Abstract
The Electroencephalogram (EEG) is one of the most used tools for diagnosing Epilepsy. Analyzing EEG, neurologists can identify alterations in brain activity associated with Epilepsy. However, this task is not always easy to perform, because of the duration of the EEGs or the subjectivity of the specialist in detecting alterations. Aim: To present an epileptic spike detector based on matched filter for supporting diagnosis of Epilepsy through a tool able to automatically detect spikes in EEG of children. Results: The results of the evaluation showed that the developed detector achieved a sensitivity of 89.28% which is within the range of what has been reported in the literature (82.68% and 94.4%), and a specificity of 99.96%, the later improving the specificity of the best reviewed work. Conclusions: Considering the results obtained in the evaluation, the solution becomes an alternative to support the automatic identification of epileptic spikes by neurologists.
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References
Garcés, A., Orosco, L., Diez, P., Laciar, E.: Automatic detection of epileptic seizures in long-term EEG records. Comput. Biol. Med. 57, 66–73 (2015)
Liliana, J., Lara, A., Alexandra, M., Gómez, M., Gómez, F. R.: Informe Final Proyecto Estdio de disponibilidad y distribución de la oferta de médicos especialistas, en servicios de alta y mediana complejidad en Colombia. https://www.minsalud.gov.co/salud/Documents/Observatorio%20Talento%20Humano%20en%20Salud/DisponibilidadDistribuciónMdEspecialistasCendex.pdf. Accessed 06 Oct 2018
Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H.: Automated sleep stage identification system based on time – frequency analysis of a single EEG channel and random forest classifier. Comput. Methods Programs Biomed. 108(1), 10–19 (2011)
Tsalikakis, D.G., Tsipouras, M.G.: Epileptic seizures classification based on long-term EEG signal wavelet analysis. In International Conference on Biomedical and Health Informatics on proceedings Precision Medicine Powered by pHealth and Connected Health, Thessaloniki, Greece, pp. 165–169 (2017)
Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100(1), 270–278 (2018)
Molina, E., Salazar-Cabrera, R., López, Diego M.: NeuroEHR: open source telehealth system for the management of clinical data, EEG and remote diagnosis of epilepsy. In: Figueroa-García, J.C., López-Santana, E.R., Rodriguez-Molano, J.I. (eds.) WEA 2018. CCIS, vol. 915, pp. 418–430. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00350-0_35
Quigg, M.: EEG Pearls: Acquisition of the Electroencephalogram 2, 1st edn. Elsevier, Charlottesville (2006)
Bancroft, J. C.: Introduction to Matched Filters. https://crewes.org/ForOurSponsors/ResearchReports/2002/2002-46.pdf. Accessed 06 Oct 2018
Hermand, J., Roderick, W.I.: Acoustic model-based matched filter processing for fading time-dispersive ocean channels: theory and experiment. IEEE J. Ocean. Eng. 18(4), 447–465 (1993)
Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet components of a new research resource for complex physiologic signals. Compon. New Res. Resour. Complex Physiol. Signals 101(23), 215–220 (2000)
Gaspard, N., Alkawadri, R., Farooque, P., Goncharova, I.I., Zaveri, H.P.: Clinical Neurophysiology Automatic detection of prominent interictal spikes in intracranial EEG: validation of an algorithm and relationsip to the seizure onset zone. Clin. Neurophysiol. 125(6), 1095–1103 (2014)
Carey, H.J., Manic, M., Arsenovic, P.: Epileptic Spike Detection with EEG using Artificial Neural Networks. In: 2016 9th International Conference on Human System Interactions (HSI), pp. 89–95 (2016)
Kumar, H., Amit, G., Kohli, K.: EEG spike detection technique using output correlation method: a Kalman filtering approach. circuits. Syst. Signal Process 34(8), 2643–2665 (2015)
Garg, H.K., Kohli, A.K.: Nonstationary-epileptic-spike detection algorithm in EEG signal using SNEO. Biomed. Eng. Lett. 3(2), 80–86 (2013)
Liu, Y., Chou-Ching, L., Tsai, J.-J., Sun, Y.-N.: Model-based spike detection of epileptic EEG data. Sensors 13, 12536–12547 (2013)
Acknowledgements
The work is funded by a grant from the Colombian Agency for Science, Technology, and Innovation Colciencias – under Calls 715-2015, project: “NeuroMoTIC: Sistema móvil para el Apoyo Diagnóstico de la Epilepsia”, Contract number FP44842-154-2016, and Call 647- 2015.
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Mera, M., López, D.M., Vargas, R., Miño, M. (2018). Automatic Detection of Epileptic Spike in EEGs of Children Using Matched Filter. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_37
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DOI: https://doi.org/10.1007/978-3-030-05587-5_37
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