Applying Machine Learning Algorithms for Automatic Detection of Swallowing from Sound

Despite the severe consequences of dysfunctional swallowing, there is no simple method of monitoring swallowing outside of clinical settings. People who cannot swallow cannot eat safely, resulting in profound changes in quality of life and risk of death from aspiration pneumonia. A non-invasive swallowing detector may have widespread impact in both clinical care and research. Detection of swallowing from laryngeal sounds could become an ideal assessment tool because sounds are simple to record, quantifiable, and amenable to software analysis. The focus of this paper is to achieve high accuracy binary swallowing detection from sound recordings. A dataset with 2500 swallow sound samples and 1700 mixed laryngeal noise samples from 15 healthy adults was used to train and test three supervised machine learning algorithms. A decision tree, support vector machine (SVM), and neural network trained with the scaled conjugate gradient (SCG) method had areas under the receiver operating characteristic (ROC) curve of 0.970, 0.961, and 0.971 and average accuracies of 93.2 percent, 86.2 percent, and 93.7 percent respectively. While further work needs to be done to further optimize these algorithms and validate their efficacy, these initial results suggest machine learning strategies may be helpful to improve accuracy of swallowing detection.