Permutation Entropy and Signal Energy Increase the Accuracy of Neuropathic Change Detection in Needle EMG

Autor: Ladislav Pazdera, Ondrej Dostal, Martin Vališ, O. Vysata, Jakub Kopal, Jiří Kuchyňka, Ales Prochazka
Rok vydání: 2018
Předmět:
Adult
Male
Support Vector Machine
Article Subject
General Computer Science
Needle emg
Computer science
Entropy
General Mathematics
02 engineering and technology
Electromyography
lcsh:Computer applications to medicine. Medical informatics
01 natural sciences
lcsh:RC321-571
Young Adult
0103 physical sciences
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
Permutation entropy
Entropy (energy dispersal)
010306 general physics
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Aged
Signal processing
medicine.diagnostic_test
business.industry
General Neuroscience
Peripheral Nervous System Diseases
Signal Processing
Computer-Assisted
Pattern recognition
General Medicine
Middle Aged
Support vector machine
Amplitude
lcsh:R858-859.7
Female
020201 artificial intelligence & image processing
Artificial intelligence
business
Algorithms
Change detection
Research Article
Zdroj: Computational Intelligence and Neuroscience, Vol 2018 (2018)
Computational Intelligence and Neuroscience
ISSN: 1687-5273
1687-5265
DOI: 10.1155/2018/5276161
Popis: Background and Objective. Needle electromyography can be used to detect the number of changes and morphological changes in motor unit potentials of patients with axonal neuropathy. General mathematical methods of pattern recognition and signal analysis were applied to recognize neuropathic changes. This study validates the possibility of extending and refining turns-amplitude analysis using permutation entropy and signal energy. Methods. In this study, we examined needle electromyography in 40 neuropathic individuals and 40 controls. The number of turns, amplitude between turns, signal energy, and “permutation entropy” were used as features for support vector machine classification. Results. The obtained results proved the superior classification performance of the combinations of all of the above-mentioned features compared to the combinations of fewer features. The lowest accuracy from the tested combinations of features had peak-ratio analysis. Conclusion. Using the combination of permutation entropy with signal energy, number of turns and mean amplitude in SVM classification can be used to refine the diagnosis of polyneuropathies examined by needle electromyography.
Databáze: OpenAIRE
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