FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308331372> ?p ?o ?g. }

• W4308331372 endingPage "8455" @default.
• W4308331372 startingPage "8455" @default.
• W4308331372 abstract "In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesion from five Macaca fasicularis monkeys. The proposed classifier is based on a CNN using filtered segmented EMG signals from the pre- and post-lesion periods as inputs, while the kNN is designed using four hand-crafted EMG features. The results suggest that the CNN provides a promising classification technique for TSCI, compared to conventional machine learning classification. The kNN with hand-crafted EMG features classified the pre- and post-lesion EMG data with an F-measure of 89.7% and 92.7% for the left- and right-side muscles, respectively, while the CNN with the EMG segments classified the data with an F-measure of 89.8% and 96.9% for the left- and right-side muscles, respectively. Finally, the proposed deep learning classification model (CNN), with its learning ability of high-level features using EMG segments as inputs, shows high potential and promising results for use as a TSCI classification system. Future studies can confirm this finding by considering more subjects." @default.
• W4308331372 created "2022-11-11" @default.
• W4308331372 creator A5024203484 @default.
• W4308331372 creator A5025063236 @default.
• W4308331372 creator A5028373751 @default.
• W4308331372 creator A5055033436 @default.
• W4308331372 creator A5065768048 @default.
• W4308331372 creator A5067709931 @default.
• W4308331372 creator A5070338424 @default.
• W4308331372 creator A5083782493 @default.
• W4308331372 creator A5090169144 @default.
• W4308331372 date "2022-11-03" @default.
• W4308331372 modified "2023-10-06" @default.
• W4308331372 title "A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal" @default.
• W4308331372 cites W2055793546 @default.
• W4308331372 cites W2144354855 @default.
• W4308331372 cites W2144699108 @default.
• W4308331372 cites W2164330572 @default.
• W4308331372 cites W2337937866 @default.
• W4308331372 cites W2516710120 @default.
• W4308331372 cites W2555541061 @default.
• W4308331372 cites W2589462861 @default.
• W4308331372 cites W2589747988 @default.
• W4308331372 cites W2684229413 @default.
• W4308331372 cites W2792919287 @default.
• W4308331372 cites W2897722020 @default.
• W4308331372 cites W2903325146 @default.
• W4308331372 cites W2951146285 @default.
• W4308331372 cites W2962879438 @default.
• W4308331372 cites W2964630260 @default.
• W4308331372 cites W2978236883 @default.
• W4308331372 cites W2983726556 @default.
• W4308331372 cites W3006536296 @default.
• W4308331372 cites W3038293458 @default.
• W4308331372 doi "https://doi.org/10.3390/s22218455" @default.
• W4308331372 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36366153" @default.
• W4308331372 hasPublicationYear "2022" @default.
• W4308331372 type Work @default.
• W4308331372 citedByCount "1" @default.
• W4308331372 crossrefType "journal-article" @default.
• W4308331372 hasAuthorship W4308331372A5024203484 @default.
• W4308331372 hasAuthorship W4308331372A5025063236 @default.
• W4308331372 hasAuthorship W4308331372A5028373751 @default.
• W4308331372 hasAuthorship W4308331372A5055033436 @default.
• W4308331372 hasAuthorship W4308331372A5065768048 @default.
• W4308331372 hasAuthorship W4308331372A5067709931 @default.
• W4308331372 hasAuthorship W4308331372A5070338424 @default.
• W4308331372 hasAuthorship W4308331372A5083782493 @default.
• W4308331372 hasAuthorship W4308331372A5090169144 @default.
• W4308331372 hasBestOaLocation W43083313721 @default.
• W4308331372 hasConcept C108583219 @default.
• W4308331372 hasConcept C119857082 @default.
• W4308331372 hasConcept C12267149 @default.
• W4308331372 hasConcept C153180895 @default.
• W4308331372 hasConcept C154945302 @default.
• W4308331372 hasConcept C15744967 @default.
• W4308331372 hasConcept C169760540 @default.
• W4308331372 hasConcept C2777515770 @default.
• W4308331372 hasConcept C2778334475 @default.
• W4308331372 hasConcept C2780775167 @default.
• W4308331372 hasConcept C41008148 @default.
• W4308331372 hasConcept C50644808 @default.
• W4308331372 hasConcept C71924100 @default.
• W4308331372 hasConcept C81363708 @default.
• W4308331372 hasConcept C95623464 @default.
• W4308331372 hasConcept C99508421 @default.
• W4308331372 hasConceptScore W4308331372C108583219 @default.
• W4308331372 hasConceptScore W4308331372C119857082 @default.
• W4308331372 hasConceptScore W4308331372C12267149 @default.
• W4308331372 hasConceptScore W4308331372C153180895 @default.
• W4308331372 hasConceptScore W4308331372C154945302 @default.
• W4308331372 hasConceptScore W4308331372C15744967 @default.
• W4308331372 hasConceptScore W4308331372C169760540 @default.
• W4308331372 hasConceptScore W4308331372C2777515770 @default.
• W4308331372 hasConceptScore W4308331372C2778334475 @default.
• W4308331372 hasConceptScore W4308331372C2780775167 @default.
• W4308331372 hasConceptScore W4308331372C41008148 @default.
• W4308331372 hasConceptScore W4308331372C50644808 @default.
• W4308331372 hasConceptScore W4308331372C71924100 @default.
• W4308331372 hasConceptScore W4308331372C81363708 @default.
• W4308331372 hasConceptScore W4308331372C95623464 @default.
• W4308331372 hasConceptScore W4308331372C99508421 @default.
• W4308331372 hasFunder F4320332161 @default.
• W4308331372 hasIssue "21" @default.
• W4308331372 hasLocation W43083313721 @default.
• W4308331372 hasLocation W43083313722 @default.
• W4308331372 hasLocation W43083313723 @default.
• W4308331372 hasLocation W43083313724 @default.
• W4308331372 hasOpenAccess W4308331372 @default.
• W4308331372 hasPrimaryLocation W43083313721 @default.
• W4308331372 hasRelatedWork W2731899572 @default.
• W4308331372 hasRelatedWork W2996933976 @default.
• W4308331372 hasRelatedWork W2999805992 @default.
• W4308331372 hasRelatedWork W3116150086 @default.
• W4308331372 hasRelatedWork W3133861977 @default.
• W4308331372 hasRelatedWork W3208266890 @default.
• W4308331372 hasRelatedWork W4200173597 @default.
• W4308331372 hasRelatedWork W4312417841 @default.

• next