Abstract
This paper concentrates on Electroencephalography (EEG) signal processing with the emphasis on seizure detection. Manually by reviewing EEG recordings for detection of electrographical patterns is a time consuming business. Therefore, the ability to automate the classification of interesting electrographical patterns is a good supplement to the wide range of detection algorithms currently used for EEG analysis. Multi channel recordings of the electrographically patterns from neural currents in the brain would generate a large amounts of data. Suitable feature extraction methods are useful to facilitate the representation and interpretation of the data
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Tamil, E.M., Radzi, H.M., Idris, M.Y.I., Tamil, A.M. (2008). A Review on Feature Extraction & Classification Techniques for Biosignal Processing (Part II: Electroencephalography). In: Abu Osman, N.A., Ibrahim, F., Wan Abas, W.A.B., Abdul Rahman, H.S., Ting, HN. (eds) 4th Kuala Lumpur International Conference on Biomedical Engineering 2008. IFMBE Proceedings, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69139-6_32
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DOI: https://doi.org/10.1007/978-3-540-69139-6_32
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