Methods for automatic detection of artifacts in microelectrode recordings
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F17%3A43918935" target="_blank" >RIV/00023752:_____/17:43918935 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/17:00312858 RIV/00216208:11110/17:10363844 RIV/00064165:_____/17:10363844 RIV/00023884:_____/12:00007434
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0165027017302492?via%3Dihub" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0165027017302492?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jneumeth.2017.07.012" target="_blank" >10.1016/j.jneumeth.2017.07.012</a>
Alternative languages
Result language
angličtina
Original language name
Methods for automatic detection of artifacts in microelectrode recordings
Original language description
Background: Extracellular microelectrode recording (MER) is a prominent technique for studies of extracellular single-unit neuronal activity. In order to achieve robust results in more complex analysis pipelines, it is necessary to have high quality input data with a low amount of artifacts. We show that noise (mainly electromagnetic interference and motion artifacts) may affect more than 25% of the recording length in a clinical MER database. New method: We present several methods for automatic detection of noise in MER signals, based on (i) unsupervised detection of stationary segments, (ii) large peaks in the power spectral density, and (iii) a classifier based on multiple time- and frequency-domain features. We evaluate the proposed methods on a manually annotated database of 5735 ten-second MER signals from 58 Parkinson's disease patients. Comparison with existing methods: The existing methods for artifact detection in single-channel MER that have been rigorously tested, are based on unsupervised change-point detection. We show on an extensive real MER database that the presented techniques are better suited for the task of artifact identification and achieve much better results. Results: The best-performing classifiers (bagging and decision tree) achieved artifact classification accuracy of up to 89% on an unseen test set and outperformed the unsupervised techniques by 5–10%. This was close to the level of agreement among raters using manual annotation (93.5%). Conclusion: We conclude that the proposed methods are suitable for automatic MER denoising and may help in the efficient elimination of undesirable signal artifacts.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Neuroscience Methods
ISSN
0165-0270
e-ISSN
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Volume of the periodical
290
Issue of the periodical within the volume
October
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
13
Pages from-to
39-51
UT code for WoS article
000411776000005
EID of the result in the Scopus database
2-s2.0-85026263634