Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands
DOI:
https://doi.org/10.18100/ijamec.2017SpecialIssue30468Keywords:
epilepsy detection, EEG, electroencephalogram frequency bands, TreeBoost, random forests, support vector machines, signal energy, computer-aided diagnosticsAbstract
This paper demonstrates the effectiveness of information fusion at the feature vectors level for automatic detection of epilepsy. Experiments used features ranging from separate EEG frequency band waves to combinations of band waves, in addition to signal energy. We used three classifiers with the feature vectors: TreeBoost, Random Forests, and support vector machines. We carried out experiments using a real life EEG signals data set that is available from the University of Bonn Hospital in Germany. This paper shows the effect of combining together signal energy with different EEG frequency band waves in order to classify epilepsy, and that this combination has computed 97.5% accuracy over using feature vectors with fewer band wave transformations (84-95.5% accuracy), using the TreeBoost algorithm and 10 folds cross validation. This combination computed 99% specificity and 95.5% sensitivity. Furthermore, the paper demonstrates and analyses the effectiveness of using ensemble based tree learning.Downloads
References
R. Begg, D. T. H. Lai, and M. Palaniswami, Computational intelligence in biomedical engineering. CRC Press, 2008.
K. Fu, J. Qu, Y. Chai, and Y. Dong, “Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM,” Biomed. Signal Process. Control, vol. 13, pp. 15–22, 2014.
A. E. Elmahdy, N. Yahya, N. S. Kamel, and A. Shahid, “Epileptic seizure detection using singular values and classical features of EEG signals,” in 2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS), 2015, pp. 162–167.
L. Murali, D. Chitra, T. Manigandan, and B. Sharanya, “An Efficient Adaptive Filter Architecture for Improving the Seizure Detection in EEG Signal,” Circuits, Syst. Signal Process., vol. 35, no. 8, pp. 2914–2931, 2016.
G. Xun, X. Jia, and A. Zhang, “Detecting epileptic seizures with electroencephalogram via a context-learning model,” BMC Med. Inform. Decis. Mak., vol. 16, no. S2, p. 70, 2016.
S. Sareen, S. K. Sood, and S. K. Gupta, “A Cloud-Based Seizure Alert System for Epileptic Patients That Uses Higher-Order Statistics,” Comput. Sci. Eng., vol. 18, no. 5, pp. 56–67, 2016.
R. Sharma and R. B. Pachori, “Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions,” Expert Syst. Appl., vol. 42, no. 3, pp. 1106–1117, 2015.
N. S. Tawfik, S. M. Youssef, and M. Kholief, “A hybrid automated detection of epileptic seizures in EEG records,” Comput. Electr. Eng., vol. 53, pp. 177–190, 2016.
K. Samiee, P. Kovács, and M. Gabbouj, “Epileptic seizure detection in long-term EEG records using sparse rational decomposition and local Gabor binary patterns feature extraction,” Knowledge-Based Syst., vol. 118, pp. 228–240, 2017.
E. Bou Assi, D. K. Nguyen, S. Rihana, and M. Sawan, “Towards accurate prediction of epileptic seizures: A review,” Biomed. Signal Process. Control, vol. 34, no. February, pp. 144–157, 2017.
A. Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Syst. Appl., vol. 32, no. 4, pp. 1084–1093, May 2007.
S. Mihandoost, M. Mazlaghani, M. Amirani, and a. Mihandoost, “Automatic feature extraction using generalised autoregressive conditional heteroscedasticity model: an application to electroencephalogram classification,” IET Signal Process., vol. 6, no. 9, pp. 829–838, Dec. 2012.
K. Polat and S. Güneş, “Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform,” Appl. Math. Comput., vol. 187, no. 2, pp. 1017–1026, Apr. 2007.
X. Bao, P. Bahl, A. Kansal, D. Chu, R. R. Choudhury, and A. Wolman, “Helping mobile apps bootstrap with fewer users,” in ACM Conference on Ubiquitous Computing, 2012, pp. 491–500.
D. Rumelhart, G. Hinton, and R. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, pp. 533–536, 1986.
A. L. Goldberger et al., “Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000.
L. Guo, D. Rivero, J. a. Seoane, and A. Pazos, “Classification of EEG signals using relative wavelet energy and artificial neural networks,” in The first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC ’09, 2009, pp. 177–183.
D. Wang, Duoqian Miao, and Chen Xie., “Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection,” Expert Syst. Appl., vol. 38, no. 11, pp. 14314–14320, 2011.
t[19] S. Sareen, S. K. Sood, and S. K. Gupta, “An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks,” J. Med. Syst., vol. 40, no. 11, p. 226, 2016.
J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Ann. Stat., pp. 1189–1232, 2001.
G. G. Moisen, E. a. Freeman, J. a. Blackard, T. S. Frescino, N. E. Zimmermann, and T. C. Edwards, “Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods,” Ecol. Modell., vol. 199, no. 2, pp. 176–187, 2006.
L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
P. H. Sherrod, “DTREG predictive modeling software.” [Online]. Available: http://www.dtreg.com.
V. VAPNIK, “Statistical learning theory,” Adapt. Learn. Syst. signal Process. Commun. Control., 1998.
R. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. Elger, “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, vol. 64, no. 6, p. 61907, Nov. 2001.
F. Sharbrough, G. E. Chatrian, R. P. Lesser, H. Lüders, M. Nuwer, and T. W. Picton, “American Electroencephalographic Society guidelines for standard electrode position nomenclature,” J. Clin. Neurophysiol, vol. 8, no. 2, 1991.
I. H. Witten, E. Frank, L. E. Trigg, M. A. Hall, G. Holmes, and S. J. Cunningham, “Weka: Practical machine learning tools and techniques with Java implementations.” 1999.
S. A. Bellegdi, S. Mohandes, and O. M. Soufan, and S. Arafat “Computational Intelligence for Cardiac Arrhythmia Classification,” in UKCI 2011, 2011, pp. 93–97.
O. M. Soufan and S. Arafat, “Arrhythmia Detection using Mutual Information-Based Integration Method,” in World Conf. Soft Computing, San Francisco, USA, 2011.
R. Ahmed and S. Arafat, “Cardiac arrhythmia classification using hierarchical classification model,” in 6th International Conference on Computer Science and Information Technology (CSIT), 2014, pp. 203–207.
Downloads
Published
Issue
Section
License
Copyright (c) 2017 International Journal of Applied Methods in Electronics and Computers
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
