Perilaryngeal-Cranial Functional Muscle Network Differentiates Vocal Tasks: A Multi-Channel sEMG Approach
Perilaryngeal-Cranial Functional Muscle Network Differentiates Vocal Tasks: A Multi-Channel sEMG Approach
复制标题
喉周颅功能肌肉网络区分声音任务：多通道 sEMG 方法
DOI：
10.1109/tbme.2022.3175948
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发表时间：
2022
影响因子：
4.6
通讯作者：
Atashzar, S. Farokh
中科院分区：
文献类型：
--
作者：
O' Keeffe, Rory;Shirazi, Seyed Yahya;Mehrdad, Sarmad;Crosby, Tyler;Johnson, Aaron M.;Atashzar, S. Farokh
目的用非侵入性方法评价发声功能和发声障碍的生理反应已引起人们的极大兴趣。这篇论文的目的是第一次实现和评估咽周围-颅骨功能肌肉网络。本研究探讨了神经网络的地形图特征及其对发声任务的识别能力的差异。方法从6个咽周和颅肌采集了12个双侧表面肌电(SEMG)信号。数据收集自8名受试者(4名女性)，他们没有已知的嗓音障碍病史。所提出的肌肉网络是由表面肌电信号记录之间的成对一致性组成的。结果不同发音任务的发音任务均表现出中位度，肌肉网络的WCC呈单调上升趋势，效应值()较高。使用度数和WCC()可以显著区分音调滑动、唱歌和说话任务。在所有任务中，俯仰滑动度和WCC(度、WCC)最高。与之相比，经典的频域测量方法在区分发声任务方面的有效性(Max)要低得多。研究表明，功能肌肉网络可以很好地区分发声任务，而经典的肌肉激活评估无法区分。意义首次展示了咽周-颅肌网络作为声乐表演的神经生理学窗口的力量。此外，这项研究还发现了网络参与度最高的任务，这可能在未来被用来监测语音障碍和康复。
ObjectiveObjective evaluation of physiological responses using non-invasive methods for the assessment of vocal performance and voice disorders has attracted great interest. This paper, for the first time, aims to implement and evaluate perilaryngeal-cranial functional muscle networks. The study investigates the variations in topographical characteristics of the network and the corresponding ability to differentiate vocal tasks.MethodTwelve surface electromyography (sEMG) signals were collected bilaterally from six perilaryngeal and cranial muscles. Data were collected from eight subjects (four females) without a known history of voice disorders. The proposed muscle network is composed of pairwise coherence between sEMG recordings. The network metrics include (a) network degree and (b) weighted clustering coefficient (WCC).ResultsThe varied phonation tasks showed the median degree, and WCC of the muscle network ascend monotonically, with a high effect size (). Pitch glide, singing, and speech tasks were significantly distinguishable using degree and WCC (). Also, pitch glide had the highest degree and WCC among all tasks (degree, WCC). In comparison, classic spectrotemporal measures showed far less effectiveness (max) in differentiating the vocal tasks.ConclusionPerilaryngeal-cranial functional muscle network was proposed in this paper. The study showed that the functional muscle network could robustly differentiate the vocal tasks while the classic assessment of muscle activation fails to differentiate.SignificanceFor the first time, we demonstrate the power of a perilaryngeal-cranial muscle network as a neurophysiological window to vocal performance. In addition, the study also discovers tasks with the highest network involvement, which may be utilized in the future to monitor voice disorders and rehabilitation.