Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models

Tal Pal Attia; Pedro F. Viana; Mona Nasseri; Jonas Duun-Henriksen; Andrea Biondi; Joel S. Winston; Isabel P. Martins; Ewan S. Nurse; Matthias Dümpelmann; Gregory A. Worrell; Andreas Schulze-Bonhage; Dean R. Freestone; Troels W. Kjaer; Benjamin H. Brinkmann; Mark P. Richardson

doi:10.1111/epi.17265

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models

Tal Pal Attia, Pedro F. Viana, Mona Nasseri, Jonas Duun-Henriksen, Andrea Biondi, Joel S. Winston, Isabel P. Martins, Ewan S. Nurse, Matthias Dümpelmann, Gregory A. Worrell, Andreas Schulze-Bonhage, Dean R. Freestone, Troels W. Kjaer, Benjamin H. Brinkmann, Mark P. Richardson

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	S114-S123
Journal	Epilepsia
Volume	64
Issue number	S4
DOIs	https://doi.org/10.1111/epi.17265
State	Published - Dec 2023
Externally published	Yes

ASJC Scopus Subject Areas

Neurology
Clinical Neurology

Keywords

LSTM neural networks
deep neural networks
epilepsy
machine learning
seizure forecasting
subcutaneous EEG

Access to Document

10.1111/epi.17265

Cite this

Pal Attia, T., Viana, P. F., Nasseri, M., Duun-Henriksen, J., Biondi, A., Winston, J. S., P. Martins, I., Nurse, E. S., Dümpelmann, M., Worrell, G. A., Schulze-Bonhage, A., Freestone, D. R., Kjaer, T. W., Brinkmann, B. H., & Richardson, M. P. (2023). Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models. Epilepsia, 64(S4), S114-S123. https://doi.org/10.1111/epi.17265

Pal Attia, T, Viana, PF, Nasseri, M, Duun-Henriksen, J, Biondi, A, Winston, JS, P. Martins, I, Nurse, ES, Dümpelmann, M, Worrell, GA, Schulze-Bonhage, A, Freestone, DR, Kjaer, TW, Brinkmann, BH & Richardson, MP 2023, 'Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models', Epilepsia, vol. 64, no. S4, pp. S114-S123. https://doi.org/10.1111/epi.17265

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title = "Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models",

keywords = "LSTM neural networks, deep neural networks, epilepsy, machine learning, seizure forecasting, subcutaneous EEG",

author = "\{Pal Attia\}, Tal and Viana, \{Pedro F.\} and Mona Nasseri and Jonas Duun-Henriksen and Andrea Biondi and Winston, \{Joel S.\} and \{P. Martins\}, Isabel and Nurse, \{Ewan S.\} and Matthias D{\"u}mpelmann and Worrell, \{Gregory A.\} and Andreas Schulze-Bonhage and Freestone, \{Dean R.\} and Kjaer, \{Troels W.\} and Brinkmann, \{Benjamin H.\} and Richardson, \{Mark P.\}",

note = "Publisher Copyright: {\textcopyright} 2022 International League Against Epilepsy.",

year = "2023",

month = dec,

doi = "10.1111/epi.17265",

language = "English",

volume = "64",

pages = "S114--S123",

number = "S4",

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TY - JOUR

T1 - Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG

T2 - Generalizable cross-patient models

AU - Pal Attia, Tal

AU - Viana, Pedro F.

AU - Nasseri, Mona

AU - Duun-Henriksen, Jonas

AU - Biondi, Andrea

AU - Winston, Joel S.

AU - P. Martins, Isabel

AU - Nurse, Ewan S.

AU - Dümpelmann, Matthias

AU - Worrell, Gregory A.

AU - Schulze-Bonhage, Andreas

AU - Freestone, Dean R.

AU - Kjaer, Troels W.

AU - Brinkmann, Benjamin H.

AU - Richardson, Mark P.

PY - 2023/12

Y1 - 2023/12

KW - LSTM neural networks

KW - deep neural networks

KW - epilepsy

KW - machine learning

KW - seizure forecasting

KW - subcutaneous EEG

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DO - 10.1111/epi.17265

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JO - Epilepsia

JF - Epilepsia

IS - S4

ER -

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models

ASJC Scopus Subject Areas

Keywords

Access to Document

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