Self-aware wearable systems in epileptic seizure detection
Forooghifar, Farnaz ; Aminifar, Amir LU
and Atienza Alonso, David
(2018)
21st Euromicro Conference on Digital System Design, DSD 2018
p.426-432
- Abstract
Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. A recently proposed concept for overcoming this challenge is self-awareness, which increases system's knowledge of itself and the surrounding environment. This is precisely what health monitoring wearable systems require to adapt to different situations. To demonstrate the impact of introducing self-awareness in wearable technologies, we consider the epileptic seizure detection problem, as a case study. Epilepsy affects around 1% of the world's population, which can dramatically... (More)
Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. A recently proposed concept for overcoming this challenge is self-awareness, which increases system's knowledge of itself and the surrounding environment. This is precisely what health monitoring wearable systems require to adapt to different situations. To demonstrate the impact of introducing self-awareness in wearable technologies, we consider the epileptic seizure detection problem, as a case study. Epilepsy affects around 1% of the world's population, which can dramatically degrade the quality of life and represents a major public health issue. As a result, detection of epileptic seizures has become more important over the past decades. In this paper, we aim to introduce a new generation of self-aware wearable systems to decrease energy consumption and improve their seizures detection capabilities by introducing the notion of self-awareness in such systems. These techniques include switching to low-power mode to reduce the energy consumption and machine-learning model enhancement to improve detection quality. We incorporated our proposed techniques in the machine learning module, which detects epileptic seizures by monitoring the cardiac and respiratory systems. We evaluated the performance of our approach based on an epilepsy database of more than 141 hours, provided by the Lausanne University Hospital (CHUV). Our self-aware wearable system achieves 36% reduction in computational complexity and 10.51% improvement in detection performance.
(Less)
- author
- Forooghifar, Farnaz
; Aminifar, Amir
LU
and Atienza Alonso, David
- publishing date
- 2018-10-12
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Classification, Energy management, Epileptic seizure detection, Machine learning, Self-awareness
- host publication
- Proceedings - 21st Euromicro Conference on Digital System Design, DSD 2018
- editor
- Konofaos, Nikos ; Novotny, Martin and Skavhaug, Amund
- article number
- 8491849
- pages
- 7 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 21st Euromicro Conference on Digital System Design, DSD 2018
- conference location
- Prague, Czech Republic
- conference dates
- 2018-08-29 - 2018-08-31
- external identifiers
-
- scopus:85056449468
- ISBN
- 9781538673768
- DOI
- 10.1109/DSD.2018.00078
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2018 IEEE.
- id
- 9cff842b-c849-4466-9db6-66829342bfca
- date added to LUP
- 2022-02-05 01:21:30
- date last changed
- 2022-04-22 07:31:27
@inproceedings{9cff842b-c849-4466-9db6-66829342bfca,
abstract = {{<p>Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. A recently proposed concept for overcoming this challenge is self-awareness, which increases system's knowledge of itself and the surrounding environment. This is precisely what health monitoring wearable systems require to adapt to different situations. To demonstrate the impact of introducing self-awareness in wearable technologies, we consider the epileptic seizure detection problem, as a case study. Epilepsy affects around 1% of the world's population, which can dramatically degrade the quality of life and represents a major public health issue. As a result, detection of epileptic seizures has become more important over the past decades. In this paper, we aim to introduce a new generation of self-aware wearable systems to decrease energy consumption and improve their seizures detection capabilities by introducing the notion of self-awareness in such systems. These techniques include switching to low-power mode to reduce the energy consumption and machine-learning model enhancement to improve detection quality. We incorporated our proposed techniques in the machine learning module, which detects epileptic seizures by monitoring the cardiac and respiratory systems. We evaluated the performance of our approach based on an epilepsy database of more than 141 hours, provided by the Lausanne University Hospital (CHUV). Our self-aware wearable system achieves 36% reduction in computational complexity and 10.51% improvement in detection performance.</p>}},
author = {{Forooghifar, Farnaz and Aminifar, Amir and Atienza Alonso, David}},
booktitle = {{Proceedings - 21st Euromicro Conference on Digital System Design, DSD 2018}},
editor = {{Konofaos, Nikos and Novotny, Martin and Skavhaug, Amund}},
isbn = {{9781538673768}},
keywords = {{Classification; Energy management; Epileptic seizure detection; Machine learning; Self-awareness}},
language = {{eng}},
month = {{10}},
pages = {{426--432}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Self-aware wearable systems in epileptic seizure detection}},
url = {{http://dx.doi.org/10.1109/DSD.2018.00078}},
doi = {{10.1109/DSD.2018.00078}},
year = {{2018}},
}