Classification of needle-EMG resting potentials by machine learning-Reference-Cited by-同舟云学术

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Classification of needle-EMG resting potentials by machine learning

Published:2018-12-18 Issue:2 Volume:59 Page:224-228
ISSN:0148-639X
Container-title:Muscle & Nerve
language:en
Short-container-title:Muscle Nerve

Author:

Nodera Hiroyuki¹,Osaki Yusuke¹,Yamazaki Hiroki¹,Mori Atsuko¹,Izumi Yuishin¹,Kaji Ryuji¹

Affiliation:

1. Department of Neurology; 3-18-15 Kuramotocho, Tokushima City 770-8503 Japan

Funder

Japan Society for the Promotion of Science

Publisher

Wiley

Subject

Physiology (medical),Cellular and Molecular Neuroscience,Clinical Neurology,Physiology

Link

https://onlinelibrary.wiley.com/doi/am-pdf/10.1002%2Fmus.26363

Reference25 articles.

1. Electrodiagnosis in Diseases of Nerve and Muscle

2. Needle electromyography;Daube;Muscle Nerve,2009

3. Ten quick tips for machine learning in computational biology;Chicco;BioData Min,2017

4. Differentiation of spontaneous activity from normal and denervated skeletal muscle;Heckmann;J Neurol Neurosurg Psychiatry,1982

5. The role of different EMG methods in evaluating myopathy;Fuglsang-Frederiksen;Clin Neurophysiol,2006

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1. Artificial intelligence for automatic classification of needle EMG signals: A scoping review;Clinical Neurophysiology;2024-03

2. The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions;Muscle & Nerve;2023-12-27

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4. Artificial Intelligence Applications in the Diagnosis of Neuromuscular Diseases: A Narrative Review;Cureus;2023-11-07

5. Combining electromyographic and electrical impedance data sets through machine learning: A study in D2‐mdx and wild‐type mice;Muscle & Nerve;2023-09-02

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