Intraoperative monitoring of the facial nerve by automated categorisation of EMG-activity

在前庭神经鞘瘤的手术过程中，可以通过术中面部肌肉的肌电图来监测面神经。用这种方法，特定的模式（a -列）表示对面神经的潜在损害。观察到的A-train活性的数量与临床术后损伤程度之间的定量相关性已经得到证实。然而，假阴性（很少a序列，术后显著缺陷）和假阳性结果（许多a序列，很少临床缺陷）已被证明会损害该方法的可靠性。在我们的第一个应用（PR1275/1-1）的背景下，面神经肌电图的自动分析方法已经开发和评估。我们的主要目标是确定假阳性和假阴性结果的来源。这个目标实现了。根据我们的假设，假阴性结果被证明是基于太小的样本和太少的电极使用量。此外，我们能够在一系列发表在高级期刊上的论文中证明，术中操作中间神经可能导致额外的病理性a序列，而没有术后临床相关性；因此，中间神经的活动在面神经监测中起到干扰信号的作用。这一后续应用是基于上述发现。其目标是开发和评估技术方法，可以区分真正的面神经a -列和术中中间神经操作引起的a -列。为此，将系统地比较各自的生物信号，以找到不同的信号特征。在这种情况下，我们将进一步遵循术中对某一运动单元的操作将导致单态和特异性肌电信号的假设，因为这意味着在一定时间后，特定模式可能失去意义，因为相应的单元不能再被破坏。此外，肌电图分析将直接与其他两种可用的面神经监测方法进行比较：直接电刺激和面神经运动诱发电位。这两种方法也存在监测结果假阳性的缺陷；然而，根据我们的假设，这些结果不应该基于中间神经活动。因此，一种互补的方法可能会在这方面产生新的发现。最后，对手术后面神经功能状态记录的技术方法进行了评估。这种方法是基于3d摄影，应该允许客观的评估，而不是通过轻瘫等级进行临床评估的常见方法。我们发现由中间神经引起的a -列倾向于局限于面部肌肉的特定部位，可以用这种方法进一步评估。

During surgery for vestibular schwannoma, the facial nerve can be monitored by intraoperative evaluation of the EMG from the facial muscles. With this method, particular patterns (A-trains) signify potential damage to the facial nerve. A quantitative correlation between the amount of observed A-trains activity and the degree of clinical postoperative impairment has been demonstrated. However, false negative (very few A-trains, significant postoperative deficits) and false positive results (many A-trains, few clinical deficits) have been shown to impair the reliability of the method. In the context of our first application (PR1275/1-1), methods for automated analysis of the facial nerve EMG have been developed and evaluated. It was our main goal to identify the sources of false positive and false negative results. This goal was accomplished. In accord with our assumptions, false negative findings were shown to be based on too small a sample with too small an amount of electrodes utilized. In addition, we were able to demonstrate in a consecutive series of papers published in highly ranking journals that intraoperative manipulation of the Intermedius Nerve may lead to additional pathological A-trains without a postoperative clinical correlate; thus, this intermedius nerve activity acts as an interfering signal in the context of facial nerve monitoring. This follow-up application is based on the findings as outlined above. Its goal is to develop and evaluate methods which may technically differentiate between real facial nerve A-trains and A-trains elicited by intermedius nerve manipulation in an intraoperative setting. To do so, the respective biosignals will be compared systematically in order to find divergent signal characteristics. In this context, the assumption that intraoperative manipulation of a given motor unit will lead to monomorphic and specific EMG-signals will be followed further, as this would mean that after a certain time, the specific pattern may lose significance, because the corresponding unit cannot be damaged any further. In addition, EMG-analysis will be directly compared with the two other available methods for facial nerve monitoring: Direct electrical stimulation and facial nerve motor evoked potentials. Both methods are also flawed with false positive monitoring results; however, these results should not be based on intermedius nerve activity according to our assumptions. Thus, a complementary approach may lead to new findings in this context. Finally, a technical method for documentation of the facial nerve functional status after surgery is to be evaluated. This method is based on 3D-fotography and supposed to allow for an objective evaluation in contrast to the common method of clinical assessment by paresis grades. Our finding that A-trains elicited by the intermedius nerve tend to be limited to specific parts of the facial muscles can be evaluated further with this method.