LAUSR

Studies using time-frequency analysis have reported that somatosensory evoked potentials provide information regarding the location of spinal cord injury. However, a better understanding of the time-frequency components derived from somatosensory evoked potentials is essential for developing more reliable algorithms that can diagnosis level (location) of cervical injury. In the present study, we proposed a random forests machine learning approach, for separating somatosensory evoked potentials depending on spinal cord state. For data acquisition, we established rat models of compression spinal cord injury at the C4, C5, and C6 levels to induce cervical myelopathy. After making the compression injury, we collected somatosensory evoked potentials and extracted their time-frequency components. We then used the random forests classification system to analyze the evoked potential dataset that was obtained from the three groups of model rats. Evaluation of the classifier performance revealed an overall classification accuracy of 84.72%, confirming that the random forests method was able to separate the time-frequency components of somatosensory evoked potentials from rats under different conditions. Features of the time-frequency components contained information that could identify the location of the cervical spinal cord injury, demonstrating the potential benefits of using time-frequency components of somatosensory evoked potentials to diagnose the level of cervical injury in cervical myelopathy.