Abstract
In this article, we investigate the performance of a seizure detection module for online monitoring of epileptic patients. The module is using as input data streams from electroencephalographic and electrocardiographic recordings. The architecture of the module consists of time and frequency domain feature extraction followed by classification. Four classification algorithms were evaluated on three epileptic subjects. The best performance was achieved by the support vector machine algorithm, with more than 90% for two of the subjects and slightly lower than 90% for the third subject.
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Mporas, I., Tsirka, V., Zacharaki, E.I., Koutroumanidis, M., Megalooikonomou, V. (2014). Online Seizure Detection from EEG and ECG Signals for Monitoring of Epileptic Patients. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_37
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DOI: https://doi.org/10.1007/978-3-319-07064-3_37
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