Multifractal Analysis of Electromyography Data-Reference-Cited by-同舟云学术

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Multifractal Analysis of Electromyography Data

Published:2019 Issue: Volume: Page:97-116
ISSN:
Container-title:Multifractals and Chronic Diseases of the Central Nervous System
language:
Short-container-title:

Author:

Ghosh Dipak,Samanta Shukla,Chakraborty Sayantan

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-3552-5_4

Reference102 articles.

1. Acharya UR, Ng EYK, Swapna G, Michelle YSL (2011) Classification of normal, neuropathic, and myopathic electromyography signals using non-linear dynamics method. J Med Imaging Health Inform 1:375–380

2. Alamedine D, Khalil M, Marque C (2013) Comparison of different EHG feature selection methods for the detection of preterm labo. Comput Math Method Med 2013:585–593

3. Alkan A, Gunay M (2012) Identification of EMG signals using discriminant analysis and SVM classifier. Expert Syst Appl 39:44–47

4. Ancillao A, Galli M, Rigoldi C, Albertini G (2014) Linear correlation between fractal dimension of surface EMG signal from rectus femoris and height of vertical jump. Chaos Solitons Fractals 66:120–126

5. Anmuth CJ, Goldberg G, Mayer NH (1994) Fractal dimension of EMG signals recorded with surface electrodes during isometric contractions is linearly correlated with muscle activation. Muscle Nerve 17:953–954

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. A comparative study on estimation of fractal dimension of EMG signal using SWT and FLP;Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization;2022-07-01

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