Parkinsonian Tremor Identification with Multiple Local Field Potential Feature Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00194267" target="_blank" >RIV/68407700:21230/12:00194267 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.jneumeth.2012.06.027" target="_blank" >http://dx.doi.org/10.1016/j.jneumeth.2012.06.027</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jneumeth.2012.06.027" target="_blank" >10.1016/j.jneumeth.2012.06.027</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parkinsonian Tremor Identification with Multiple Local Field Potential Feature Classification

Popis výsledku v původním jazyce

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internusof eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization.A range of signal processing techniques were evaluated with respect to their tremor detection capability and used asinputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy onpatients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

Název v anglickém jazyce

Parkinsonian Tremor Identification with Multiple Local Field Potential Feature Classification

Popis výsledku anglicky

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internusof eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization.A range of signal processing techniques were evaluated with respect to their tremor detection capability and used asinputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy onpatients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GA309%2F09%2F1145" target="_blank" >GA309/09/1145: Mechanismy hluboké mozkové stimulace: Úloha subthalamu v motorických, vizuálních a afektivních procesech</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

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

2

Číslo periodika v rámci svazku

209

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

320-330

Kód UT WoS článku

000308897900007

EID výsledku v databázi Scopus

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