gms | German Medical Science

60. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
Joint Meeting mit den Benelux-Ländern und Bulgarien

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

24. - 27.05.2009, Münster

Real-time monitoring of the facial nerve during vestibular schwannoma surgery

Meeting Abstract

  • J. Prell - Neurochirurgische Klinik, Martin-Luther-Universität Halle-Wittenberg
  • S. Rampp - Neurochirurgische Klinik, Martin-Luther-Universität Halle-Wittenberg
  • J. Rachinger - Neurochirurgische Klinik, Martin-Luther-Universität Halle-Wittenberg
  • C. Scheller - Neurochirurgische Klinik, Martin-Luther-Universität Halle-Wittenberg
  • C. Strauss - Neurochirurgische Klinik, Martin-Luther-Universität Halle-Wittenberg

Deutsche Gesellschaft für Neurochirurgie. 60. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit den Benelux-Ländern und Bulgarien. Münster, 24.-27.05.2009. Düsseldorf: German Medical Science GMS Publishing House; 2009. DocP10-07

doi: 10.3205/09dgnc359, urn:nbn:de:0183-09dgnc3592

Veröffentlicht: 20. Mai 2009

© 2009 Prell et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen ( Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.



Objective: One of the main problems of intraoperative neurophysiologic monitoring is the gap between the incident causing damage to neural tissue and its detection. Closure of this gap by real-time neuromonitoring is a desired goal. Damage to the facial nerve during surgery in the cerebello-pontine angle leads to A-trains, a specific EMG-pattern. These A-trains can be quantified by the parameter “train time”, which is closely correlated with postoperative functional outcome. The system presented was designed to monitor train time in real-time.

Methods: A dedicated hard- and software-platform for automated continuous analysis of the intraoperative facial nerve EMG was specifically designed. Automatic detection of A-trains is performed by a software algorithm for real-time analysis of non-stationary biosignals. It is based on morphology, frequency and rhythmic characteristics of EMG-patterns. A-trains were quantified by the parameter “train time” for three channels of facial nerve EMG.

Results: A-trains can be detected and measured automatically by the described method for real-time analysis. Real-time monitoring is performed for the three main branches of the facial nerve. Train time is monitored continuously via a graphic display and also shown as an absolute numerical value during the operation. It is an expression of overall, cumulated length of A-trains in a given channel; high correlation between train time as measured by real-time analysis and functional outcome immediately after the operation was observed (Spearman’s Rho correlation coefficient 0.647, p<0.001).

Conclusions: Automated real-time-analysis of the intraoperative facial nerve EMG is the first technique capable of reliable continuous real-time monitoring. It can critically contribute to the estimation of functional outcome in the course of the operative procedure, and it provides instantaneous information about the damage being done to the facial nerve. This option for reliable prognostic statements at any time during the operation can improve functional outcome in vestibular schwannoma surgery.