Online Seizure Detection from EEG and ECG Signals for Monitoring of Epileptic Patients
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.
KeywordsSeizure electroencephalogram electrocardiogram classification
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- 4.Valderrama, M., Nikolopoulos, S., Adam, C., Navarro, V., Le Van Quyen, M.: Patient-specific seizure prediction using a multi-feature and multi-modal EEG-ECG classification. In: XII Med. Conf. on Medical and Biological Engineering and Computing, vol. 29, pp. 77–80 (2010)Google Scholar
- 5.Mohseni, H.R., Maghsoudi, A., Shamsollahi, M.B.: Seizure detection in EEG signals: A comparison of different approaches. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 6724–6727 (2006)Google Scholar
- 8.Varon, C., Jansen, K., Lagae, L., Van Huffel, S.: Detection of epileptic seizures by means of morphological changes in the ECG, ftp://ftp.esat.kuleuven.be/pub/SISTA/cvaron/13-163.pdf
- 11.Fazle Rabbi, A., Fazel-Rezai, R.: A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG. Computational Intelligence and Neuroscience 2012, Article ID 705140, 12 (2012)Google Scholar
- 12.ARMOR project, http://www.armor-project.eu/
- 14.Witten, H.I., Frank, E.: Data Mining: practical machine learning tools and techniques. Morgan Kaufmann PublishingGoogle Scholar