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 [rho] = 0.664, P < .001) and in long-term outcome (rho = 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.
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 [rho] = 0.664, P < .001) and in long-term outcome (rho = 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.
Authors: Maura K Cosetti; Ming Xu; Andrew Rivera; Daniel Jethanamest; Maggie A Kuhn; Aleksandar Beric; John G Golfinos; J Thomas Roland Journal: J Neurol Surg B Skull Base Date: 2012-10
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Authors: Xuefan Zha; Leila Wehbe; Robert J Sclabassi; Zachary Mace; Ye V Liang; Alexander Yu; Jody Leonardo; Boyle C Cheng; Todd A Hillman; Douglas A Chen; Cameron N Riviere Journal: IEEE Trans Med Robot Bionics Date: 2020-12-30
Authors: Ismail Taha; Antti Hyvärinen; Antti Ranta; Olli-Pekka Kämäräinen; Jukka Huttunen; Esa Mervaala; Heikki Löppönen; Tuomas Rauramaa; Antti Ronkainen; Juha E Jääskeläinen; Arto Immonen; Nils Danner Journal: Acta Neurochir (Wien) Date: 2019-09-07 Impact factor: 2.216