Classification of brain-stem trigeminal evoked potentials in multiple sclerosis, minor head injuries and post-concussion syndrome pathologies by similarity measurements.

In this study measurements obtained from brain-stem trigeminal evoked potentials (BTEP) are applied to the problem of diagnosing Multiple Sclerosis (MS) and Post-concussion syndrome (PCS). We present a simplistic model that depicts the BTEP waveform as the linear combination of a set of filters excited by a short stimulus. The relation between the BTEP latencies and the 1st to 4th harmonic components is shown. The performance of a fuzzy similarity measure based classifier is compared with that of human experts. The efficiency of the proposed classifier in conjunction with delay time and amplitude features is examined. Using this novel approach, a classification rate of 93.55% and 84.1% for MS and PCS pathologies, respectively, was achieved. This performance compares favorably to the classification rates of 84.28% for MS and 70.47% for PCS pathologies achieved by human experts.