Automatic Epileptic Seizure Onset Detection Using Matching Pursuit: A case Study

Thomas Lynggaard Sorensen; Ulrich L. Olsen; Isa Conradsen; Jonas Duun-Henriksen; Troels W. Kjaer; Carsten E. Thomsen; Helge Bjarup Dissing Sørensen

doi:10.1109/IEMBS.2010.5627265

Automatic Epileptic Seizure Onset Detection Using Matching Pursuit: A case Study

Thomas Lynggaard Sorensen
, Ulrich L. Olsen
, Isa Conradsen
, Jonas Duun-Henriksen
, Troels W. Kjaer
, Carsten E. Thomsen
, Helge Bjarup Dissing Sørensen

Technical University of Denmark
Copenhagen University Hospital Herlev and Gentofte
University of Copenhagen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature xtractor for detection of epileptic seizures.

Original language	English
Title of host publication	Engineering in Medicine and Biology Conference
Publisher	IEEE
Publication date	2010
Pages	3277-3280
ISBN (Print)	978-1-4244-4124-2
DOIs	https://doi.org/10.1109/IEMBS.2010.5627265
Publication status	Published - 2010
Event	32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Buenos Aires, Argentina Duration: 31 Aug 2010 → 4 Sept 2010 Conference number: 32 http://embc2010.embs.org/

Conference

Conference	32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Number	32
Country/Territory	Argentina
City	Buenos Aires
Period	31/08/2010 → 04/09/2010
Internet address	http://embc2010.embs.org/

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Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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@inproceedings{eee57409d1e94f72a7b16f41407cd555,

title = "Automatic Epileptic Seizure Onset Detection Using Matching Pursuit: A case Study",

abstract = "An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial electroencephalography (iEEG). A sensitivity of 78-100\% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature xtractor for detection of epileptic seizures.",

author = "Sorensen, \{Thomas Lynggaard\} and Olsen, \{Ulrich L.\} and Isa Conradsen and Jonas Duun-Henriksen and Kjaer, \{Troels W.\} and Thomsen, \{Carsten E.\} and S{\o}rensen, \{Helge Bjarup Dissing\}",

note = "Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 ; Conference date: 31-08-2010 Through 04-09-2010",

year = "2010",

doi = "10.1109/IEMBS.2010.5627265",

language = "English",

isbn = "978-1-4244-4124-2",

pages = "3277--3280",

booktitle = "Engineering in Medicine and Biology Conference",

publisher = "IEEE",

address = "United States",

url = "http://embc2010.embs.org/",

}

Sorensen, TL, Olsen, UL, Conradsen, I, Duun-Henriksen, J, Kjaer, TW, Thomsen, CE & Sørensen, HBD 2010, Automatic Epileptic Seizure Onset Detection Using Matching Pursuit: A case Study. in Engineering in Medicine and Biology Conference. IEEE, pp. 3277-3280, 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina, 31/08/2010. https://doi.org/10.1109/IEMBS.2010.5627265

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T1 - Automatic Epileptic Seizure Onset Detection Using Matching Pursuit

T2 - 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

AU - Sorensen, Thomas Lynggaard

AU - Olsen, Ulrich L.

AU - Conradsen, Isa

AU - Duun-Henriksen, Jonas

AU - Kjaer, Troels W.

AU - Thomsen, Carsten E.

AU - Sørensen, Helge Bjarup Dissing

N1 - Conference code: 32

PY - 2010

Y1 - 2010

N2 - An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature xtractor for detection of epileptic seizures.

AB - An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature xtractor for detection of epileptic seizures.

U2 - 10.1109/IEMBS.2010.5627265

DO - 10.1109/IEMBS.2010.5627265

M3 - Article in proceedings

C2 - 21096613

SN - 978-1-4244-4124-2

SP - 3277

EP - 3280

BT - Engineering in Medicine and Biology Conference

PB - IEEE

Y2 - 31 August 2010 through 4 September 2010

ER -

Automatic Epileptic Seizure Onset Detection Using Matching Pursuit: A case Study

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