Increasing the diagnostic value of evoked potentials in multiple sclerosis by quantitative topographic analysis of multichannel recordings

J Clin Neurophysiol. 2009 Oct;26(5):316-25. doi: 10.1097/WNP.0b013e3181baac00.

Abstract

This study presents a method to record and analyze multichannel visual-evoked potential (VEP) and somatosensory-evoked potential (SEP) in an objective, automatic, and quantitative manner. The intention of this study was to assess their diagnostic value in multiple sclerosis (MS). A 256-channel VEP and SEP were recorded in 44 healthy subjects, 26 patients with MS, and 20 patients with other neurologic diseases. Topographic pattern recognition methods were applied and a normative database was established. Z-score statistics allowed identifying the number of subjects with significant abnormal values in each group. These values were compared with conventional single-channel waveform analysis. The diagnostic value of the new measures for MS reached a sensitivity of 72% and a specificity of 100% for the VEP, which was significantly higher than the conventional analysis. For the SEP, the specificity was also high (93%) but the sensitivity remained low as in the conventional analysis (30%). The quantitative topographic analysis of multichannel VEP revealed high-diagnostic sensitivity and specificity for MS. Moreover, the method reliably identified the most dominant VEP and SEP components in the healthy subject group. The results indicate that objective topographic analysis of multichannel recordings increase the value of VEP as surrogate marker for MS.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Brain / physiopathology*
  • Databases as Topic
  • Electroencephalography
  • Evoked Potentials, Somatosensory*
  • Evoked Potentials, Visual*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Multiple Sclerosis / diagnosis*
  • Multiple Sclerosis / physiopathology*
  • Reference Standards
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted
  • Young Adult