Abstract
Outstanding success of CNN image classification affected using it as an instrument for time series classification. Powerful graph clustering methods have capabilities to come across entity relationships. In this study we propose time series pattern discovery approach as a hybrid of independent CNN image classification and graph mining. Our experiments are based on Electroencephalography (EEG) channel signals data from research of Alcoholic and Control person behaviors. For image classification we used techniques of transforming vectors to images on Gramian Angular Fields (GAF) and for graph mining we built time series graphs on pairs of vectors with high cosine similarities. We unlocked EEG time series patterns that not just validate differences in stimuli reactions of persons from Alcoholic or Control groups but also indicate similarities or dissimilarities between EEG channel signals located in different scalp landscape positions.
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Romanova, A. (2021). Time Series Pattern Discovery by Deep Learning and Graph Mining. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2021 Workshops. DEXA 2021. Communications in Computer and Information Science, vol 1479. Springer, Cham. https://doi.org/10.1007/978-3-030-87101-7_19
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