Epileptic seizure detection using wristworn biosensors

D. Cogan; M. Nourani; J. Harvey; V. Nagaraddi

doi:10.1109/EMBC.2015.7319535

Epileptic seizure detection using wristworn biosensors

D. Cogan, M. Nourani, J. Harvey, V. Nagaraddi

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

20 Scopus citations

Abstract

Single signal seizure detection algorithms suffer from high false positive rates. We have found a set of signals which can be easily monitored by a wristworn device and which produce a distinctive pattern during seizure for patients in an epilepsy monitoring unit (EMU). This pattern is much less likely to be reproduced by nonseizure events in the patient's daily life than are changes in heart rate alone. We collected 108 hours of data from three EMU patients who suffered a combined total of seven seizures, then developed a time series analysis/pattern recognition based algorithm which distinguishes the seizures from nonseizure events with 100% accuracy.

Original language	English (US)
Title of host publication	2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5086-5089
Number of pages	4
Volume	2015-November
ISBN (Electronic)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7319535
State	Published - Nov 4 2015
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: Aug 25 2015 → Aug 29 2015

Other

Other	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/Territory	Italy
City	Milan
Period	8/25/15 → 8/29/15

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

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10.1109/EMBC.2015.7319535

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Cogan, D., Nourani, M., Harvey, J., & Nagaraddi, V. (2015). Epileptic seizure detection using wristworn biosensors. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (Vol. 2015-November, pp. 5086-5089). [7319535] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7319535

Epileptic seizure detection using wristworn biosensors. / Cogan, D.; Nourani, M.; Harvey, J. et al.

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. p. 5086-5089 7319535.

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

Cogan, D, Nourani, M, Harvey, J & Nagaraddi, V 2015, Epileptic seizure detection using wristworn biosensors. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. vol. 2015-November, 7319535, Institute of Electrical and Electronics Engineers Inc., pp. 5086-5089, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 8/25/15. https://doi.org/10.1109/EMBC.2015.7319535

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abstract = "Single signal seizure detection algorithms suffer from high false positive rates. We have found a set of signals which can be easily monitored by a wristworn device and which produce a distinctive pattern during seizure for patients in an epilepsy monitoring unit (EMU). This pattern is much less likely to be reproduced by nonseizure events in the patient's daily life than are changes in heart rate alone. We collected 108 hours of data from three EMU patients who suffered a combined total of seven seizures, then developed a time series analysis/pattern recognition based algorithm which distinguishes the seizures from nonseizure events with 100% accuracy.",

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Epileptic seizure detection using wristworn biosensors

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