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A RealTime Monitoring System for the Facial Nerve

Prell, Julian MD; Rachinger, Jens MD; Scheller, Christian MD; Alfieri, Alex MD; Strauss, Christian MD; Rampp, Stefan MD

doi: 10.1227/01.NEU.0000369605.79765.3E
Clinical Studies: Editor's Choice

OBJECTIVE: Damage to the facial nerve during surgery in the cerebellopontine angle is indicated by A-trains, a specific electromyogram pattern. These A-trains can be quantified by the parameter “traintime,” which is reliably correlated with postoperative functional outcome. The system presented was designed to monitor traintime in real-time.

METHODS: A dedicated hardware and software platform for automated continuous analysis of the intraoperative facial nerve electromyogram was specifically designed. The automatic detection of A-trains is performed by a software algorithm for real-time analysis of nonstationary biosignals. The system was evaluated in a series of 30 patients operated on for vestibular schwannoma.

RESULTS: A-trains can be detected and measured automatically by the described method for real-time analysis. Traintime is monitored continuously via a graphic display and is shown as an absolute numeric value during the operation. It is an expression of overall, cumulated length of A-trains in a given channel; a high correlation between traintime as measured by real-time analysis and functional outcome immediately after the operation (Spearman correlation coefficient [ρ] = 0.664, P < .001) and in long-term outcome (ρ = 0.631, P < .001) was observed.

CONCLUSION: Automated real-time analysis of the intraoperative facial nerve electromyogram is the first technique capable of reliable continuous real-time monitoring. It can critically contribute to the estimation of functional outcome during the course of the operative procedure.

BACKGROUND: Damage to the facial nerve during surgery in the cerebellopontine angle is indicated by A-trains, a specific electromyogram pattern. These A-trains can be quantified by the parameter “traintime,” which is reliably correlated with postoperative functional outcome. The system presented was designed to monitor traintime in real-time. METHODS: A dedicated hardware and software platform for automated continuous analysis of the intraoperative facial nerve electromyogram was designed. The automatic detection of A-trains is performed by a software algorithm for real-time analysis of nonstationary biosignals. The system was evaluated in a series of 30 patients operated on for vestibular schwannoma. RESULTS: A-trains can be detected and measured automatically by the described method for real-time analysis. Traintime is monitored continuously via a graphic display and an absolute numeric value. It is an expression of overall, cumulated length of A-trains in a given channel; a high correlation between traintime as measured by real-time analysis and functional outcome immediately after the operation (Spearman correlation coefficient [ρ] = 0.647, P < .001) and in long-term outcome (ρ = 0.631, P < .001) was observed. CONCLUSION: Automated real-time analysis of the intraoperative facial nerve electromyogram is the first technique capable of reliable continuous real-time monitoring, and critically contributes to the estimation of functional outcome during the operative procedure.

Department of Neurosurgery, University of Halle, Halle, Germany

Reprint requests: Julian Prell, MD, Department of Neurosurgery, University of Halle, Ernst-Grube-Str 40, 06097 Halle, Germany. E-mail: julian.prell@medizin.uni-halle.de

Received, February 3, 2009.

Accepted, December 5, 2009.

Copyright © by the Congress of Neurological Surgeons