, Y.; Yuan, Q.; Li, X. Epileptic seizure detection using lacunarity and Bayesian linear discriminant analysis in intracranial EEG. IEEE Trans. Biomed. Eng., 2013, 60(12), 3375-3381.
[http://dx.doi.org/10.1109/TBME.2013.2254486] [PMID: 23629837]
[21]
Xiang, J.; Li, C.; Li, H.; Cao, R.; Wang, B.; Han, X.; Chen, J. The detection of epileptic seizure signals based on fuzzy entropy. J. Neurosci. Methods, 2015, 243, 18-25.
[http://dx.doi.org/10.1016/j.jneumeth.2015.01.015] [PMID: 25614384]
[22]
Divya, S. Classification of EEG signal for epileptic seizure detection using EMD and ELM. Int. J. Trends Engin. Technol., 2015, 3(2), 68-74.
[23]
Wang, Y.; Li, Z.; Feng, L.; Bai, H.; Wang, C. Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection. IET Circuits Dev. Syst., 2018, 12, 108-115.
[http://dx.doi.org/10.1049/iet-cds.2017.0216]
[24]
Murugavel, A.S.; Ramakrishnan, S. Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification. Med. Biol. Eng. Comput., 2016, 54(1), 149-161.
[http://dx.doi.org/10.1007/s11517-015-1351-2] [PMID: 26296799]
[25]
Song, Y.; Liò, P. A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. JBiSE, 2010, 03(6), 556-567.
[http://dx.doi.org/10.4236/jbise.2010.36078]
[26]
Tzimourta, K.D.; Tzallas, A.T.; Giannakeas, N.; Astrakas, L.G.; Tsalikakis, D.G.; Angelidis, P.; Tsipouras, M.G. A robust methodology for classification of epileptic seizures in EEG signals. Health Technol. (Berl.), 2019, 9(2), 135-142.
[http://dx.doi.org/10.1007/s12553-018-0265-z]
[27]
Siuly, S.; Li, Y.; Wen, P.P. Clustering technique-based least square support vector machine for EEG signal classification. Comput. Methods Programs Biomed., 2011, 104(3), 358-372.
[http://dx.doi.org/10.1016/j.cmpb.2010.11.014] [PMID: 21168234]
[28]
Burioka, N.; Miyata, M.; Cornélissen, G.; Halberg, F.; Takeshima, T.; Kaplan, D.T.; Suyama, H.; Endo, M.; Maegaki, Y.; Nomura, T.; Tomita, Y.; Nakashima, K.; Shimizu, E. Approximate entropy in the electroencephalogram during wake and sleep. Clin. EEG Neurosci., 2005, 36(1), 21-24.
[http://dx.doi.org/10.1177/155005940503600106] [PMID: 15683194]
[29]
Kannathal, N.; Choo, M.L.; Acharya, U.R.; Sadasivan, P.K. Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed., 2005, 80(3), 187-194.
[http://dx.doi.org/10.1016/j.cmpb.2005.06.012] [PMID: 16219385]
[30]
Chandaka, S.; Chatterjee, A.; Munshi, S. Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Syst. Appl., 2009, 36(2 PART 1), 1329-1336.
[http://dx.doi.org/10.1016/j.eswa.2007.11.017]
[31]
Panda, R.; Khobragade, P.S.; Jambhule, P.D.; Jengthe, S.; Pal, P.R.; Gandhi, T.K. Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure detection. Proc. Syst. Med. Biol; ICSMB, 2009, pp. 405-408.
[32]
Siuly, S.; Li, Y.; Wen, P.P. Clustering technique-based least square support vector machine for EEG signal classification. Comput. Methods Programs Biomed., 2011, 104(3), 358-372.
[http://dx.doi.org/10.1016/j.cmpb.2010.11.014] [PMID: 21168234]
[33]
Nicolaou, N.; Georgiou, J. Expert Systems with Applications Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst. Appl., 2012, 39(1), 202-209.
[http://dx.doi.org/10.1016/j.eswa.2011.07.008]
[34]
Xiang, J.; Li, C.; Li, H.; Cao, R.; Wang, B.; Han, X.; Chen, J. The detection of epileptic seizure signals based on fuzzy entropy. J. Neurosci. Methods, 2015, 243, 18-25.
[http://dx.doi.org/10.1016/j.jneumeth.2015.01.015] [PMID: 25614384]
[35]
Sharma, M.; Ram, B.U.; Rajendra, A. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit. Lett., 2017, 94, 172-179.
[http://dx.doi.org/10.1016/j.patrec.2017.03.023]
[36]
Sharmila, A.; Aman Raj, S.; Shashank, P.; Mahalakshmi, P. Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: A case study. J. Med. Eng. Technol., 2018, 42(1), 1-8.
[http://dx.doi.org/10.1080/03091902.2017.1394389]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 20
ISSUE: 9
Year: 2019
Page: [755 - 765]
Pages: 11
DOI: 10.2174/1389201020666190618112715
Price: $65

Article Metrics

PDF: 25
HTML: 4
EPUB: 1
PRC: 1