A NEW QUANTITATIVE MEASURE FOR MONITORING SOMATOSENSORY-EVOKED POTENTIALS

This paper describes the development and testing of a computer algorit hm to automate the process of peak identification and somatosensory ev oked potential (SSEP) grading. We tested the accuracy of computerized peak detection and evaluated grading schemes using a test set of 60 SS EPs ranked from worst to best by the programmer (RJM) and a blinded gr ader (PO). The computer algorithm recognized 95% of peaks identified b y visual inspection. Twelve percent of peaks identified by the compute r were noise. Summed peak to peak amplitude gave the most accurate ran king of SSEPs. Rank correlation between computer and blinded and unbli nded expert grading was r=.82 for PO, r=.92 for RJM, p<.0001 for both. Computer and manually summed amplitudes were highly correlated (Pears on r=.98, p<.0001). Correlation between the 2 expert graders was .86, p<.0001. Computer graded SSEPs were significantly related to clinical outcome at 3 months, p<.0001. Automatic grading of SSEPs using summed peak to peak amplitude is highly correlated with expert grading. The m easure is objective, continuous, and well suited to statistical analys is.