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Article: Identifying Intraoperative Spinal Cord Injury Location from Somatosensory Evoked Potentials’ Time-Frequency Components

TitleIdentifying Intraoperative Spinal Cord Injury Location from Somatosensory Evoked Potentials’ Time-Frequency Components
Authors
Keywordsmachine learning
naive Bayes
somatosensory evoked potentials
spinal cord injury
time-frequency components
Issue Date11-Jun-2023
PublisherMDPI
Citation
Bioengineering, 2023, v. 10, n. 6 How to Cite?
Abstract

Excessive distraction in corrective spine surgery can lead to iatrogenic distraction spinal cord injury. Diagnosis of the location of the spinal cord injury helps in early removal of the injury source. The time-frequency components of the somatosensory evoked potential have been reported to provide information on the location of spinal cord injury, but most studies have focused on contusion injuries of the cervical spine. In this study, we established 19 rat models of distraction spinal cord injury at different levels and collected the somatosensory evoked potentials of the hindlimb and extracted their time-frequency components. Subsequently, we used k-medoid clustering and naive Bayes to classify spinal cord injury at the C5 and C6 level, as well as spinal cord injury at the cervical, thoracic, and lumbar spine, respectively. The results showed that there was a significant delay in the latency of the time-frequency components distributed between 15 and 30 ms and 50 and 150 Hz in all spinal cord injury groups. The overall classification accuracy was 88.28% and 84.87%. The results demonstrate that the k-medoid clustering and naive Bayes methods are capable of extracting the time-frequency component information depending on the spinal cord injury location and suggest that the somatosensory evoked potential has the potential to diagnose the location of a spinal cord injury.


Persistent Identifierhttp://hdl.handle.net/10722/332037
ISSN
2023 Impact Factor: 3.8
2023 SCImago Journal Rankings: 0.627
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Hanlei-
dc.contributor.authorGao, Songkun-
dc.contributor.authorLi, Rong-
dc.contributor.authorCui, Hongyan-
dc.contributor.authorHuang, Wei-
dc.contributor.authorHuang, Yongcan-
dc.contributor.authorHu, Yong-
dc.date.accessioned2023-09-28T05:00:25Z-
dc.date.available2023-09-28T05:00:25Z-
dc.date.issued2023-06-11-
dc.identifier.citationBioengineering, 2023, v. 10, n. 6-
dc.identifier.issn2306-5354-
dc.identifier.urihttp://hdl.handle.net/10722/332037-
dc.description.abstract<p>Excessive distraction in corrective spine surgery can lead to iatrogenic distraction spinal cord injury. Diagnosis of the location of the spinal cord injury helps in early removal of the injury source. The time-frequency components of the somatosensory evoked potential have been reported to provide information on the location of spinal cord injury, but most studies have focused on contusion injuries of the cervical spine. In this study, we established 19 rat models of distraction spinal cord injury at different levels and collected the somatosensory evoked potentials of the hindlimb and extracted their time-frequency components. Subsequently, we used k-medoid clustering and naive Bayes to classify spinal cord injury at the C5 and C6 level, as well as spinal cord injury at the cervical, thoracic, and lumbar spine, respectively. The results showed that there was a significant delay in the latency of the time-frequency components distributed between 15 and 30 ms and 50 and 150 Hz in all spinal cord injury groups. The overall classification accuracy was 88.28% and 84.87%. The results demonstrate that the k-medoid clustering and naive Bayes methods are capable of extracting the time-frequency component information depending on the spinal cord injury location and suggest that the somatosensory evoked potential has the potential to diagnose the location of a spinal cord injury.<br></p>-
dc.languageeng-
dc.publisherMDPI-
dc.relation.ispartofBioengineering-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectmachine learning-
dc.subjectnaive Bayes-
dc.subjectsomatosensory evoked potentials-
dc.subjectspinal cord injury-
dc.subjecttime-frequency components-
dc.titleIdentifying Intraoperative Spinal Cord Injury Location from Somatosensory Evoked Potentials’ Time-Frequency Components-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/bioengineering10060707-
dc.identifier.scopuseid_2-s2.0-85163751048-
dc.identifier.volume10-
dc.identifier.issue6-
dc.identifier.eissn2306-5354-
dc.identifier.isiWOS:001013911700001-
dc.identifier.issnl2306-5354-

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