e-Glass: A Wearable System for Real-Time Detection of Epileptic Seizures
Sopic, Dionisije ; Aminifar, Amir LU
and Atienza, David
(2018)
2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
In Proceedings - IEEE International Symposium on Circuits and Systems
2018-May.
- Abstract
Today, epilepsy is one of the most common chronic diseases affecting more than 65 million people worldwide and is ranked number four after migraine, Alzheimer's disease, and stroke. Despite the recent advances in anti-epileptic drugs, one-third of the epileptic patients continue to have seizures. More importantly, epilepsy-related causes of death account for 40% of mortality in high-risk patients. However, no reliable wearable device currently exists for real-time epileptic seizure detection. In this paper, we propose e-Glass, a wearable system based on four electroencephalogram (EEG) electrodes for the detection of epileptic seizures. Based on an early warning from e-Glass, it is possible to notify caregivers for rescue to avoid... (More)
Today, epilepsy is one of the most common chronic diseases affecting more than 65 million people worldwide and is ranked number four after migraine, Alzheimer's disease, and stroke. Despite the recent advances in anti-epileptic drugs, one-third of the epileptic patients continue to have seizures. More importantly, epilepsy-related causes of death account for 40% of mortality in high-risk patients. However, no reliable wearable device currently exists for real-time epileptic seizure detection. In this paper, we propose e-Glass, a wearable system based on four electroencephalogram (EEG) electrodes for the detection of epileptic seizures. Based on an early warning from e-Glass, it is possible to notify caregivers for rescue to avoid epilepsy-related death due to the underlying neurological disorders, sudden unexpected death in epilepsy, or accidents during seizures. We demonstrate the performance of our system using the Physionet.org CHB-MIT Scalp EEG database for epileptic children. Our experimental evaluation demonstrates that our system reaches a sensitivity of 93.80% and a specificity of 93.37%, allowing for 2.71 days of operation on a single battery charge.
(Less)
- author
- Sopic, Dionisije
; Aminifar, Amir
LU
and Atienza, David
- publishing date
- 2018-04-26
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
- series title
- Proceedings - IEEE International Symposium on Circuits and Systems
- volume
- 2018-May
- article number
- 8351728
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
- conference location
- Florence, Italy
- conference dates
- 2018-05-27 - 2018-05-30
- external identifiers
-
- scopus:85056466779
- ISSN
- 0271-4310
- ISBN
- 9781538648827
- 9781538648810
- DOI
- 10.1109/ISCAS.2018.8351728
- language
- English
- LU publication?
- no
- additional info
- Funding Information: This work has been partially supported by the MyPreHealth research project (Hasler Foundation project no. 16073). Publisher Copyright: © 2018 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
- id
- 134af4fc-ffcb-4228-b301-c34ec9928694
- date added to LUP
- 2021-08-31 15:32:40
- date last changed
- 2024-06-16 18:04:31
@inproceedings{134af4fc-ffcb-4228-b301-c34ec9928694,
abstract = {{<p>Today, epilepsy is one of the most common chronic diseases affecting more than 65 million people worldwide and is ranked number four after migraine, Alzheimer's disease, and stroke. Despite the recent advances in anti-epileptic drugs, one-third of the epileptic patients continue to have seizures. More importantly, epilepsy-related causes of death account for 40% of mortality in high-risk patients. However, no reliable wearable device currently exists for real-time epileptic seizure detection. In this paper, we propose e-Glass, a wearable system based on four electroencephalogram (EEG) electrodes for the detection of epileptic seizures. Based on an early warning from e-Glass, it is possible to notify caregivers for rescue to avoid epilepsy-related death due to the underlying neurological disorders, sudden unexpected death in epilepsy, or accidents during seizures. We demonstrate the performance of our system using the Physionet.org CHB-MIT Scalp EEG database for epileptic children. Our experimental evaluation demonstrates that our system reaches a sensitivity of 93.80% and a specificity of 93.37%, allowing for 2.71 days of operation on a single battery charge.</p>}},
author = {{Sopic, Dionisije and Aminifar, Amir and Atienza, David}},
booktitle = {{2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings}},
isbn = {{9781538648827}},
issn = {{0271-4310}},
language = {{eng}},
month = {{04}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings - IEEE International Symposium on Circuits and Systems}},
title = {{e-Glass: A Wearable System for Real-Time Detection of Epileptic Seizures}},
url = {{http://dx.doi.org/10.1109/ISCAS.2018.8351728}},
doi = {{10.1109/ISCAS.2018.8351728}},
volume = {{2018-May}},
year = {{2018}},
}