Automated Algorithm for Generalized Tonic–Clonic Epileptic Seizure Onset Detection Based on sEMG Zero-Crossing Rate

Isa Conradsen; Sándor Beniczky; Karsten Hoppe; Peter Wolf; Helge Bjarup Dissing Sørensen

doi:10.1109/TBME.2011.2178094

Automated Algorithm for Generalized Tonic–Clonic Epileptic Seizure Onset Detection Based on sEMG Zero-Crossing Rate

Isa Conradsen, Sándor Beniczky, Karsten Hoppe, Peter Wolf, Helge Bjarup Dissing Sørensen

Department of Electrical Engineering

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

Patients are not able to call for help during a generalized tonic–clonic epileptic seizure. Our objective was to develop a robust generic algorithm for automatic detection of tonic–clonic seizures, based on surface electromyography (sEMG) signals suitable for a portable device. Twenty-two seizures were analyzed from 11 consecutive patients. Our method is based on a high-pass filtering with a cutoff at 150 Hz, and monitoring a count of zero crossings with a hysteresis of $\pm 50\,\mu \hbox{V}$ . Based on data from one sEMG electrode (on the deltoid muscle), we achieved a sensitivity of 100% with a mean detection latency of 13.7 s, while the rate of false detection was limited to 1 false alarm per 24 h. The overall performance of the presented generic algorithm is adequate for clinical implementation.

Original language	English
Journal	I E E E Transactions on Biomedical Engineering
Volume	59
Issue number	2
Pages (from-to)	579-585
ISSN	0018-9294
DOIs	https://doi.org/10.1109/TBME.2011.2178094
Publication status	Published - 2012

Keywords

Tonic–clonic
Epilepsy
Surface electromyography (sEMG)
Seizure detection

Access to Document

10.1109/TBME.2011.2178094

Conradsen 2011 Automated algorithm for generalised tonic-clonic epileptic seizure onset detection based on sEMG zero-crossing rate

Cite this

Conradsen, I., Beniczky, S., Hoppe, K., Wolf, P., & Sørensen, H. B. D. (2012). Automated Algorithm for Generalized Tonic–Clonic Epileptic Seizure Onset Detection Based on sEMG Zero-Crossing Rate. I E E E Transactions on Biomedical Engineering, 59(2), 579-585. https://doi.org/10.1109/TBME.2011.2178094

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abstract = "Patients are not able to call for help during a generalized tonic–clonic epileptic seizure. Our objective was to develop a robust generic algorithm for automatic detection of tonic–clonic seizures, based on surface electromyography (sEMG) signals suitable for a portable device. Twenty-two seizures were analyzed from 11 consecutive patients. Our method is based on a high-pass filtering with a cutoff at 150 Hz, and monitoring a count of zero crossings with a hysteresis of $\pm 50\,\mu \hbox{V}$ . Based on data from one sEMG electrode (on the deltoid muscle), we achieved a sensitivity of 100% with a mean detection latency of 13.7 s, while the rate of false detection was limited to 1 false alarm per 24 h. The overall performance of the presented generic algorithm is adequate for clinical implementation.",

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Automated Algorithm for Generalized Tonic–Clonic Epileptic Seizure Onset Detection Based on sEMG Zero-Crossing Rate. / Conradsen, Isa; Beniczky, Sándor; Hoppe, Karsten; Wolf, Peter; Sørensen, Helge Bjarup Dissing.

In: I E E E Transactions on Biomedical Engineering, Vol. 59, No. 2, 2012, p. 579-585.

Research output: Contribution to journal › Journal article › Research › peer-review

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AU - Beniczky, Sándor

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AU - Wolf, Peter

AU - Sørensen, Helge Bjarup Dissing

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AB - Patients are not able to call for help during a generalized tonic–clonic epileptic seizure. Our objective was to develop a robust generic algorithm for automatic detection of tonic–clonic seizures, based on surface electromyography (sEMG) signals suitable for a portable device. Twenty-two seizures were analyzed from 11 consecutive patients. Our method is based on a high-pass filtering with a cutoff at 150 Hz, and monitoring a count of zero crossings with a hysteresis of $\pm 50\,\mu \hbox{V}$ . Based on data from one sEMG electrode (on the deltoid muscle), we achieved a sensitivity of 100% with a mean detection latency of 13.7 s, while the rate of false detection was limited to 1 false alarm per 24 h. The overall performance of the presented generic algorithm is adequate for clinical implementation.

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KW - Epilepsy

KW - Surface electromyography (sEMG)

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