Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: Individualized intrapatient models

Author:

Viana Pedro F.123ORCID,Pal Attia Tal4ORCID,Nasseri Mona45,Duun‐Henriksen Jonas6ORCID,Biondi Andrea12ORCID,Winston Joel S.12ORCID,Pavão Martins Isabel3ORCID,Nurse Ewan S.78ORCID,Dümpelmann Matthias9ORCID,Schulze‐Bonhage Andreas9ORCID,Freestone Dean R.78ORCID,Kjaer Troels W.1011ORCID,Richardson Mark P.1212ORCID,Brinkmann Benjamin H.4ORCID

Affiliation:

1. School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience King’s College London London UK

2. Centre for Epilepsy King’s College Hospital National Health Service Foundation Trust London UK

3. Faculty of Medicine University of Lisbon Lisbon Portugal

4. Bioelectronics Neurology and Engineering Laboratory Department of Neurology Department of Physiology and Biomedical Engineering Mayo Clinic Rochester Minnesota USA

5. School of Engineering University of North Florida Jacksonville Florida USA

6. UNEEG medical Lillerød Denmark

7. Seer Medical Melbourne Victoria Australia

8. Department of Medicine University of Melbourne Melbourne Victoria Australia

9. Epilepsy Center Department for Neurosurgery University Medical Center Freiburg Freiburg Germany

10. Department of Neurology Zealand University Hospital Roskilde Denmark

11. Department of Clinical Medicine University of Copenhagen Copenhagen Denmark

12. National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust London UK

Funder

European Commission

National Institutes of Health

Publisher

Wiley

Subject

Neurology (clinical),Neurology

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1. Automated algorithms for seizure forecast: a systematic review and meta-analysis;Journal of Neurology;2024-09-06

2. Editorial: Seizure forecasting tools, biomarkers and devices;Frontiers in Neuroscience;2024-08-28

3. User experience of a seizure risk forecasting app: A mixed methods investigation;Epilepsy & Behavior;2024-08

4. Addressing data limitations in seizure prediction through transfer learning;Scientific Reports;2024-06-19

5. EEG-Based Seizure Prediction Using CNN and BiLSTM Algorithm of Deep Learning;2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS);2024-04-18

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