Multicenter intracranial EEG dataset for classification of
graphoelements and artifactual signals

J 2020

Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals

NEJEDLY, P., V. KREMEN, V. SLADKY, J. CIMBALNIK, P. KLIMES et. al.

Basic information

Original name

Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals

Authors

NEJEDLY, P. (203 Czech Republic, guarantor), V. KREMEN (203 Czech Republic), V. SLADKY (203 Czech Republic), J. CIMBALNIK (203 Czech Republic), P. KLIMES (203 Czech Republic), F. PLESINGER (203 Czech Republic), F. MIVALT (203 Czech Republic), V. TRAVNICEK (203 Czech Republic), I. VISCOR (203 Czech Republic), Martin PAIL (203 Czech Republic, belonging to the institution), J. HALAMEK (203 Czech Republic), B. H. BRINKMANN (840 United States of America), Milan BRÁZDIL (203 Czech Republic, belonging to the institution), P. JURAK (203 Czech Republic) and G. WORRELL (840 United States of America)

Edition

Scientific Data, LONDON, NATURE PUBLISHING GROUP, 2020, 2052-4463

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30103 Neurosciences

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 6.444

RIV identification code

RIV/00216224:14110/20:00118605

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1038/s41597-020-0532-5

UT WoS

000542737000002

Keywords in English

HIGH-FREQUENCY OSCILLATIONS

Abstract

V originále

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.

Links

NV19-04-00343, research and development project

Name: Predikce Efektu Stimulace u pacientů s Epilepsií (PRESEnCE) (Acronym: PRESEnCE)

Investor: Ministry of Health of the CR

Citovat

NEJEDLY, P., V. KREMEN, V. SLADKY, J. CIMBALNIK, P. KLIMES, F. PLESINGER, F. MIVALT, V. TRAVNICEK, I. VISCOR, Martin PAIL, J. HALAMEK, B. H. BRINKMANN, Milan BRÁZDIL, P. JURAK and G. WORRELL. Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals. Scientific Data. LONDON: NATURE PUBLISHING GROUP, 2020, vol. 7, No 1, p. 1-7. ISSN 2052-4463. Available from: https://dx.doi.org/10.1038/s41597-020-0532-5.

@article{1670396,
   author = {Nejedly, P. and Kremen, V. and Sladky, V. and Cimbalnik, J. and Klimes, P. and Plesinger, F. and Mivalt, F. and Travnicek, V. and Viscor, I. and Pail, Martin and Halamek, J. and Brinkmann, B. H. and Brázdil, Milan and Jurak, P. and Worrell, G.},
   article_location = {LONDON},
   article_number = {1},
   doi = {http://dx.doi.org/10.1038/s41597-020-0532-5},
   keywords = {HIGH-FREQUENCY OSCILLATIONS},
   language = {eng},
   issn = {2052-4463},
   journal = {Scientific Data},
   title = {Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals},
   url = {https://www.nature.com/articles/s41597-020-0532-5.pdf},
   volume = {7},
   year = {2020}
}

TY  - JOUR
ID  - 1670396
AU  - Nejedly, P. - Kremen, V. - Sladky, V. - Cimbalnik, J. - Klimes, P. - Plesinger, F. - Mivalt, F. - Travnicek, V. - Viscor, I. - Pail, Martin - Halamek, J. - Brinkmann, B. H. - Brázdil, Milan - Jurak, P. - Worrell, G.
PY  - 2020
TI  - Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals
JF  - Scientific Data
VL  - 7
IS  - 1
SP  - 1-7
EP  - 1-7
PB  - NATURE PUBLISHING GROUP
SN  - 20524463
KW  - HIGH-FREQUENCY OSCILLATIONS
UR  - https://www.nature.com/articles/s41597-020-0532-5.pdf
L2  - https://www.nature.com/articles/s41597-020-0532-5.pdf
N2  - 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.
ER  -

NEJEDLY, P., V. KREMEN, V. SLADKY, J. CIMBALNIK, P. KLIMES, F. PLESINGER, F. MIVALT, V. TRAVNICEK, I. VISCOR, Martin PAIL, J. HALAMEK, B. H. BRINKMANN, Milan BRÁZDIL, P. JURAK and G. WORRELL. Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals. \textit{Scientific Data}. LONDON: NATURE PUBLISHING GROUP, 2020, vol.~7, No~1, p.~1-7. ISSN~2052-4463. Available from: https://dx.doi.org/10.1038/s41597-020-0532-5.

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