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Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution

A deconvolution method is proposed for conduction block (CB) estimation based on two compound muscle action potentials (CMAPs) elicited by stimulating a nerve proximal and distal to the region in which the block is suspected. It estimates the time delay distributions by CMAPs deconvolution, from whi...

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Autores principales: Mesin, Luca, Lingua, Edoardo, Cocito, Dario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8773146/
https://www.ncbi.nlm.nih.gov/pubmed/35049732
http://dx.doi.org/10.3390/bioengineering9010023
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author Mesin, Luca
Lingua, Edoardo
Cocito, Dario
author_facet Mesin, Luca
Lingua, Edoardo
Cocito, Dario
author_sort Mesin, Luca
collection PubMed
description A deconvolution method is proposed for conduction block (CB) estimation based on two compound muscle action potentials (CMAPs) elicited by stimulating a nerve proximal and distal to the region in which the block is suspected. It estimates the time delay distributions by CMAPs deconvolution, from which CB is computed. The slow afterwave (SAW) is included to describe the motor unit potential, as it gives an important contribution in case of the large temporal dispersion (TD) often found in patients. The method is tested on experimental signals obtained from both healthy subjects and pathological patients, with either Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) or Multifocal Motor Neuropathy (MMN). The new technique outperforms the clinical methods (based on amplitude and area of CMAPs) and a previous state-of-the-art deconvolution approach. It compensates phase cancellations, allowing to discriminate among CB and TD: estimated by the methods of amplitude, area and deconvolution, CB showed a correlation with TD equal to 39.3%, 29.5% and 8.2%, respectively. Moreover, a significant decrease of percentage reconstruction errors of the CMAPs with respect to the previous deconvolution approach is obtained (from a mean/median of 19.1%/16.7% to 11.7%/11.2%). Therefore, the new method is able to discriminate between CB and TD (overcoming the important limitation of clinical approaches) and can approximate patients’ CMAPs better than the previous deconvolution algorithm. Then, it appears to be promising for the diagnosis of demyelinating polyneuropathies, to be further tested in the future in a prospective clinical trial.
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spelling pubmed-87731462022-01-21 Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution Mesin, Luca Lingua, Edoardo Cocito, Dario Bioengineering (Basel) Article A deconvolution method is proposed for conduction block (CB) estimation based on two compound muscle action potentials (CMAPs) elicited by stimulating a nerve proximal and distal to the region in which the block is suspected. It estimates the time delay distributions by CMAPs deconvolution, from which CB is computed. The slow afterwave (SAW) is included to describe the motor unit potential, as it gives an important contribution in case of the large temporal dispersion (TD) often found in patients. The method is tested on experimental signals obtained from both healthy subjects and pathological patients, with either Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) or Multifocal Motor Neuropathy (MMN). The new technique outperforms the clinical methods (based on amplitude and area of CMAPs) and a previous state-of-the-art deconvolution approach. It compensates phase cancellations, allowing to discriminate among CB and TD: estimated by the methods of amplitude, area and deconvolution, CB showed a correlation with TD equal to 39.3%, 29.5% and 8.2%, respectively. Moreover, a significant decrease of percentage reconstruction errors of the CMAPs with respect to the previous deconvolution approach is obtained (from a mean/median of 19.1%/16.7% to 11.7%/11.2%). Therefore, the new method is able to discriminate between CB and TD (overcoming the important limitation of clinical approaches) and can approximate patients’ CMAPs better than the previous deconvolution algorithm. Then, it appears to be promising for the diagnosis of demyelinating polyneuropathies, to be further tested in the future in a prospective clinical trial. MDPI 2022-01-10 /pmc/articles/PMC8773146/ /pubmed/35049732 http://dx.doi.org/10.3390/bioengineering9010023 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mesin, Luca
Lingua, Edoardo
Cocito, Dario
Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution
title Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution
title_full Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution
title_fullStr Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution
title_full_unstemmed Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution
title_short Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution
title_sort motor nerve conduction block estimation in demyelinating neuropathies by deconvolution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8773146/
https://www.ncbi.nlm.nih.gov/pubmed/35049732
http://dx.doi.org/10.3390/bioengineering9010023
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