Identification of microrecording artifacts with wavelet analysis and convolutional neural network: an image recognition Approach

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21230/19:00334854 RIV/68407700:21460/19:00334854 RIV/00216208:11110/19:10398954 RIV/00064165:_____/19:10398954

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Identification of microrecording artifacts with wavelet analysis and convolutional neural network: an image recognition Approach

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Identification of microrecording artifacts with wavelet analysis and convolutional neural network: an image recognition Approach

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/NV19-04-00233" target="_blank" >NV19-04-00233: Klinické, zobrazovací a biologické prediktory účinků hluboké mozkové stimulace u Parkinsonovy nemoci</a><br>

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Measurement Science Review

ISSN

1335-8871

e-ISSN

—

Svazek periodika

19

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

10

Strana od-do

222-231

Kód UT WoS článku

000489311900005

EID výsledku v databázi Scopus

2-s2.0-85074544169