Hadley Aaron J, Krival Kate R, Ridgel Angela L, Hahn Elizabeth C, Tyler Dustin J
Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Room 309 Wickenden Building, Cleveland, OH, 44106, USA,
Dysphagia. 2015 Apr;30(2):176-87. doi: 10.1007/s00455-014-9593-y. Epub 2015 Jan 25.
We describe a novel device and method for real-time measurement of lingual-palatal pressure and automatic identification of the oral transfer phase of deglutition. Clinical measurement of the oral transport phase of swallowing is a complicated process requiring either placement of obstructive sensors or sitting within a fluoroscope or articulograph for recording. Existing detection algorithms distinguish oral events with EMG, sound, and pressure signals from the head and neck, but are imprecise and frequently result in false detection. We placed seven pressure sensors on a molded mouthpiece fitting over the upper teeth and hard palate and recorded pressure during a variety of swallow and non-swallow activities. Pressure measures and swallow times from 12 healthy and 7 Parkinson's subjects provided training data for a time-delay artificial neural network to categorize the recordings as swallow or non-swallow events. User-specific neural networks properly categorized 96 % of swallow and non-swallow events, while a generalized population-trained network was able to properly categorize 93 % of swallow and non-swallow events across all recordings. Lingual-palatal pressure signals are sufficient to selectively and specifically recognize the initiation of swallowing in healthy and dysphagic patients.
我们描述了一种用于实时测量舌腭压力和自动识别吞咽口腔转移阶段的新型设备和方法。吞咽口腔运输阶段的临床测量是一个复杂的过程，需要放置阻塞性传感器或坐在荧光镜或关节造影仪内进行记录。现有的检测算法通过来自头部和颈部的肌电图、声音和压力信号来区分口腔事件，但不准确且经常导致误检测。我们在一个覆盖上牙和硬腭的模制口器上放置了七个压力传感器，并在各种吞咽和非吞咽活动期间记录压力。来自12名健康受试者和7名帕金森病受试者的压力测量值和吞咽时间为一个延时人工神经网络提供了训练数据，以便将记录分类为吞咽或非吞咽事件。针对特定用户的神经网络正确分类了96%的吞咽和非吞咽事件，而一个经过一般人群训练的网络能够在所有记录中正确分类93%的吞咽和非吞咽事件。舌腭压力信号足以选择性且特异性地识别健康患者和吞咽困难患者吞咽的起始。
