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Abstract
Background: Epileptic Seizure (ES) is a neural disorder that generates an uncontrolled brain signal impulse. The disorder is seen in young children and adults with a positive medical history.
Method: In this paper, a novel approach to epileptic seizure detection and prediction is proposed and evaluated. The seizure is retrained from Electroencephalography (EEG) high-dimension datasets. The EEG datasets further segment features of interdependent EEG into a matrix. This matrix is linked to providing a validation occurrence of similar feature events with a minimum redundancy maximum relevance (MRMR) approach for ES feature optimization.
Result and Discussion: The uncertainty-based genetic algorithm for parametric evaluation and validation (GAPEr) is used for predictive analysis and decision support via a dedicated neural networking model. The sizer detection and prediction are supported and validated via a series of interactions from trained datasets.
Conclusion: The proposed setup has achieved higher accuracy and dependency in decision support of Epileptic Seizure identification and classification based on predictive evaluation.
[1]
"Epilepsy", Available From: https://www.who.int/news-room/fact-sheets/detail/epilepsy (accessed on 12th Oct 2021).
[2]
G. Zaccara, G. Citerio, A. Del Gaudio, M. Ferlisi, F.R. Pugliese, and D. Toni, "Clinical pathways of epileptic seizures and status epilepticus: Results from a survey in Italy", Neurol. Sci., vol. 41, no. 6, pp. 1571-1575, 2020.
[http://dx.doi.org/10.1007/s10072-020-04270-3] [PMID: 31989348]
[http://dx.doi.org/10.1007/s10072-020-04270-3] [PMID: 31989348]
[3]
D. Ahmedt-Aristizabal, C. Fookes, S. Dionisio, K. Nguyen, J.P.S. Cunha, and S. Sridharan, "Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey", Epilepsia, vol. 58, no. 11, pp. 1817-1831, 2017.
[http://dx.doi.org/10.1111/epi.13907] [PMID: 28990168]
[http://dx.doi.org/10.1111/epi.13907] [PMID: 28990168]
[4]
L.S. Vidyaratne, and K.M. Iftekharuddin, "Real-time epileptic seizure detection using EEG", IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 11, pp. 2146-2156, 2017.
[http://dx.doi.org/10.1109/TNSRE.2017.2697920] [PMID: 28459693]
[http://dx.doi.org/10.1109/TNSRE.2017.2697920] [PMID: 28459693]
[5]
F. Achilles, F. Tombari, V. Belagiannis, A.M. Loesch, S. Noachtar, and N. Navab, "Convolutional neural networks for real-time epileptic seizure detection", Comput. Methods Biomech. Biomed. Eng. Imaging Vis., vol. 6, no. 3, pp. 264-269, 2018.
[http://dx.doi.org/10.1080/21681163.2016.1141062]
[http://dx.doi.org/10.1080/21681163.2016.1141062]
[6]
R. Sarić, D. Jokić, N. Beganović, L.G. Pokvić, and A. Badnjević, "FPGA-based real-time epileptic seizure classification using Artificial Neural Network", Biomed. Signal Process. Control, vol. 62, p. 102106, 2020.
[http://dx.doi.org/10.1016/j.bspc.2020.102106]
[http://dx.doi.org/10.1016/j.bspc.2020.102106]
[7]
M. Alhussein, G. Muhammad, M.S. Hossain, and S.U. Amin, "Cognitive IoT-cloud integration for smart healthcare: Case study for epileptic seizure detection and monitoring", Mob. Netw. Appl., vol. 23, no. 6, pp. 1624-1635, 2018.
[http://dx.doi.org/10.1007/s11036-018-1113-0]
[http://dx.doi.org/10.1007/s11036-018-1113-0]
[8]
S.T. Ahmed, M. Sandhya, and S. Sankar, "A dynamic MooM dataset processing under TelMED protocol design for QoS improvisation of telemedicine environment", J. Med. Syst., vol. 43, no. 8, p. 257, 2019.
[http://dx.doi.org/10.1007/s10916-019-1392-4] [PMID: 31264006]
[http://dx.doi.org/10.1007/s10916-019-1392-4] [PMID: 31264006]
[9]
M.A. Sayeed, S.P. Mohanty, E. Kougianos, and H. Zaveri, "A fast and accurate approach for real-time seizure detection in the IoMT", In 2018 IEEE International Smart Cities Conference (ISC2) Kansas City, MO, USA, 2018, pp. 1-5
[10]
S.T. Ahmed, M. Sandhya, and S. Sankar, "“An optimized RTSRV machine learning algorithm for biomedical signal transmission and regeneration for telemedicine environment.”", Procedia Comput. Sci., vol. 152, pp. 140-149, 2019.
[http://dx.doi.org/10.1016/j.procs.2019.05.036]
[http://dx.doi.org/10.1016/j.procs.2019.05.036]
[11]
R. Hussein, H. Palangi, R. Ward, and Z. Jane Wang, "Epileptic seizure detection: A deep learning approach", arXiv: 1803.09848.
[12]
C.E. Elger, and C. Hoppe, "Diagnostic challenges in epilepsy: Seizure under-reporting and seizure detection", Lancet Neurol., vol. 17, no. 3, pp. 279-288, 2018.
[http://dx.doi.org/10.1016/S1474-4422(18)30038-3] [PMID: 29452687]
[http://dx.doi.org/10.1016/S1474-4422(18)30038-3] [PMID: 29452687]
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