| Attributes | Values |
|---|
| rdf:type
| |
| Description
| - In needle electromyography, general mathematical methods of pattern recognition and signal analysis can be used to detect motor unit potentials and to classify various types of muscle diseases. The goal of the paper is to contribute to various methods enabling discrimination of individuals with axonal neuropathy (a positive set) from normal cases (a control set) using signals acquired from muscle activities. Data from a control set of 104 individuals and a set of 76 patients were used to validate selected methods of their separation and classification. Different features in both the time and frequency domains were studied to obtain the most reliable results . This novel approach involves comparison of individual features based on adaptive signal thresholding, as well as their combination, using supervised sigmoidal neural networks for their classification. Specificity, sensitivity and accuracy of the features used to detect individuals with axonal neuropathy was analysed using the receiver operating characteristic curves and confusion analysis. An accuracy higher than 93 % was achieved for the given sets of individuals and the optimal criterion values. The proposed modified Willison amplitude together with statistical and spectral properties of signal components classified individuals into sets of healthy and neuropathic patients by artificial neural networks with sensitivity 96.1 % and sufficient accuracy in the wide range of criterion values.
- In needle electromyography, general mathematical methods of pattern recognition and signal analysis can be used to detect motor unit potentials and to classify various types of muscle diseases. The goal of the paper is to contribute to various methods enabling discrimination of individuals with axonal neuropathy (a positive set) from normal cases (a control set) using signals acquired from muscle activities. Data from a control set of 104 individuals and a set of 76 patients were used to validate selected methods of their separation and classification. Different features in both the time and frequency domains were studied to obtain the most reliable results . This novel approach involves comparison of individual features based on adaptive signal thresholding, as well as their combination, using supervised sigmoidal neural networks for their classification. Specificity, sensitivity and accuracy of the features used to detect individuals with axonal neuropathy was analysed using the receiver operating characteristic curves and confusion analysis. An accuracy higher than 93 % was achieved for the given sets of individuals and the optimal criterion values. The proposed modified Willison amplitude together with statistical and spectral properties of signal components classified individuals into sets of healthy and neuropathic patients by artificial neural networks with sensitivity 96.1 % and sufficient accuracy in the wide range of criterion values. (en)
|
| Title
| - Discrimination of axonal neuropathy using sensitivity and specificity statistical measures
- Discrimination of axonal neuropathy using sensitivity and specificity statistical measures (en)
|
| skos:prefLabel
| - Discrimination of axonal neuropathy using sensitivity and specificity statistical measures
- Discrimination of axonal neuropathy using sensitivity and specificity statistical measures (en)
|
| skos:notation
| - RIV/00216208:11150/14:10271668!RIV15-MSM-11150___
|
| http://linked.open...avai/riv/aktivita
| |
| http://linked.open...avai/riv/aktivity
| |
| http://linked.open...iv/cisloPeriodika
| |
| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
| |
| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
| |
| http://linked.open...titaPredkladatele
| |
| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/00216208:11150/14:10271668
|
| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - Artificial neural networks; Computational intelligence; Digital signal processing; Signal classification; ROC curves; Selectivity and specificity measures; Signal features detection; Axonal neuropathy; Needle electromyography (en)
|
| http://linked.open.../riv/klicoveSlovo
| |
| http://linked.open...odStatuVydavatele
| - US - Spojené státy americké
|
| http://linked.open...ontrolniKodProRIV
| |
| http://linked.open...i/riv/nazevZdroje
| - Neural Computing and Applications
|
| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...v/svazekPeriodika
| |
| http://linked.open...iv/tvurceVysledku
| - Procházka, Aleš
- Vališ, Martin
- Vyšata, Oldřich
- Yadollahi, Mohammadreza
- Ťupa, Ondřej
|
| http://linked.open...ain/vavai/riv/wos
| |
| issn
| |
| number of pages
| |
| http://bibframe.org/vocab/doi
| - 10.1007/s00521-014-1622-0
|
| http://localhost/t...ganizacniJednotka
| |
![[RDF Data]](/fct/images/sw-rdf-blue.png)
![[cxml]](/fct/images/cxml_doc.png)
![[csv]](/fct/images/csv_doc.png)
![[text]](/fct/images/ntriples_doc.png)
![[turtle]](/fct/images/n3turtle_doc.png)
![[ld+json]](/fct/images/jsonld_doc.png)
![[rdf+json]](/fct/images/json_doc.png)
![[rdf+xml]](/fct/images/xml_doc.png)
![[atom+xml]](/fct/images/atom_doc.png)
![[html]](/fct/images/html_doc.png)