Noise-resilient and interpretable epileptic seizure detection
Thomas, Anthony Hitchcock ; Aminifar, Amir LU
and Atienza, David
(2020)
52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
In Proceedings - IEEE International Symposium on Circuits and Systems
2020-October.
- Abstract
Deep convolutional neural networks have recently emerged as a state-of-the art tool in detection of seizures. Such models offer the ability to extract complex nonlinear representations of an electroencephalogram (EEG) signal which can improve accuracy over methods relying on hand-crafted features. However, neural networks are susceptible to confounding artifacts commonly present in EEG signals and are notoriously difficult to interpret. In this work, we present a neural-network based algorithm for seizure detection which leverages recent advances in information theory to construct a signal representation containing the minimal amount of information necessary to discriminate between seizure and normal brain activity. We show our approach... (More)
Deep convolutional neural networks have recently emerged as a state-of-the art tool in detection of seizures. Such models offer the ability to extract complex nonlinear representations of an electroencephalogram (EEG) signal which can improve accuracy over methods relying on hand-crafted features. However, neural networks are susceptible to confounding artifacts commonly present in EEG signals and are notoriously difficult to interpret. In this work, we present a neural-network based algorithm for seizure detection which leverages recent advances in information theory to construct a signal representation containing the minimal amount of information necessary to discriminate between seizure and normal brain activity. We show our approach automatically learns representations that ignore common signal artifacts and which encode medically relevant information from the raw signal.
(Less)
- author
- Thomas, Anthony Hitchcock
; Aminifar, Amir
LU
and Atienza, David
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings
- series title
- Proceedings - IEEE International Symposium on Circuits and Systems
- volume
- 2020-October
- article number
- 9180429
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
- conference location
- Virtual, Online
- conference dates
- 2020-10-10 - 2020-10-21
- external identifiers
-
- scopus:85089215848
- ISSN
- 0271-4310
- ISBN
- 9781728133201
- DOI
- 10.1109/ISCAS45731.2020.9180429
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2020 IEEE
- id
- f66779f6-4eeb-4c15-af37-7d1ce260c10d
- date added to LUP
- 2022-02-05 01:16:26
- date last changed
- 2022-04-22 07:20:27
@inproceedings{f66779f6-4eeb-4c15-af37-7d1ce260c10d,
abstract = {{<p>Deep convolutional neural networks have recently emerged as a state-of-the art tool in detection of seizures. Such models offer the ability to extract complex nonlinear representations of an electroencephalogram (EEG) signal which can improve accuracy over methods relying on hand-crafted features. However, neural networks are susceptible to confounding artifacts commonly present in EEG signals and are notoriously difficult to interpret. In this work, we present a neural-network based algorithm for seizure detection which leverages recent advances in information theory to construct a signal representation containing the minimal amount of information necessary to discriminate between seizure and normal brain activity. We show our approach automatically learns representations that ignore common signal artifacts and which encode medically relevant information from the raw signal.</p>}},
author = {{Thomas, Anthony Hitchcock and Aminifar, Amir and Atienza, David}},
booktitle = {{2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings}},
isbn = {{9781728133201}},
issn = {{0271-4310}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings - IEEE International Symposium on Circuits and Systems}},
title = {{Noise-resilient and interpretable epileptic seizure detection}},
url = {{http://dx.doi.org/10.1109/ISCAS45731.2020.9180429}},
doi = {{10.1109/ISCAS45731.2020.9180429}},
volume = {{2020-October}},
year = {{2020}},
}