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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

References

1. Rappaport, M, Hopkins, HK, Hall, K, Belleza, T. Evoked potentials and head injury. 2. Clinical applications. Clin Electroencephalogr 1981; 12: 167–176.Google Scholar
2. De La Torre, JC, Trimble, JL, Beard, RT, Hanlon, K, Surgeon, JW. Somatosensory evoked potentials for the prognosis of coma in humans. Exp Neurol 1978; 60: 304–317.Google Scholar
3. Lindsay, KW, Carlin, J, Kennedy, I, et al. Evoked potentials in severe head injury -analysis and relation to outcome. J Neurol Neurosurg Psychiatry 1981; 44: 796–802.Google Scholar
4. Moulton, R, Kresta, P, Ramirez, M, Tucker, W. Continuous automated monitoring of somatosensory evoked potentials in posttraumatic coma. J Trauma 1991; 31: 676–685.Google Scholar
5. Newlon, PG, Greenberg, RP, Hyatt, MS, Enas, G, Becker, DP. The dynamics of neuronal dysfunction and recovery following severe head injury assessed with serial multimodality evoked potentials. J Neurosurg 1982; 57: 168–177.Google Scholar
6. Bertrand, O, Bohorquez, J, Pernier, J. Technical requirements for evoked potential monitoring in the Intensive Care Unit. In: Rossini, PM, Maugiere, F, eds. New Trends and Advanced Techniques in Clinical Neurophysiology (EEG Suppl. 41). Elsevier Science Publishers, 1990: 51–70.Google Scholar
7. Lindsay, KW, Pasaoglu, A, Hirst, D, et al. Somatosensory and auditory brain stem conduction after head injury: a comparison with clinical features in prediction of outcome. Neurosurgery 1990; 26: 278–285.Google Scholar
8. Hume, A, Cant, BR. Central somatosensory conduction time after head injury. Ann Neurol 1981; 10: 411–419.Google Scholar
9. Greenberg, RP, Mayer, DJ, Becker, DP, Miller, JD. Evaluation of brain function in severe human head trauma with multimodality evoked potentials. Part I: Evoked brain-injury potentials, methods, and analysis. J Neurosurg 1977; 47: 150–162.Google Scholar
10. Jennett, B, Bond, M. Assessment of outcome after severe brain damage: a practical scale. Lancet 1975; 1: 480–484.Google Scholar
11. Bertrand, O, Garcia-Larria, L, Artu, F, Maugiere, F, Pernier, J. Brainstem monitoring. I. A system for high-rate sequential BAEP recording and feature extraction. Electroencephalogr Clin Neurophysiol 1987; 68: 433–445.Google Scholar
12. Pool, KD, Finitzo, T. Evaluation of a computer-automated program for clinical assessment of the auditory brainstem response. Ear Hear 1989; 10:304–310.Google Scholar
13. Rappaport, M, Hall, K, Hopkins, K, and Belleza, T. Evoked potentials and Head Injury. I. Rating of Evoked Potential Abnormality. Clin Electroencephalogr 1981; 12: 154–166.Google Scholar
14. Newlon, PG. Utility of multimodality evoked potentials in cerebral injury. In: Aminoff, MJ, ed. Neurologic Clinics. (Vol 3. No. 3). Philadelphia: W. B. Saunders, 1985: 675–686.Google Scholar
15. Madl, C, Grimm, G, Kramer, L, et al. Early prediction of individual outcome after cardiopulmonary resuscitation. Lancet 1994; 341: 855–858.Google Scholar
16. Yamada, T. The anatomic and physiologic bases of median nerve somatosensory evoked potentials. In: Gilmore, R, ed. Neurologic Clinics (Vol. 6 No. 4). Philadelphia: W. B. Saunders, 1988: 705–733.Google Scholar