Robust and low complexity algorithms for seizure detection

Mojtaba Bandarabadi; Cesar A. Teixeira; Theoden I. Netoff; Keshab K. Parhi; Antonio Dourado

doi:10.1109/EMBC.2014.6944611

Robust and low complexity algorithms for seizure detection

Mojtaba Bandarabadi, Cesar A. Teixeira, Theoden I. Netoff, Keshab K. Parhi, Antonio Dourado

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

10 Scopus citations

Abstract

This paper presents two low complexity and yet robust methods for automated seizure detection using a set of 2 intracranial Electroencephalogram (iEEG) recordings. Most current seizure detection methods suffer from high number of false alarms, even when designed to be subject-specific. In this study, the ratios of power between pairs of frequency bands are used as features to detect epileptic seizures. For comparison, these features are calculated from monopolar and bipolar iEEG recordings. Optimal thresholds are individually determined and used for each feature. Alarms are generated when the measure passes the threshold. The detector was applied to long-term continuous invasive recordings from 5 patients with refractory partial epilepsy, containing 54 seizures in 780 hours. On average, the results revealed 88.9% sensitivity, a very low false detection rate of 0.041 per hour (h<sup>-1</sup>) and detection latency of 9.4 seconds.

Original language	English (US)
Title of host publication	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4447-4450
Number of pages	4
Volume	2014
ISBN (Electronic)	9781424479290
DOIs	https://doi.org/10.1109/EMBC.2014.6944611
State	Published - Nov 2 2014
Event	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: Aug 26 2014 → Aug 30 2014

Publication series

Name	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

Other

Other	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Country/Territory	United States
City	Chicago
Period	8/26/14 → 8/30/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

bipolar analysis
power spectral density
Seizure detection

PubMed: MeSH publication types

Journal Article
Research Support, Non-U.S. Gov't

Publisher link

10.1109/EMBC.2014.6944611

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Bandarabadi, M., Teixeira, C. A., Netoff, T. I., Parhi, K. K., & Dourado, A. (2014). Robust and low complexity algorithms for seizure detection. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (Vol. 2014, pp. 4447-4450). Article 6944611 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2014.6944611

Robust and low complexity algorithms for seizure detection. / Bandarabadi, Mojtaba; Teixeira, Cesar A.; Netoff, Theoden I. et al.
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Vol. 2014 Institute of Electrical and Electronics Engineers Inc., 2014. p. 4447-4450 6944611 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014).

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

Bandarabadi, M, Teixeira, CA, Netoff, TI , Parhi, KK & Dourado, A 2014, Robust and low complexity algorithms for seizure detection. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. vol. 2014, 6944611, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 4447-4450, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 8/26/14. https://doi.org/10.1109/EMBC.2014.6944611

Bandarabadi M, Teixeira CA, Netoff TI , Parhi KK, Dourado A. Robust and low complexity algorithms for seizure detection. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Vol. 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 4447-4450. 6944611. (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014). doi: 10.1109/EMBC.2014.6944611

Bandarabadi, Mojtaba ; Teixeira, Cesar A. ; Netoff, Theoden I. et al. / Robust and low complexity algorithms for seizure detection. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Vol. 2014 Institute of Electrical and Electronics Engineers Inc., 2014. pp. 4447-4450 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014).

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AB - This paper presents two low complexity and yet robust methods for automated seizure detection using a set of 2 intracranial Electroencephalogram (iEEG) recordings. Most current seizure detection methods suffer from high number of false alarms, even when designed to be subject-specific. In this study, the ratios of power between pairs of frequency bands are used as features to detect epileptic seizures. For comparison, these features are calculated from monopolar and bipolar iEEG recordings. Optimal thresholds are individually determined and used for each feature. Alarms are generated when the measure passes the threshold. The detector was applied to long-term continuous invasive recordings from 5 patients with refractory partial epilepsy, containing 54 seizures in 780 hours. On average, the results revealed 88.9% sensitivity, a very low false detection rate of 0.041 per hour (h-1) and detection latency of 9.4 seconds.

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Robust and low complexity algorithms for seizure detection

Abstract

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Keywords

PubMed: MeSH publication types

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