Intracerebral EEG Artifact Identification Using Convolutional
Neural Networks

J 2019

Intracerebral EEG Artifact Identification Using Convolutional Neural Networks

NEJEDLY, Petr, Jan CIMBALNIK, Petr KLIMES, Filip PLESINGER, Josef HALAMEK et. al.

Základní údaje

Originální název

Intracerebral EEG Artifact Identification Using Convolutional Neural Networks

Autoři

NEJEDLY, Petr (203 Česká republika, garant), Jan CIMBALNIK (203 Česká republika), Petr KLIMES (203 Česká republika), Filip PLESINGER (203 Česká republika), Josef HALAMEK (203 Česká republika), Vaclav KREMEN (203 Česká republika), Ivo VISCOR (203 Česká republika), Benjamin H. BRINKMANN (840 Spojené státy), Martin PAIL (203 Česká republika, domácí), Milan BRÁZDIL (203 Česká republika, domácí), Gregory WORRELL (840 Spojené státy) a Pavel JURAK (203 Česká republika)

Vydání

NEUROINFORMATICS, TOTOWA, HUMANA PRESS INC, 2019, 1539-2791

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30103 Neurosciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.300

Kód RIV

RIV/00216224:14110/19:00107403

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.1007/s12021-018-9397-6

UT WoS

000464856900005

Klíčová slova anglicky

Intracranial EEG (iEEG); Noise detection; Convolutional neural networks (CNN); Artifact probability matrix (APM)

Štítky

14110127, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 3. 3. 2020 16:52, Mgr. Pavla Foltynová, Ph.D.

Anotace

V originále

Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method's performance against expert annotations. The method was trained and tested on data obtained from St Anne's University Hospital (Brno, Czech Republic) and validated on data from Mayo Clinic (Rochester, Minnesota, U.S.A). We show that the proposed technique can be used as a generalized model for iEEG artifact detection. Moreover, a transfer learning process might be used for retraining of the generalized version to form a data-specific model. The generalized model can be efficiently retrained for use with different EEG acquisition systems and noise environments. The generalized and specialized model F1 scores on the testing dataset were 0.81 and 0.96, respectively. The CNN model provides faster, more objective, and more reproducible iEEG artifact detection compared to manual approaches.

Návaznosti

GAP103/11/0933, projekt VaV

Název: Analýza vysokofrekvenčního EEG signálu z hlubokých mozkových elektrod

Investor: Grantová agentura ČR, Analýza vysokofrekvenčního EEG signálu z hlubokých mozkových elektrod

NV16-33798A, projekt VaV

Název: Modulace funkční konektivity kortikálních sítí vlivem STN DBS

Citovat

NEJEDLY, Petr, Jan CIMBALNIK, Petr KLIMES, Filip PLESINGER, Josef HALAMEK, Vaclav KREMEN, Ivo VISCOR, Benjamin H. BRINKMANN, Martin PAIL, Milan BRÁZDIL, Gregory WORRELL a Pavel JURAK. Intracerebral EEG Artifact Identification Using Convolutional Neural Networks. NEUROINFORMATICS. TOTOWA: HUMANA PRESS INC, 2019, roč. 17, č. 2, s. 225-234. ISSN 1539-2791. Dostupné z: https://dx.doi.org/10.1007/s12021-018-9397-6.

@article{1531399,
   author = {Nejedly, Petr and Cimbalnik, Jan and Klimes, Petr and Plesinger, Filip and Halamek, Josef and Kremen, Vaclav and Viscor, Ivo and Brinkmann, Benjamin H. and Pail, Martin and Brázdil, Milan and Worrell, Gregory and Jurak, Pavel},
   article_location = {TOTOWA},
   article_number = {2},
   doi = {http://dx.doi.org/10.1007/s12021-018-9397-6},
   keywords = {Intracranial EEG (iEEG); Noise detection; Convolutional neural networks (CNN); Artifact probability matrix (APM)},
   language = {eng},
   issn = {1539-2791},
   journal = {NEUROINFORMATICS},
   title = {Intracerebral EEG Artifact Identification Using Convolutional Neural Networks},
   url = {https://link.springer.com/content/pdf/10.1007%2Fs12021-018-9397-6.pdf},
   volume = {17},
   year = {2019}
}

TY  - JOUR
ID  - 1531399
AU  - Nejedly, Petr - Cimbalnik, Jan - Klimes, Petr - Plesinger, Filip - Halamek, Josef - Kremen, Vaclav - Viscor, Ivo - Brinkmann, Benjamin H. - Pail, Martin - Brázdil, Milan - Worrell, Gregory - Jurak, Pavel
PY  - 2019
TI  - Intracerebral EEG Artifact Identification Using Convolutional Neural Networks
JF  - NEUROINFORMATICS
VL  - 17
IS  - 2
SP  - 225-234
EP  - 225-234
PB  - HUMANA PRESS INC
SN  - 15392791
KW  - Intracranial EEG (iEEG)
KW  - Noise detection
KW  - Convolutional neural networks (CNN)
KW  - Artifact probability matrix (APM)
UR  - https://link.springer.com/content/pdf/10.1007%2Fs12021-018-9397-6.pdf
L2  - https://link.springer.com/content/pdf/10.1007%2Fs12021-018-9397-6.pdf
N2  - Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method's performance against expert annotations. The method was trained and tested on data obtained from St Anne's University Hospital (Brno, Czech Republic) and validated on data from Mayo Clinic (Rochester, Minnesota, U.S.A). We show that the proposed technique can be used as a generalized model for iEEG artifact detection. Moreover, a transfer learning process might be used for retraining of the generalized version to form a data-specific model. The generalized model can be efficiently retrained for use with different EEG acquisition systems and noise environments. The generalized and specialized model F1 scores on the testing dataset were 0.81 and 0.96, respectively. The CNN model provides faster, more objective, and more reproducible iEEG artifact detection compared to manual approaches.
ER  -

NEJEDLY, Petr, Jan CIMBALNIK, Petr KLIMES, Filip PLESINGER, Josef HALAMEK, Vaclav KREMEN, Ivo VISCOR, Benjamin H. BRINKMANN, Martin PAIL, Milan BRÁZDIL, Gregory WORRELL a Pavel JURAK. Intracerebral EEG Artifact Identification Using Convolutional Neural Networks. \textit{NEUROINFORMATICS}. TOTOWA: HUMANA PRESS INC, 2019, roč.~17, č.~2, s.~225-234. ISSN~1539-2791. Dostupné z: https://dx.doi.org/10.1007/s12021-018-9397-6.

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