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Seizure detection using common spatial patterns and classification techniques

Giannakakis Georgios, Tsekos Nikolaos, Giannakaki Aikaterini-Antonia, Michalopoulos Kostas, Vorgia Pelagia, Zervakis Michail

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Year 2019
Type of Item Conference Full Paper
Bibliographic Citation G. Giannakakis, N. Tsekos, K. Giannakaki, K. Michalopoulos, P. Vorgia and M. Zervakis, "Seizure detection using common spatial patterns and classification techniques," in 19th International Conference on Bioinformatics and Bioengineering, 2019, pp. 890-893. doi: 10.1109/BIBE.2019.00166
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This paper investigates the effectiveness of Common Spatial Patterns (CSP) analysis of EEG signals on the automatic detection of focal epileptic seizures. Focal seizures are characterized by unilaterally triggered abnormal brain activity. CSP analysis has been frequently used in literature for multichannel EEG signal separation between two states. In the present study, EEG recordings from 10 subjects aged 7.7±4.4 years, including 63 seizures, were analyzed with respect to seizure detection and discrimination between interictal and ictal periods. Machine learning techniques of feature selection and classification were used in the analysis, resulting in a best achieved classification accuracy of 91.1%.