Cargando…

BacAv, a new free online platform for clinical back-averaging

OBJECTIVE: The back-average technique is very useful to study the relation between the activity in the cortex and the muscles. It has two main clinical applications, Bereitschaftspotential (BP) recording and myoclonus studies. The BP is a slow wave negativity originating in the supplementary motor c...

Descripción completa

Detalles Bibliográficos
Autores principales: Vial, Felipe, Attaripour, Sanaz, McGurrin, Patrick, Hallett, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033354/
https://www.ncbi.nlm.nih.gov/pubmed/32095660
http://dx.doi.org/10.1016/j.cnp.2019.12.001
_version_ 1783499648996999168
author Vial, Felipe
Attaripour, Sanaz
McGurrin, Patrick
Hallett, Mark
author_facet Vial, Felipe
Attaripour, Sanaz
McGurrin, Patrick
Hallett, Mark
author_sort Vial, Felipe
collection PubMed
description OBJECTIVE: The back-average technique is very useful to study the relation between the activity in the cortex and the muscles. It has two main clinical applications, Bereitschaftspotential (BP) recording and myoclonus studies. The BP is a slow wave negativity originating in the supplementary motor cortex and premotor cortex that precedes voluntary movements. This wave also precedes involuntary movements in functional movement disorders (FMD), and it can be used as a helpful diagnostic tool. For the myoclonus studies, the back-average technique is very important to help localizing the source of the myoclonus. The hardware needed to do BP or myoclonus studies is standard and available in any electrophysiology lab, but there are not many software solutions to do the analysis. In this article together with describing the methodology that we use for recording clinical BPs and myoclonus, we present BacAv, an online free application that we developed for the purpose of doing back-average analysis. METHODS: BacAv was developed in “R” language using Rstudio, a free integrated development environment. The recommended parameters for the data acquisition for BP recording and myoclonus studies are given in this section. RESULTS: The platform was successfully developed, is able to read txt files, look for muscle bursts, segment the data, and plot the average. The parameters of the algorithm that look for the muscle bursts can be adapted according to the characteristics of the dataset. CONCLUSION: We have developed software for clinicians who do not have sophisticated equipment to do back-averaging. SIGNIFICANCE: This tool will make this useful analysis method more available in a clinical environment.
format Online
Article
Text
id pubmed-7033354
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-70333542020-02-24 BacAv, a new free online platform for clinical back-averaging Vial, Felipe Attaripour, Sanaz McGurrin, Patrick Hallett, Mark Clin Neurophysiol Pract Clinical and Research Article OBJECTIVE: The back-average technique is very useful to study the relation between the activity in the cortex and the muscles. It has two main clinical applications, Bereitschaftspotential (BP) recording and myoclonus studies. The BP is a slow wave negativity originating in the supplementary motor cortex and premotor cortex that precedes voluntary movements. This wave also precedes involuntary movements in functional movement disorders (FMD), and it can be used as a helpful diagnostic tool. For the myoclonus studies, the back-average technique is very important to help localizing the source of the myoclonus. The hardware needed to do BP or myoclonus studies is standard and available in any electrophysiology lab, but there are not many software solutions to do the analysis. In this article together with describing the methodology that we use for recording clinical BPs and myoclonus, we present BacAv, an online free application that we developed for the purpose of doing back-average analysis. METHODS: BacAv was developed in “R” language using Rstudio, a free integrated development environment. The recommended parameters for the data acquisition for BP recording and myoclonus studies are given in this section. RESULTS: The platform was successfully developed, is able to read txt files, look for muscle bursts, segment the data, and plot the average. The parameters of the algorithm that look for the muscle bursts can be adapted according to the characteristics of the dataset. CONCLUSION: We have developed software for clinicians who do not have sophisticated equipment to do back-averaging. SIGNIFICANCE: This tool will make this useful analysis method more available in a clinical environment. Elsevier 2020-01-25 /pmc/articles/PMC7033354/ /pubmed/32095660 http://dx.doi.org/10.1016/j.cnp.2019.12.001 Text en http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Clinical and Research Article
Vial, Felipe
Attaripour, Sanaz
McGurrin, Patrick
Hallett, Mark
BacAv, a new free online platform for clinical back-averaging
title BacAv, a new free online platform for clinical back-averaging
title_full BacAv, a new free online platform for clinical back-averaging
title_fullStr BacAv, a new free online platform for clinical back-averaging
title_full_unstemmed BacAv, a new free online platform for clinical back-averaging
title_short BacAv, a new free online platform for clinical back-averaging
title_sort bacav, a new free online platform for clinical back-averaging
topic Clinical and Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7033354/
https://www.ncbi.nlm.nih.gov/pubmed/32095660
http://dx.doi.org/10.1016/j.cnp.2019.12.001
work_keys_str_mv AT vialfelipe bacavanewfreeonlineplatformforclinicalbackaveraging
AT attaripoursanaz bacavanewfreeonlineplatformforclinicalbackaveraging
AT mcgurrinpatrick bacavanewfreeonlineplatformforclinicalbackaveraging
AT hallettmark bacavanewfreeonlineplatformforclinicalbackaveraging