Using Machine Learning and a Combination of Respiratory Flow, Laryngeal Motion, and Swallowing Sounds to Classify Safe and Unsafe Swallowing.

Abstract

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 life. 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). 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. Although 20% of testing sample sets were erroneously classified, we conclude that our proposed method may facilitate screening examinations of swallowing function. In combination with the portable sensors, our proposed method is worth utilizing for noninvasive swallowing assessment.