A Real-Time Monitoring System for the Facial Nerve

Abstract

In Brief 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.