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

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.

Electrophysiology Methods for Assessing of Neurodegenerative and Post-Traumatic Processes as Applied to Translational Research

“… Comparison of somatosensory evoked potentials of EMG between humans and model animals. Spinal cord injury: Human [ 67 ], Rodents [ 68 ], Primates [ 69 ]; Parkinson’s disease: Human [ 70 ], Rodents [ 71 ], Primates [ 72 ]; Normal Human [ 73 ], Rodents [ 68 ], Primates [ 74 ]. …”

“…Nevertheless, a more rapid recovery of indices to the level of norm is observed more often in animals. [71], Primates [72]; Normal Human [73], Rodents [68], Primates [74]. Rodents [71], Primates [72]; Normal Human [73], Rodents [68], Primates [74].…”

“…[71], Primates [72]; Normal Human [73], Rodents [68], Primates [74]. Rodents [71], Primates [72]; Normal Human [73], Rodents [68], Primates [74].…”

Biomedical Data Mining and Machine Learning for Disease Diagnosis and Health Informatics

“…These advanced technologies can assist in the precise monitoring and prediction of the clinical features of long COVID and effectively discover key clinical factors, laying a solid foundation for the development of scientifically rigorous and multidisciplinary integration of treatment plans. By integrating innovative biomedical data mining and machine learning technologies, smart healthcare systems can provide more accurate and efficient diagnostic solutions [7] and treatment opportunities [8], aiming towards the purpose of significantly improving overall healthcare quality and rehabilitation outcomes [9]. In this Special Issue, we strive to highlight the recent development of biomedical data mining and machine learning technologies for the diagnosis of infectious diseases and chronic diseases; the topics also cover the theoretical advances and practical ap-plications of deep learning neural network architectures for physiological signal measurement and data analysis [10].…”