Abstract
According to the World Health Organization, 50 million people have epilepsy with 80% of them living in low- and middle-income countries. Three quarters of these do not receive the treatment they need due to delays in interpreting electroencephalograms (EEGs). This paper presents a Machine learning model to support the diagnosis of pediatric epilepsy in semi-automatic way. The model was built from more than 100 pediatric EEGs, with a diagnosis of epileptic seizure. The results achieved using the software were compared with annotations made by a pediatric neurologist, reaching up to 85% agreement. In addition, the neurologists stated that, during the evaluation of a 30-min EEG, the system allowed them to save up to half of the time that usually takes. The tool herein presented facilitates the study and evaluation of pediatric EEGs using a semi-automatic classification of EEG signals and it can be used in the diagnosis of pediatric epilepsy.
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Acknowledgements
This 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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Vargas-Canas, R., Mino-Arango, M.E., Lopez-Gutierrez, D.M. (2020). NeuroMoTIC: An Smart Tool to Support Pediatric Epilepsy Diagnosis. In: González Díaz, C., et al. VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering. CLAIB 2019. IFMBE Proceedings, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-030-30648-9_21
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DOI: https://doi.org/10.1007/978-3-030-30648-9_21
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