Artificial intelligence-based classification of motor unit action potentials in real-world needle EMG recordings-Reference-Cited by-同舟云学术

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Artificial intelligence-based classification of motor unit action potentials in real-world needle EMG recordings

Published:2023-12 Issue: Volume:156 Page:220-227
ISSN:1388-2457
Container-title:Clinical Neurophysiology
language:en
Short-container-title:Clinical Neurophysiology

Author:

Hubers Deborah^ORCID,Potters Wouter^ORCID,Paalvast Olivier^ORCID,de Jonge Sterre,Doelkahar Brian^ORCID,Tannemaat Martijn^ORCID,Wieske Luuk^ORCID,Verhamme Camiel^ORCID

Funder

UCB Pharma

Leids Universitair Medisch Centrum

Publisher

Elsevier BV

Subject

Physiology (medical),Neurology (clinical),Neurology,Sensory Systems

Reference28 articles.

1. Deep neural network assisted diagnosis of time-frequency transformed electromyograms;Bakiya;Multimed Tools Appl,2020

2. Action potential parameters in different human muscles;Buchtal;Acta Psychiatr Neurol Scand,1955

3. Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders;Chatterjee;IET Science Meas Technol,2020

4. Unsupervised pattern recognition for the classification of EMG signals;Christodoulou;IEEE Trans Biomed Eng,1999

5. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration;Cohen;BMJ Open,2016

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Design of an EMG Signal Generator Based on Random Firing Patterns;International Journal of Online and Biomedical Engineering (iJOE);2024-05-06

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