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Personalized seizure signature : An interpretable approach to false alarm reduction for long-term epileptic seizure detection

Sopic, Dionisije ; Teijeiro, Tomas ; Atienza, David ; Aminifar, Amir LU orcid and Ryvlin, Philippe (2023) In Epilepsia 64(S4).
Abstract

Objective: Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific approach based on similarly occurring morphological electroencephalographic (EEG) signal patterns could be used to distinguish seizures from nonseizure events, as well as to estimate its maximum performance. Methods: We evaluated our approach on >5500 h of long-term EEG recordings using two public datasets: the PhysioNet.org Children’s Hospital Boston–Massachusetts Institute of Technology (CHB-MIT) Scalp EEG database and the EPILEPSIAE European epilepsy database. We visually... (More)

Objective: Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific approach based on similarly occurring morphological electroencephalographic (EEG) signal patterns could be used to distinguish seizures from nonseizure events, as well as to estimate its maximum performance. Methods: We evaluated our approach on >5500 h of long-term EEG recordings using two public datasets: the PhysioNet.org Children’s Hospital Boston–Massachusetts Institute of Technology (CHB-MIT) Scalp EEG database and the EPILEPSIAE European epilepsy database. We visually identified a set of similarly occurring morphological patterns (seizure signature) seen simultaneously over two different EEG channels, and within two randomly selected seizures from each individual. The same seizure signature was then searched for in the entire recording from the same patient using dynamic time warping (DTW) as a similarity metric, with a threshold set to reflect the maximum sensitivity our algorithm could achieve without false alarm. Results: At a DTW threshold providing no false alarm during the entire recordings, the mean seizure detection sensitivity across patients was 84%, including 96% for the CHB-MIT database and 74% for the European epilepsy database. A 100% sensitivity was reached in 50% of patients, including 79% from the CHB-MIT database and 27% from the European epilepsy database. The median latency from seizure onset to its detection was 17 ± 10 s, with 84% of seizures being detected within 40 s. Significance: Personalized EEG signature combined with DTW appears to be a promising method to detect ictal events from a limited number of EEG channels with high sensitivity despite low rate of false alarms, high degree of interpretability, and low computational complexity, compatible with its future use in wearable devices.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Epilepsia
volume
64
issue
S4
publisher
Wiley-Blackwell
external identifiers
  • pmid:35113451
  • scopus:85124145480
ISSN
0013-9580
DOI
10.1111/epi.17176
language
English
LU publication?
yes
id
1ea60c72-3fc6-4a56-9f74-9030e297e59d
date added to LUP
2022-04-06 11:29:15
date last changed
2024-03-27 14:48:47
@article{1ea60c72-3fc6-4a56-9f74-9030e297e59d,
  abstract     = {{<p>Objective: Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific approach based on similarly occurring morphological electroencephalographic (EEG) signal patterns could be used to distinguish seizures from nonseizure events, as well as to estimate its maximum performance. Methods: We evaluated our approach on &gt;5500 h of long-term EEG recordings using two public datasets: the PhysioNet.org Children’s Hospital Boston–Massachusetts Institute of Technology (CHB-MIT) Scalp EEG database and the EPILEPSIAE European epilepsy database. We visually identified a set of similarly occurring morphological patterns (seizure signature) seen simultaneously over two different EEG channels, and within two randomly selected seizures from each individual. The same seizure signature was then searched for in the entire recording from the same patient using dynamic time warping (DTW) as a similarity metric, with a threshold set to reflect the maximum sensitivity our algorithm could achieve without false alarm. Results: At a DTW threshold providing no false alarm during the entire recordings, the mean seizure detection sensitivity across patients was 84%, including 96% for the CHB-MIT database and 74% for the European epilepsy database. A 100% sensitivity was reached in 50% of patients, including 79% from the CHB-MIT database and 27% from the European epilepsy database. The median latency from seizure onset to its detection was 17 ± 10 s, with 84% of seizures being detected within 40 s. Significance: Personalized EEG signature combined with DTW appears to be a promising method to detect ictal events from a limited number of EEG channels with high sensitivity despite low rate of false alarms, high degree of interpretability, and low computational complexity, compatible with its future use in wearable devices.</p>}},
  author       = {{Sopic, Dionisije and Teijeiro, Tomas and Atienza, David and Aminifar, Amir and Ryvlin, Philippe}},
  issn         = {{0013-9580}},
  language     = {{eng}},
  number       = {{S4}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{Epilepsia}},
  title        = {{Personalized seizure signature : An interpretable approach to false alarm reduction for long-term epileptic seizure detection}},
  url          = {{http://dx.doi.org/10.1111/epi.17176}},
  doi          = {{10.1111/epi.17176}},
  volume       = {{64}},
  year         = {{2023}},
}