Identifying Intraoperative Spinal Cord Injury Location from Somatosensory Evoked Potentials’ Time-Frequency Components
| Title: | Identifying Intraoperative Spinal Cord Injury Location from Somatosensory Evoked Potentials’ Time-Frequency Components |
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| Authors: | Hanlei Li, Songkun Gao, Rong Li, Hongyan Cui, Wei Huang, Yongcan Huang, Yong Hu |
| Source: | Bioengineering, Vol 10, Iss 6, p 707 (2023) |
| Publisher Information: | MDPI AG, 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Technology LCC:Biology (General) |
| Subject Terms: | machine learning, naive Bayes, somatosensory evoked potentials, spinal cord injury, time-frequency components, Technology, Biology (General), QH301-705.5 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 10060707 2306-5354 |
| Relation: | https://www.mdpi.com/2306-5354/10/6/707; https://doaj.org/toc/2306-5354 |
| DOI: | 10.3390/bioengineering10060707 |
| Access URL: | https://doaj.org/article/81068c2942994c8f98b0e949c121eb6c |
| Accession Number: | edsdoj.81068c2942994c8f98b0e949c121eb6c |
| Database: | Directory of Open Access Journals |
| ISSN: | 10060707 23065354 |
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| DOI: | 10.3390/bioengineering10060707 |
| Published in: | Bioengineering |
| Language: | English |