Abstract
Traditional seizure detection algorithms act on single channels ignoring the synchronously recorded, inherently interdependent multichannel nature of EEG. However, the spatial distribution and evolution of the ictal pattern is a crucial characteristic of the seizure. Two different approaches aiming at including such structural information into the data representation are presented in this paper. Their performance is compared to the traditional approach both in a simulation study and a real-life example, showing that spatial and structural information facilitates precise classification.
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Hunyadi, B., De Vos, M., Signoretto, M., Suykens, J.A.K., Van Paesschen, W., Van Huffel, S. (2011). Automatic Seizure Detection Incorporating Structural Information. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_29
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DOI: https://doi.org/10.1007/978-3-642-21735-7_29
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