Robust Epileptic Seizure Detection on Wearable Systems with Reduced False-Alarm Rate
Zanetti, Renato ; Aminifar, Amir LU
and Atienza, David
(2020)
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2020.
p.4248-4251
- Abstract
Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epilepsy. However, the majority of employed seizure detection techniques and devices suffer from unacceptable false-alarm rate. In this paper, we propose a robust seizure detection methodology for a wearable platform and validate it on the Physionet.org CHB-MIT Scalp EEG database. It reaches sensitivity of 0.966 and specificity of 0.925, and... (More)
Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epilepsy. However, the majority of employed seizure detection techniques and devices suffer from unacceptable false-alarm rate. In this paper, we propose a robust seizure detection methodology for a wearable platform and validate it on the Physionet.org CHB-MIT Scalp EEG database. It reaches sensitivity of 0.966 and specificity of 0.925, and reducing the false-alarm rate by 34.7%. We also evaluate the battery lifetime of the wearable system including our proposed methodology and demonstrate the feasibility of using it in real time for up to 40.87 hours on a single battery charge.
(Less)
- author
- Zanetti, Renato
; Aminifar, Amir
LU
and Atienza, David
- publishing date
- 2020-07
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Electroencephalography, Epilepsy/diagnosis, Humans, Quality of Life, Seizures/diagnosis, Wearable Electronic Devices
- host publication
- 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society : Enabling Innovative Technologies for Global Healthcare, EMBC 2020 - Enabling Innovative Technologies for Global Healthcare, EMBC 2020
- series title
- Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
- volume
- 2020
- article number
- 9175339
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
- conference location
- Montreal, Canada
- conference dates
- 2020-07-20 - 2020-07-24
- external identifiers
-
- pmid:33018934
- scopus:85091050064
- ISSN
- 1557-170X
- ISBN
- 9781728119908
- DOI
- 10.1109/EMBC44109.2020.9175339
- language
- English
- LU publication?
- no
- id
- 4fae6364-21ae-4bf0-88c2-ecfd7392f28e
- date added to LUP
- 2022-02-05 01:17:32
- date last changed
- 2024-04-11 06:03:26
@inproceedings{4fae6364-21ae-4bf0-88c2-ecfd7392f28e,
abstract = {{<p>Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epilepsy. However, the majority of employed seizure detection techniques and devices suffer from unacceptable false-alarm rate. In this paper, we propose a robust seizure detection methodology for a wearable platform and validate it on the Physionet.org CHB-MIT Scalp EEG database. It reaches sensitivity of 0.966 and specificity of 0.925, and reducing the false-alarm rate by 34.7%. We also evaluate the battery lifetime of the wearable system including our proposed methodology and demonstrate the feasibility of using it in real time for up to 40.87 hours on a single battery charge.</p>}},
author = {{Zanetti, Renato and Aminifar, Amir and Atienza, David}},
booktitle = {{42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society : Enabling Innovative Technologies for Global Healthcare, EMBC 2020}},
isbn = {{9781728119908}},
issn = {{1557-170X}},
keywords = {{Electroencephalography; Epilepsy/diagnosis; Humans; Quality of Life; Seizures/diagnosis; Wearable Electronic Devices}},
language = {{eng}},
pages = {{4248--4251}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS}},
title = {{Robust Epileptic Seizure Detection on Wearable Systems with Reduced False-Alarm Rate}},
url = {{http://dx.doi.org/10.1109/EMBC44109.2020.9175339}},
doi = {{10.1109/EMBC44109.2020.9175339}},
volume = {{2020}},
year = {{2020}},
}