gms | German Medical Science

Introduction: Functional preservation is of critical importance in any surgical procedure performed in close vicinity of nerve structures. Neurophysiologic monitoring has been introduced in order to provide for functional surveillance during surgery, but it has always suffered from a certain time delay needed for analysis of the aquired data. Thus, real-time neuromonitoring is a strongly desired goal. The EMG-parameter “traintime” is known to be closely correlated with the extent of postoperative facial nerve paresis in surgery of the cerebellopontine angle. It is a quantitative expression of overall A-train activity, an EMG-pattern known to be pathognomonic for postoperative paresis. The system presented was designed to monitor traintime in real-time.

Material and methods: A dedicated hard- and software-platform for automated analysis of the intraoperative facial nerve EMG was specifically designed. It is capable of monitoring 16 EMG-channels simultaneously. The automatic detection of A-trains is performed by a software-algorithm which incorporates several new techniques for real-time analysis of non-stationary biosignals. It is based on morphology, frequency and rhythmic characteristics of electrophysiological EMG-patterns. The system was tested in a prospective study, monitoring 30 patients operated upon vestibular schwannoma. The results of automated realtime-monitoring were correlated with functional outcome and the results of visual EMG-analysis of the same data performed by experienced monitoring personnel.

Results: Intraoperative A-trains can be detected and measured automatically by the described method for real-time analysis. With the occurrence of A-trains, an acoustic warning-signal is emitted. Traintime is calculated automatically for the estimation of overall A-train „power“. It is monitored continuously and in real-time via a “traffic lights” display and also shown as an absolute numerical value during the operation. The system correctly foresees disfiguring facial nerve palsy with high reliability (sensitivity 100%, specificity 67%, p < 0.0002 in the exact Fisher test). A Spearman’s Rho correlation coefficient of cumulated traintime with postoperative HB grade of 0.647 was calculated with a two-sided significance of <0.001 for the 30 patients. Automated monitoring obtained superior results as compared to visual offline-analysis, for which a Spearman’s Rho correlation coefficient of 0.432 (p = 0.017) was calculated. The results of visual and automated analysis proved to be closely correlated as well. Spearman’s Rho for overall automated traintime in relation to overall visual traintime was 0.772 (p < 0.001).

Conclusion: Automated real-time-analysis of the intraoperative facial nerve EMG can critically contribute to the estimation of functional outcome in the course of the operative procedure. The technique is fast, reliable and qualitatively superior to visual analysis. It allows for prognostic statements at any time during the operation and can thus help in achieving better functional outcomes in acoustic neuroma surgery. Potentially, this monitoring technique might prove to be valuable in any other kind of surgery taking place in close proximity to motor nerves. In order to evaluate this potential, further studies are required.