Facial Nerve EMG: Low-Tech Monitoring with a Stopwatch
Facial Nerve EMG: Low-Tech Monitoring with a Stopwatch
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DOI：
10.1055/s-0040-1701616
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发表时间：
2021-01-08
影响因子：
1
通讯作者：
Rampp, Stefan
中科院分区：
文献类型：
--
作者：
Prell, Julian;Scheller, Christian;Rampp, Stefan
目的 A 列是自由运行面神经肌电图的高频模式，其数量与术后重度面神经麻痹的风险相关。这种相关性已通过专用算法的自动分析和视觉离线分析（但未通过视听实时分析）得到证实。方法 向研究者提供 29 个完整的数据集，这些数据集是在实际手术期间实时测量的，并且按随机顺序不间断。数据要么严格通过扬声器（音频）呈现，要么同时通过扬声器和计算机屏幕（视听）呈现。然后，研究人员使用计算机化的秒表对可见和/或可听的 A 列活动进行量化。还通过自动化算法对 A 列进行量化来分析相同的数据。结果 自动 (auto) 训练时间 (TT) 是整个 A 训练活动中一小部分，但具有高度代表性，范围为 0.01 到 10.86 秒（中位数：0.58 秒）。相比之下，音频 TT 的范围为 0 到 1,357.44 秒（中位数：29.69 秒），视听 TT 的范围为 0 到 786.57 秒（中位数：46.19 秒）。所有三种模式都以高度显着的方式相互关联。同样，所有三种方式都与术后面瘫的程度显着相关。根据经验，患者可看到/听到 A-train 活动 1 分钟的 A-train 活动。结论 A-train 的检测甚至量化在技术上是可能的，不仅可以通过术中自动实时计算或术后视觉离线分析，而且还可以通过非常基本的监控设备和实时高质量的视听分析。然而，调查人员发现视听实时分析的要求非常高；因此，自动量化工具在这方面非常有帮助。
Objective The 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. Methods An 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. Results Automated (auto) traintime (TT), known to be a small, yet highly representative fraction of overall A-train activity, ranged from 0.01 to 10.86s (median: 0.58s). In contrast, audio-TT ranged from 0 to 1,357.44s (median: 29.69s), and audiovisual-TT ranged from 0 to 786.57s (median: 46.19s). 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 activity1minute of A-train activity. Conclusion Detection 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.