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Application of new waveform analysis methods reflecting F-wave diversity -classification of F-wave diversity according to differences in the derived muscles

BACKGROUND: The F wave waveforms show diversity according to the number and size of re-firing cells, but there is still no analytical method that reflects this feature. We previously reported that five classifications of F waves are obtained from the ulnar nerve. However, the diversity of F waves de...

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Autores principales: Todo, Marina, Suzuki, Toshiaki, Hanaoka, Masaaki, Asai, Hitoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10550510/
https://www.ncbi.nlm.nih.gov/pubmed/37800073
http://dx.doi.org/10.1016/j.heliyon.2023.e20551
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author Todo, Marina
Suzuki, Toshiaki
Hanaoka, Masaaki
Asai, Hitoshi
author_facet Todo, Marina
Suzuki, Toshiaki
Hanaoka, Masaaki
Asai, Hitoshi
author_sort Todo, Marina
collection PubMed
description BACKGROUND: The F wave waveforms show diversity according to the number and size of re-firing cells, but there is still no analytical method that reflects this feature. We previously reported that five classifications of F waves are obtained from the ulnar nerve. However, the diversity of F waves derived from the lower extremities may not be similar. We therefore compared the diversity of F waves in the upper and lower extremities in healthy subjects. NEW METHOD: F waves were measured during tibial nerve stimulation in 26 healthy subjects. The amount of amplitude decrease was calculated from the amplitude value after the additive averaging process and based on the average amplitude value of each stimulus, and the relationship between the peak latency and density was examined. RESULTS: The amount of amplitude decrease due to the additive averaging process was negatively correlated with the density of negative peaks. The diversity of F waves could be categorized into four class based on the histograms. COMPARISON WITH EXISTING METHOD: The new method uses a novel additive average method that reflects the diversity of F waves. Furthermore, it uses a histogram to visualize the cancellation between waveforms. CONCLUSION: We developed an analysis method that reflects the diversity of F waves in a novel manner, which visualizes cancellation between waveforms using a histogram.
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spelling pubmed-105505102023-10-05 Application of new waveform analysis methods reflecting F-wave diversity -classification of F-wave diversity according to differences in the derived muscles Todo, Marina Suzuki, Toshiaki Hanaoka, Masaaki Asai, Hitoshi Heliyon Research Article BACKGROUND: The F wave waveforms show diversity according to the number and size of re-firing cells, but there is still no analytical method that reflects this feature. We previously reported that five classifications of F waves are obtained from the ulnar nerve. However, the diversity of F waves derived from the lower extremities may not be similar. We therefore compared the diversity of F waves in the upper and lower extremities in healthy subjects. NEW METHOD: F waves were measured during tibial nerve stimulation in 26 healthy subjects. The amount of amplitude decrease was calculated from the amplitude value after the additive averaging process and based on the average amplitude value of each stimulus, and the relationship between the peak latency and density was examined. RESULTS: The amount of amplitude decrease due to the additive averaging process was negatively correlated with the density of negative peaks. The diversity of F waves could be categorized into four class based on the histograms. COMPARISON WITH EXISTING METHOD: The new method uses a novel additive average method that reflects the diversity of F waves. Furthermore, it uses a histogram to visualize the cancellation between waveforms. CONCLUSION: We developed an analysis method that reflects the diversity of F waves in a novel manner, which visualizes cancellation between waveforms using a histogram. Elsevier 2023-09-29 /pmc/articles/PMC10550510/ /pubmed/37800073 http://dx.doi.org/10.1016/j.heliyon.2023.e20551 Text en © 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Todo, Marina
Suzuki, Toshiaki
Hanaoka, Masaaki
Asai, Hitoshi
Application of new waveform analysis methods reflecting F-wave diversity -classification of F-wave diversity according to differences in the derived muscles
title Application of new waveform analysis methods reflecting F-wave diversity -classification of F-wave diversity according to differences in the derived muscles
title_full Application of new waveform analysis methods reflecting F-wave diversity -classification of F-wave diversity according to differences in the derived muscles
title_fullStr Application of new waveform analysis methods reflecting F-wave diversity -classification of F-wave diversity according to differences in the derived muscles
title_full_unstemmed Application of new waveform analysis methods reflecting F-wave diversity -classification of F-wave diversity according to differences in the derived muscles
title_short Application of new waveform analysis methods reflecting F-wave diversity -classification of F-wave diversity according to differences in the derived muscles
title_sort application of new waveform analysis methods reflecting f-wave diversity -classification of f-wave diversity according to differences in the derived muscles
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10550510/
https://www.ncbi.nlm.nih.gov/pubmed/37800073
http://dx.doi.org/10.1016/j.heliyon.2023.e20551
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