Abstract
We introduce and assess a better machine learning way to deal with the problem of seizure detection by hosting a comparative study between different machine learning algorithms. This problem is a multiclass problem with overlaying attributes and thus making it demanding. The most crucial part of develo** a highly efficient classifier was to identify the attributes that are necessary to distinguish seizure from other brain activities. We trained our model on 23.6 s of recorded brain activity from 500 patients, which detected 80% of 500 test cases with a F1 score of 71%. Information about the dataset gathered from UCI machine learning repository database, which we analyzed in this study, is also provided.
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References
A. Shoeb, Application of machine learning to epileptic seizure onset detection and treatment. Ph.D. thesis, Massachusetts Institute of Technology, 2009
J. Gotman, J. Ives, P. Gloor, Automatic recognition of inter-ictal epileptic activity in prolonged EEG recordings. Electroencephalogr. Clin. Neurophysiol. 46(5), 510–520 (1979)
J. Gotman, Automatic recognition of epileptic seizures in the EEG. Electroencephalogr. Clin. Neurophysiol. 54(5), 530–540 (1982)
J. Gotman, Automatic detection of seizures and spikes. J. Clin. Neurophysiol. 16(2), 130–140 (1999)
D. Koffler, J. Gotman, Automatic detection of spike-and-wave bursts in ambulatory EEG recordings. Electroencephalogr. Clin. Neurophysiol. 61(2), 165–180 (1985)
H. Qu, J. Gotman, Improvement in seizure detection performance by automatic adaptation to the EEG of each patient. Electroencephalogr. Clin. Neurophysiol. 86(2), 79–87 (1993)
Scott B. Wilson, Mark L. Scheuer, Ronald G. Emerson, Andrew J. Gabor, Seizure detection: evaluation of the reveal algorithm. Clin. Neurophysiol. 115, 2280–2291 (2004)
V. Srinivasan, C. Eswaran, N. Sriraam, Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans. Inf. Technol. Biomed. 11(3), 288–295 (2007)
L. Guo, D. Rivero, J. Dorado, J.R. Rabunal, A. Pazos, Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Methods 191(1), 101–109 (2010)
P. Swami, T.K. Gandhi, B.K. Panigrahi, M. Tripathi, S. Anand, A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst. Appl. 56, 116–130 (2016)
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)
D. Rivero, J. Dorado, J. Rabuñal, A. Pazos, Automatic Recurrent ANN development for signal classification: detection of seizures in EEGs, in ECAI 2008, vol. 178 (2008), pp. 142–146
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Guha, A., Ghosh, S., Roy, A., Chatterjee, S. (2020). Epileptic Seizure Recognition Using Deep Neural Network. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_3
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DOI: https://doi.org/10.1007/978-981-13-7403-6_3
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