Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
16 06 2020
16 06 2020
Historique:
received:
05
11
2019
accepted:
06
05
2020
entrez:
18
6
2020
pubmed:
18
6
2020
medline:
5
11
2020
Statut:
epublish
Résumé
EEG signal processing is a fundamental method for neurophysiology research and clinical neurology practice. Historically the classification of EEG into physiological, pathological, or artifacts has been performed by expert visual review of the recordings. However, the size of EEG data recordings is rapidly increasing with a trend for higher channel counts, greater sampling frequency, and longer recording duration and complete reliance on visual data review is not sustainable. In this study, we publicly share annotated intracranial EEG data clips from two institutions: Mayo Clinic, MN, USA and St. Anne's University Hospital Brno, Czech Republic. The dataset contains intracranial EEG that are labeled into three groups: physiological activity, pathological/epileptic activity, and artifactual signals. The dataset published here should support and facilitate training of generalized machine learning and digital signal processing methods for intracranial EEG and promote research reproducibility. Along with the data, we also propose a statistical method that is recommended for comparison of candidate classifier performance utilizing out-of-institution/out-of-patient testing.
Identifiants
pubmed: 32546753
doi: 10.1038/s41597-020-0532-5
pii: 10.1038/s41597-020-0532-5
pmc: PMC7297990
doi:
Types de publication
Dataset
Journal Article
Multicenter Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
179Subventions
Organisme : NINDS NIH HHS
ID : R01 NS092882
Pays : United States
Organisme : NINDS NIH HHS
ID : UH2 NS095495
Pays : United States
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