1. bookVolume 12 (2022): Issue 1 (January 2022)
Journal Details
First Published
30 Dec 2014
Publication timeframe
4 times per year
access type Open Access

Performance Analysis of Data Fusion Methods Applied to Epileptic Seizure Recognition

Published Online: 08 Oct 2021
Page range: 5 - 17
Received: 30 Jun 2020
Accepted: 24 Jun 2021
Journal Details
First Published
30 Dec 2014
Publication timeframe
4 times per year

Epilepsy is a chronic neurological disorder that is caused by unprovoked recurrent seizures. The most commonly used tool for the diagnosis of epilepsy is the electroencephalogram (EEG) whereby the electrical activity of the brain is measured. In order to prevent potential risks, the patients have to be monitored as to detect an epileptic episode early on and to provide prevention measures. Many different research studies have used a combination of time and frequency features for the automatic recognition of epileptic seizures. In this paper, two fusion methods are compared. The first is based on an ensemble method and the second uses the Choquet fuzzy integral method. In particular, three different machine learning approaches namely RNN, ML and DNN are used as inputs for the ensemble method and the Choquet fuzzy integral fusion method. Evaluation measures such as confusion matrix, AUC and accuracy are compared as well as MSE and RMSE are provided. The results show that the Choquet fuzzy integral fusion method outperforms the ensemble method as well as other state-of-the-art classification methods.


[1] S.L. Moshé, E. Perucca, P. Ryvlin, T. Tomson. Epilepsy: New advances. Lancet. 385:884-898, 2015. doi: 10.1016/S0140-6736(14)60456-6. Search in Google Scholar

[2] F. Leijten. Multimodal seizure detection: A review. Epilepsia. 59:42-47, 2018. doi: 10.1111/epi.14047. Search in Google Scholar

[3] D. Sukumaran, Y. Enyi, S. Sun, A. Basu, D. Zhao, J. Dauwels. A low-power, reconfigurable smart sensor system for EEG acquisition and classification; Proceedings of the 2012 IEEE Asia Pacific Conference on Circuits and Systems; Kaohsiung, Taiwan, pp. 9-12, 2-5 December 2012. Search in Google Scholar

[4] T.M.E. Nijsen, R.M. Aarts, P.J.M. Cluitmans, P.A.M. Griep. Time-Frequency Analysis of Accelerometry Data for Detection of Myoclonic Seizures. IEEE Trans. Inf. Technol. Biomed. 14:1197-1203, 2010. doi: 10.1109/TITB.2010.2058123. Search in Google Scholar

[5] H. Chen, M. Xue, Z. Mei, S.B. Oetomo, W. Chen. A Review of Wearable Sensor Systems for Monitoring Body Movements of Neonates. Sensors. 16:2134, 2016. doi: 10.3390/s16122134. Search in Google Scholar

[6] J.L. Andreassi. Psychophysiology: Human behavior and physiological response (Fifth ed.). Mahwah, NJ: Lawrence Erlbaum Associates, 2006. Search in Google Scholar

[7] J. Coosemans, B. Hermans, R. Puers. Integrating wireless ECG monitoring in textiles; Proceedings of the International Conference on Solid-State Sensors, Actuators and Microsystems; pp. 228-232. Digest of Technical Papers, Transducers, Seoul, Korea. 5-9 June 2005. Search in Google Scholar

[8] B. Yang, C. Yu, Y. Dong. Capacitively Coupled Electrocardiogram Measuring System and Noise Reduction by Singular Spectrum Analysis. IEEE Sens. J. 16:3802-3810, 2016. doi: 10.1109/JSEN.2016.2532599. Search in Google Scholar

[9] H.C. Jung, J.H. Moon, D.H. Baek, J.H. Lee, Y.Y. Choi, J.S. Hong, S.H. Lee. CNT/PDMS composite flexible dry electrodes for long-term ECG monitoring. IEEE Trans. Biomed. Eng. 59:1472-1479, 2012. doi: 10.1109/TBME.2012.2190288. Search in Google Scholar

[10] M.A. Yokus, J.S. Jur. Fabric-Based Wearable Dry Electrodes for Body Surface Biopotential Recording. IEEE Trans. Bio-Med. Eng. 63:423-430, 2016. doi: 10.1109/TBME.2015.2462312. Search in Google Scholar

[11] R. Lei, Q. Jiang, K. Chen, Z. Chen, C. Pan, L. Jiang. Fabrication of a Micro-Needle Array Electrode by Thermal Drawing for Bio-Signals Monitoring. Sensors. 16:908, 2016. Search in Google Scholar

[12] S.S. Spencer. MRI, SPECT, and PET imaging in epilepsy: Their relative contributions. Epilepsia. 35:S72-S89, 1994. doi: 10.1111/j.1528-1157.1994.tb05990.x. Search in Google Scholar

[13] C.Á. Szabó, L.C. Morgan, K.M. Karkar, L.D. Leary, O.V. Lie, M. Girouard, J.E. Cavazos. Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings. Epilepsia. 56:1432-1437, 2015. doi: 10.1111/epi.13083. Search in Google Scholar

[14] Y. Gu, E. Cleeren, J. Dan, K. Claes, W.V. Paesschen, S.V. Huffel, B. Hunyadi. Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy. Sensors. 18:29, 2018. doi: 10.3390/s18010029. Search in Google Scholar

[15] C. Shi, X. Kong, P. Yu, B. Wang, Multi-label Ensemble Learning, in Machine Learning and Knowledge Discovery in Databases, Springer Berlin Heidelberg, 2011. Search in Google Scholar

[16] Y. Wang, Z. Li, L. Feng, H. Bai, C. Wang. Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection. IET Circuits Devices Syst. 12:108-115, 2018. doi: 10.1049/iet-cds.2017.0216. Search in Google Scholar

[17] Q. He, B. Wu, H. Wang, L. Zhu. VEP Feature Extraction and Classification for Brain-Computer Interface; Proceedings of the 8th International Conference on Signal Processing; Guilin, China. 16-20 November 2006. Search in Google Scholar

[18] B.E. Boser, I.M. Guyon, V.N. Vapnik. A Training Algorithm for Optimal Margin Classifiers; Proceedings of the Annual Workshop on Computational Learning Theory; Pittsburgh, PA, USA, pp. 144-152. 27-29 July 1992. Search in Google Scholar

[19] K. Fu, J. Qu, Y. Chai, T. Zou. Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals. Biomed. Signal Process. Control. 18:179-185, 2015. doi: 10.1016/j.bspc.2015.01.002. Search in Google Scholar

[20] K.D. Brabanter, P. Karsmakers, F. Ojeda, C. Alzate, J.D. Brabanter, K. Pelckmans, B.D. Moor, J. Vandewalle, J.A.K. Suykens. LS-SVMlab Tool-box User’s Guide: Version 1.7. Ku Leuven Leuven; Leuven, Belgium: 2010. Search in Google Scholar

[21] Y.C. Liu, C.C.K. Lin, T. Jing-Jane, Y.N. Sun. Model-Based Spike Detection of Epileptic EEG Data. Sensors. 13:12536-12547, 2013. doi: 10.3390/s130912536. Search in Google Scholar

[22] D.H. Wolpert, W.G. Macready, No Free Lunch Theorems for Optimization, IEEE Transactions on Evolutionary Computation 1, 67, 1997. Search in Google Scholar

[23] R.M. Isa, I. Pasya, M.N. Taib, A.H. Jahidin, W.R.W. Omar, N. Fuad, H. Norhazman. EEG brainwave behaviour due to RF Exposure using kNN classification; Proceedings of the IEEE International Conference on System Engineering and Technology; Shah Alam, Malaysia. 19-20 August, pp. 385-388, 2013. Search in Google Scholar

[24] R. Chai, Y. Tran, G.R. Naik, T.N. Nguyen, S.H. Ling, A. Craig, H.T. Nguyen. Classification of EEG based-mental fatigue using principal component analysis and Bayesian neural network; Proceedings of the Engineering in Medicine and Biology Society; p. 4654, Orlando, FL, USA, 16-20 August 2016. Search in Google Scholar

[25] I. Kiral-Kornek, S. Roy, E. Nurse, B. Mashford, P. Karoly, T. Carroll, D. Payne, S. Saha, S. Baldassano, T. O’Brien, D. Grayden. Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine, 27, pp.103-111. Vancouver, 2018. Search in Google Scholar

[26] A. Antoniades, L. Spyrou, C.C. Took, S. Sanei. Deep learning for epileptic intracranial EEG data. In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE, 2016. Search in Google Scholar

[27] P. Thodoroff, J. Pineau, A. Lim. Learning robust features using deep learning for automatic seizure detection. In Machine learning for healthcare conference (pp. 178-190), 2016. Search in Google Scholar

[28] H. Liu, L. Xi, Y. Zhao, Z. Li. Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data, arXiv, eprint:1910.02544, 2019. Search in Google Scholar

[29] S. A. Ludwig, Epileptic Seizure Recognition: Deep Neural Network Ensemble versus Choquet Fuzzy Integral Fusion, 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020. Search in Google Scholar

[30] A. Graves, M. Liwicki, S. Fernndez, R. Bertolami, H. Bunke, J. Schmidhuber, A novel connectionist system for unconstrained handwriting recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855-868, 2009. Search in Google Scholar

[31] A. Graves, A. R. Mohamed, G. Hinton, Speech recognition with deep recurrent neural networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6645-6649, 2013. Search in Google Scholar

[32] K. Kourou, et al. Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal 13, 8-17, 2015. Search in Google Scholar

[33] S. Aiello et al. Machine Learning with Python and H20. H2O. ai Inc., 2016. Search in Google Scholar

[34] B.V. Dasarathy and B.V. Sheela, Composite classifier system design: concepts and methodology, Proceedings of the IEEE, vol. 67, no. 5, pp. 708-713, 1979. Search in Google Scholar

[35] L.K. Hansen and P. Salamon, Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993-1001, 1990. Search in Google Scholar

[36] R.E. Schapire, The Strength of Weak Learnability, Machine Learning, vol. 5, no. 2, pp. 197-227, 1990. Search in Google Scholar

[37] G. Seni, J.F. Elder. Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions. In Grossman, R., ed.: Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool, 2010. Search in Google Scholar

[38] T. Chen, C. Guestrin. XGBoost: A Scalable Tree Boosting System. In Krishnapuram, Balaji; Shah, Mohak; Smola, Alexander J.; Aggarwal, Charu C.; Shen, Dou; Rastogi, Rajeev (eds.). Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016. ACM. pp. 785-794, 2016. Search in Google Scholar

[39] J. Xie, V. Rojkova, S. Pal, S. Coggeshall. A Combination of Boosting and Bagging for KDD Cup 2009 - Fast Scoring on a Large Database. The Journal of Machine Learning Research (JMLR) 7, 35-43, 2009. Search in Google Scholar

[40] M. Sugeno, Theory of fuzzy integrals and its applications, Ph.D. thesis, Tokyo Institute of Technology, 1974. Search in Google Scholar

[41] G. Choquet, Theory of capacities, in Annales de l’institut Fourier, vol. 5. Institut Fourier, 1954, pp. 131-295. Search in Google Scholar

[42] M. Grabisch, H.T. Nguyen, and E.A. Walker, Fundamentals of uncertainty calculi with applications to fuzzy inference. Springer Science & Business Media, 2013, vol. 30. Search in Google Scholar

[43] D.T. Anderson, T.C. Havens, C. Wagner, J.M. Keller, M.F. Anderson, and D.J. Wescott, Extension of the fuzzy integral for general fuzzy set-valued information, IEEE Trans. Fuzzy Syst., vol. 22, no. 6, pp. 1625-1639, 2014. Search in Google Scholar

[44] D.T. Anderson et al., Binary fuzzy measures and Choquet integration for multi-source fusion, International Conference on Military Technologies (ICMT), Brno, 2017, pp. 676-681, 2017. Search in Google Scholar

[45] A. Galusha, J. Dale, J.M. Keller and A. Zare, Deep convolutional neural network target classification for underwater synthetic aperture sonar imagery, in Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 1101205, May 2019. Search in Google Scholar

[46] J.M. Keller, D. Liu, D.B. Fogel, [Fundamentals of Computational Intelligence], John Wiley & Sons, Inc., Hoboken, New Jersey, 2016. Search in Google Scholar

[47] R.G. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, C.E. 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, 64, 061907, 2001. Search in Google Scholar

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