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Automatic Detection of Epileptic Waves in Electroencephalograms Using Bag of Visual Words and Machine Learning

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12241)


Epilepsy is one of the most recurrent brain disorders worldwide and mainly affects children. As a diagnostic support, the electroencephalogram is used, which is relatively easy to apply but requires a long time to analyze. Automatic EEG analysis presents difficulties both in the construction of the database and in the extracted characteristics used to build models. This article a machine learning-based methodology that uses a visual word bag of raw EEG images as input to identify images with abnormal signals. The performance introduces of the algorithms was tested using a proprietary pediatric EEG database. Accuracy greater than 95% was achieved, with calculation times less than 0.01 s per image. Therefore, the paper demonstrates the feasibility of using machine learning algorithms to directly analyze EEG images.


  • Childhood epilepsy
  • Feature extraction and selection
  • Supervised classification
  • Visual categorization
  • Semantic categorization

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  • DOI: 10.1007/978-3-030-59277-6_15
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This work was funded by the Colombian Agency for Science, Technology, and Innovation - COLCIENCIAS - in call 715-2015, “Call for Research and Development Projects in Engineering” Project “NeuroMoTIC: Mobile System for Diagnostic Support of Epilepsy,” contract number FP44842-154-2016.

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Correspondence to Diego M. López .

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Muñoz, M.S., Torres, C.E.S., López, D.M., Salazar-Cabrera, R., Vargas-Cañas, R. (2020). Automatic Detection of Epileptic Waves in Electroencephalograms Using Bag of Visual Words and Machine Learning. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham.

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