IEEE J Transl Eng Health Med. 2015 Nov 25;3:2900310. doi: 10.1109/JTEHM.2015.2500562. eCollection 2015.
Dysphagia can cause serious challenges to both physical and mental health. Aspiration due to dysphagia is a major health risk that could cause pneumonia and even death. The videofluoroscopic swallow study (VFSS), which is considered the gold standard for the diagnosis of dysphagia, is not widely available, expensive and causes exposure to radiation. The screening tests used for dysphagia need to be carried out by trained staff, and the evaluations are usually non-quantifiable. This paper investigates the development of the Swallowscope, a smartphone-based device and a feasible real-time swallowing sound-processing algorithm for the automatic screening, quantitative evaluation, and the visualisation of swallowing ability. The device can be used during activities of daily life with minimal intervention, making it potentially more capable of capturing aspirations and risky swallow patterns through the continuous monitoring. It also consists of a cloud-based system for the server-side analyzing and automatic sharing of the swallowing sound. The real-time algorithm we developed for the detection of dry and water swallows is based on a template matching approach. We analyzed the wavelet transformation-based spectral characteristics and the temporal characteristics of simultaneous synchronised VFSS and swallowing sound recordings of 25% barium mixed 3-ml water swallows of 70 subjects and the dry or saliva swallowing sound of 15 healthy subjects to establish the parameters of the template. With this algorithm, we achieved an overall detection accuracy of 79.3% (standard error: 4.2%) for the 92 water swallows; and a precision of 83.7% (range: 66.6%-100%) and a recall of 93.9% (range: 72.7%-100%) for the 71 episodes of dry swallows.
吞咽困难会对身心健康造成严重影响。因吞咽困难导致的误吸是一个主要的健康风险，可能导致肺炎，甚至死亡。视频荧光透视吞咽检查（VFSS）被认为是吞咽困难诊断的金标准，但该检查并不普及，费用昂贵且存在辐射暴露风险。用于吞咽困难筛查的检查需要经过培训的专业人员进行操作，评估通常是非量化的。本文研究了 Swallowscope 的开发，这是一种基于智能手机的设备和一种可行的实时吞咽声音处理算法，用于自动筛查、定量评估和可视化吞咽能力。该设备可以在日常生活活动中进行最小干预使用，通过持续监测，有可能更能捕捉到误吸和有风险的吞咽模式。它还包括一个基于云的服务器端分析系统，用于自动共享吞咽声音。我们为检测干吞咽和水吞咽开发的实时算法基于模板匹配方法。我们分析了 70 名受试者的 25%钡混合 3 毫升水吞咽和 15 名健康受试者的干吞咽或唾液吞咽声音的同步 VFSS 和吞咽声音记录的基于小波变换的光谱特征和时间特征，以确定模板参数。使用该算法，我们实现了 92 次水吞咽的总体检测准确率为 79.3%（标准误差：4.2%）；71 次干吞咽的精确率为 83.7%（范围：66.6%-100%），召回率为 93.9%（范围：72.7%-100%）。
