Performance analysis of data fusion methods applied to epileptic seizure recognition

• Autorzy: Ludwig Simone A.

Identyfikatory

DOI

10.2478/jaiscr-2022-0001

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

epilepsy; ensemble method; Choquet fuzzy integral fusion

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2022
• Tom: Vol. 12, No. 1
• Strony: 5--17
• Opis fizyczny: Bibliogr. 47 poz., rys.

Twórcy

autor

Ludwig Simone A.

• Department of Computer Science, North Dakota State University, Fargo, ND, USA

Bibliografia

• [1] S.L. Moshe, E. Perucca, P. Ryvlin, T. Tomson. Epilepsy: New advances. Lancet. 385:884-898, 2015. doi: 10.1016/S0140-6736(14)60456-6.
• [2] F. Leijten. Multimodal seizure detection: A review. Epilepsia. 59:42-47, 2018. doi: 10.1111/epi.14047.
• [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.
• [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.
• [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.
• [6] J.L. Andreassi. Psychophysiology: Human behavior and physiological response (Fifth ed.). Mahwah, NJ: Lawrence Erlbaum Associates, 2006.
• [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.
• [8] B. Yang, C. Yu, Y. Dong. Capacitively Coupled Electrocardiogram Measuring System andNoise Reduction by Singular Spectrum Analysis. IEEE Sens. J. 16:3802-3810, 2016. doi:10.1109/JSEN.2016.2532599.
• [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.
• [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.
• [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.
• [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.
• [13] C.A. Szab ´ o, 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 tonicclonic seizure detection algorithm to video-EEG recordings. Epilepsia. 56:1432-1437, 2015. doi:10.1111/epi.13083.
• [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.
• [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.
• [16] Y. Wang, Z. Li, L. Feng, H. Bai, C. Wang. ardware 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.
• [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.
• [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.
• [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.
• [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 Toolbox User’s Guide: Version 1.7. Ku Leuven Leuven; Leuven, Belgium: 2010.
• [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.
• [22] D.H. Wolpert, W.G. Macready, No Free Lunch Theorems for Optimization, IEEE Transactions on Evolutionary Computation 1, 67, 1997.
• [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.
• [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.
• [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.
• [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.
• [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.
• [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.
• [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.
• [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.
• [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.
• [32] K. Kourou, et al. Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal 13, 8-17, 2015.
• [33] S. Aiello et al. Machine Learning with Python and H20. H2O. ai Inc., 2016.
• [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.
• [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.
• [36] R.E. Schapire, The Strength of Weak Learnability, Machine Learning, vol. 5, no. 2, pp. 197-227,1990.
• [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.
• [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.
• [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.
• [40] M. Sugeno, Theory of fuzzy integrals and its applications, Ph.D. thesis, Tokyo Institute of Technology, 1974.
• [41] G. Choquet, Theory of capacities, in Annales de l’institut Fourier, vol. 5. Institut Fourier, 1954, pp. 131-295.
• [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.
• [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 setvalued information, IEEE Trans. Fuzzy Syst., vol. 22, no. 6, pp. 1625-1639, 2014.
• [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.
• [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.
• [46] J.M. Keller, D. Liu, D.B. Fogel, [Fundamentals of Computational Intelligence], John Wiley & Sons, Inc., Hoboken, New Jersey, 2016.
• [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.