Facial Nerve EMG: Low-Tech Monitoring with a Stopwatch.

Abstract

OBJECTIVE\n\u2003The quantity of A-trains, a high-frequency pattern of free-running facial nerve electromyography, is correlated with the risk for postoperative high-grade facial nerve paresis. This correlation has been confirmed by automated analysis with dedicated algorithms and by visual offline analysis but not by audiovisual real-time analysis.\n\n\nMETHODS\n\u2003An investigator was presented with 29 complete data sets measured during actual surgeries in real time and without breaks in a random order. Data were presented either strictly via loudspeaker (audio) or simultaneously by loudspeaker and computer screen (audiovisual). Visible and/or audible A-train activity was then quantified by the investigator with the computerized equivalent of a stopwatch. The same data were also analyzed with quantification of A-trains by automated algorithms.\n\n\nRESULTS\n\u2003Automated (auto) traintime (TT), known to be a small, yet highly representative fraction of overall A-train activity, ranged from 0.01 to 10.86\u2009s (median: 0.58\u2009s). In contrast, audio-TT ranged from 0 to 1,357.44\u2009s (median: 29.69\u2009s), and audiovisual-TT ranged from 0 to 786.57\u2009s (median: 46.19\u2009s). All three modalities were correlated to each other in a highly significant way. Likewise, all three modalities correlated significantly with the extent of postoperative facial paresis. As a rule of thumb, patients with visible/audible A-train activity\u2009<\u20091\u2009minute presented with a more favorable clinical outcome than patients with\u2009>\u20091\u2009minute of A-train activity.\n\n\nCONCLUSION\n\u2003Detection and even quantification of A-trains is technically possible not only with intraoperative automated real-time calculation or postoperative visual offline analysis, but also with very basic monitoring equipment and real-time good quality audiovisual analysis. However, the investigator found audiovisual real-time-analysis to be very demanding; thus tools for automated quantification can be very helpful in this respect.