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

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.