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A New Quantitative Measure for Monitoring Somatosensory Evoked Potentials

Published online by Cambridge University Press:  12 November 2018

Richard J. Moulton ,
Stefan J. Konasiewicz  and
Paul O'Connor
Richard J. Moulton*
Affiliation:
The Divisions of Neurosurgery (R.J.M., S.J.K.) and Neurology (P.O.), St. Michael's Hospital, Toronto
Stefan J. Konasiewicz
Affiliation:
The Divisions of Neurosurgery (R.J.M., S.J.K.) and Neurology (P.O.), St. Michael's Hospital, Toronto
Paul O'Connor
Affiliation:
The Divisions of Neurosurgery (R.J.M., S.J.K.) and Neurology (P.O.), St. Michael's Hospital, Toronto
*
Reprint requests to: Dr. R. Moulton, 38 Shuter Street. Toronto. Ontario, Canada M5B 1A6
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Abstract

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This paper describes the development and testing of a computer algorithm to automate the process of peak identification and somatosensory evoked potential (SSEP) grading. We tested the accuracy of computerized peak detection and evaluated grading schemes using a test set of 60 SSEPs ranked from worst to best by the programmer (RJM) and a blinded grader (PO). The computer algorithm recognized 95% of peaks identified by visual inspection. Twelve percent of peaks identified by the computer were noise. Summed peak to peak amplitude gave the most accurate ranking of SSEPs. Rank correlation between computer and blinded and unblinded expert grading was r = .82 for PO, r = .92 for RJM, p < .0001 for both. Computer and manually summed amplitudes were highly correlated (Pearson 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 measure is objective, continuous, and well suited to statistical analysis.

Résumé

Résumé

Dans cet article, nous décrivons le développement et l'évaluation d'un algorithme informatique pour automatiser l'identification des pics et la classification des potentiels évoqués somesthésiques (PES). Nous avons étudié la précision de la détection informatisée des pics et la cote de classification au moyen de 60 PES classifiés du plus mauvais au meilleur par le programmeur (RJM) et un évaluateur travaillant en aveugle (PO). Valgorithme a reconnu 95% des pics identifiés par inspection visuelle. Douze pourcent des pics identifiés par l'ordinateur étaient des artefacts. La sommation de l'amplitude d'un pic à l'autre donnait l'ordre le plus précis des PES. La corrélation entre la classification de l'ordinateur et celle des experts, en aveugle ou non, était de r = .82 pour PO, r = .92 pour RJM, p < .0001 pour les deux. Les amplitudes calculées par ordinateur ou manuellement étaient hautement corrélées (Pearson r = .98, p < .0001). La corrélation entre les 2 évaluateurs experts était de .86, p < .0001. Les PES évalués par ordinateur étaient significativement reliés à l'issue clinique à 3 mois, p < .0001. L'évaluation automatique des PES au moyen du calcul de l'amplitude d'un pic à l'autre est hautement corrélée à l'évaluation par des experts. La mesure est objective, continue et se prete bien à l'analyse statistique.

Type
Research Article
Information
Canadian Journal of Neurological Sciences , Volume 21 , Issue S1: Neurocritical Care Symposium , February 1994 , pp. S17 - S22
Copyright
Copyright © The Canadian Journal of Neurological 1994

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