Applying Machine Learning Algorithms for Automatic Detection of Swallowing from Sound

UMMS Affiliation

School of Medicine; Senior Scholars Program

Faculty Mentor

David Paydarfar

Publication Date


Document Type

Conference Proceeding


Artificial Intelligence and Robotics | Biomedical Engineering and Bioengineering | Diagnosis | Digestive System Diseases | Medical Education | Otorhinolaryngologic Diseases


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.


dysphagia, swallowing detection, swallowing sound, signal analysis, neural network, machine learning algorithms, sensors, microphones

DOI of Published Version



Santoso LF, Baqai F, Gwozdz M, Lange J, Rosenberger MG, Sulzer J, Paydarfar D. Applying Machine Learning Algorithms for Automatic Detection of Swallowing from Sound. Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:2584-2588. doi: 10.1109/EMBC.2019.8857937. PMID: 31946425. Link to article on publisher's site

Journal/Book/Conference Title

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference


Laura F. Santoso participated in this study as a medical student in the Senior Scholars research program at the University of Massachusetts Medical School.

Related Resources

Link to Article in PubMed

PubMed ID