Methods for automatic detection of artifacts in microelectrode recordings

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21230/17:00312858 RIV/00216208:11110/17:10363844 RIV/00064165:_____/17:10363844 RIV/00023884:_____/12:00007434

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Methods for automatic detection of artifacts in microelectrode recordings

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

Methods for automatic detection of artifacts in microelectrode recordings

Popis výsledku anglicky

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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Journal of Neuroscience Methods

ISSN

0165-0270

e-ISSN

—

Svazek periodika

290

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

39-51

Kód UT WoS článku

000411776000005

EID výsledku v databázi Scopus

2-s2.0-85026263634