Identification of microrecording artifacts with wavelet analysis and convolutional neural network: an image recognition Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F19%3A43920000" target="_blank" >RIV/00023752:_____/19:43920000 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/19:00334854 RIV/68407700:21460/19:00334854 RIV/00216208:11110/19:10398954 RIV/00064165:_____/19:10398954
Result on the web
<a href="https://content.sciendo.com/configurable/contentpage/journals$002fmsr$002f19$002f5$002farticle-p222.xml" target="_blank" >https://content.sciendo.com/configurable/contentpage/journals$002fmsr$002f19$002f5$002farticle-p222.xml</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2478/msr-2019-0029" target="_blank" >10.2478/msr-2019-0029</a>
Alternative languages
Result language
angličtina
Original language name
Identification of microrecording artifacts with wavelet analysis and convolutional neural network: an image recognition Approach
Original language description
Deep brain stimulation (DBS) is an internationally accepted form of treatment option for selected patients with Parkinson’s disease and dystonia. Intraoperative extracellular microelectrode recordings (MER) are considered as the standard electrophysiological method for the precise positioning of the DBS electrode into the target brain structure. Pre-processing of MERs is a key phase in clinical analysis, with intraoperative microelectrode recordings being prone to several artifact groups (up to 25 %). The aim of this methodological article is to provide a convolutional neural network (CNN) processing pipeline for the detection of artifacts in an MER. We applied continuous wavelet transform (CWT) to generate an over-complete time–frequency representation. We demonstrated that when attempting to find artifacts in an MER, the new CNN + CWT provides a high level of accuracy (ACC = 88.1 %), identifies individual classes of artifacts (ACC = 75.3 %) and also offers artifact time onset detail, which can lead to a reduction in false positives/negatives. In summary, the presented methodology is capable of identifying and removing various artifacts in a comprehensive database of MER and represents a substantial improvement over the existing methodology. We believe that this approach will assist in the proposal of interesting clinical hypotheses and will have neurologically relevant effects.
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
20601 - Medical engineering
Result continuities
Project
<a href="/en/project/NV19-04-00233" target="_blank" >NV19-04-00233: Clinical, Imaging and Biological predictors of effects associated with deep brain stimulation in Parkinson’s disease</a><br>
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Measurement Science Review
ISSN
1335-8871
e-ISSN
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Volume of the periodical
19
Issue of the periodical within the volume
5
Country of publishing house
PL - POLAND
Number of pages
10
Pages from-to
222-231
UT code for WoS article
000489311900005
EID of the result in the Scopus database
2-s2.0-85074544169