Seizure detection using common spatial patterns and classification techniques

Giorgos Giannakakis; Nikolaos Tsekos; Katerina Giannakaki; Kostas Michalopoulos; Pelagia Vorgia; Michalis Zervakis

doi:10.1109/BIBE.2019.00166

Seizure detection using common spatial patterns and classification techniques

Giorgos Giannakakis, Nikolaos Tsekos, Katerina Giannakaki, Kostas Michalopoulos, Pelagia Vorgia, Michalis Zervakis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

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%.

Original language	English (US)
Title of host publication	Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	890-893
Number of pages	4
ISBN (Electronic)	9781728146171
DOIs	https://doi.org/10.1109/BIBE.2019.00166
State	Published - Oct 2019
Event	19th International Conference on Bioinformatics and Bioengineering, BIBE 2019 - Athens, Greece Duration: Oct 28 2019 → Oct 30 2019

Publication series

Name	Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019

Other

Other	19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Country/Territory	Greece
City	Athens
Period	10/28/19 → 10/30/19

Keywords

Common spatial patterns
CSP
EEG
Focal epilepsy
Seizure detection

ASJC Scopus subject areas

Information Systems
Biomedical Engineering
Health Informatics

Access to Document

10.1109/BIBE.2019.00166

Cite this

Giannakakis, G., Tsekos, N., Giannakaki, K., Michalopoulos, K., Vorgia, P., & Zervakis, M. (2019). Seizure detection using common spatial patterns and classification techniques. In Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019 (pp. 890-893). Article 8941916 (Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBE.2019.00166

Seizure detection using common spatial patterns and classification techniques. / Giannakakis, Giorgos; Tsekos, Nikolaos; Giannakaki, Katerina et al.
Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 890-893 8941916 (Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Giannakakis, G, Tsekos, N, Giannakaki, K, Michalopoulos, K, Vorgia, P & Zervakis, M 2019, Seizure detection using common spatial patterns and classification techniques. in Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019., 8941916, Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019, Institute of Electrical and Electronics Engineers Inc., pp. 890-893, 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019, Athens, Greece, 10/28/19. https://doi.org/10.1109/BIBE.2019.00166

Giannakakis G, Tsekos N, Giannakaki K, Michalopoulos K, Vorgia P, Zervakis M. Seizure detection using common spatial patterns and classification techniques. In Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 890-893. 8941916. (Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019). doi: 10.1109/BIBE.2019.00166

Giannakakis, Giorgos ; Tsekos, Nikolaos ; Giannakaki, Katerina et al. / Seizure detection using common spatial patterns and classification techniques. Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 890-893 (Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019).

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title = "Seizure detection using common spatial patterns and classification techniques",

abstract = "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%.",

keywords = "Common spatial patterns, CSP, EEG, Focal epilepsy, Seizure detection",

author = "Giorgos Giannakakis and Nikolaos Tsekos and Katerina Giannakaki and Kostas Michalopoulos and Pelagia Vorgia and Michalis Zervakis",

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AU - Zervakis, Michalis

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AB - 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%.

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Seizure detection using common spatial patterns and classification techniques

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