Using Machine Learning and a Combination of Respiratory Flow, Laryngeal Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing
Using Machine Learning and a Combination of Respiratory Flow, Laryngeal Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing
复制标题
DOI：
10.1109/tbme.2018.2807487
复制
发表时间：
2018-11-01
影响因子：
4.6
通讯作者：
Oku, Yoshitaka
中科院分区：
文献类型：
--
作者：
Inoue, Katsufumi;Yoshioka, Michifumi;Oku, Yoshitaka
目的：本研究的目的是开发一种吞咽评估方法来帮助预防吸入性肺炎。该方法使用简单的传感器来监测个人日常生活中的吞咽功能。方法：我们提出的方法的主要特点如下。先我们通过使用呼吸流量、喉部运动和由简单传感器记录的吞咽声音信号来评估吞咽功能。其次，我们对记录的信号是否对应于健康受试者或吞咽困难患者进行分类。最后，我们使用特征提取方法（线性预测编码）和机器学习方法（支持向量机）来分析记录的信号。结果如下：基于我们对140名健康受试者（54.5 ± 32.5岁）和52名吞咽困难患者（75.5 ± 20.5岁）的实验结果，我们提出的方法可以达到82.4%的灵敏度和86.0%的特异性。结论：虽然20%的测试样本集被错误地分类，我们得出结论，我们提出的方法可能有助于筛选检查吞咽功能。意义：结合便携式传感器，我们提出的方法是值得利用的无创吞咽评估。
Objective: The aim of this research was to develop a swallowing assessment method to help prevent aspiration pneumonia. The method uses simple sensors to monitor swallowing function during an individual's daily fife. Methods: The key characteristics of our proposed method are as follows. First. we assess swallowing function by using respiratory flow, laryngeal motion, and swallowing sound signals recorded by simple sensors. Second, we classify whether the recorded signals correspond to healthy subjects or patients with dysphagia. Finally, we analyze the recorded signals using both a feature extraction method (linear predictive coding) and a machine learning method (support vector machine). Results: Based on our experimental results for 140 healthy subjects (54.5 +/- 32.5 years old) and 52 patients with dysphagia (75.5 + 20.5 years old), our proposed method could achieve 82.4% sensitivity and 86.0% specificity. Conclusion: Although 20% of testing sample sets were erroneously classified, we conclude that our proposed method may facilitate screening examinations of swallowing function. Significance: In combination with the portable sensors, our proposed method is worth utilizing for noninvasive swallowing assessment.