Swallowing Sound Recognition at Home Using GMM - 23/11/18

Doi : 10.1016/j.irbm.2018.10.009

H. Khlaifi ^c,^a,^{^⁎} , D. Istrate ^a, J. Demongeot ^b, D. Malouche ^c
^a Sorbonne University, University of Technology of Compiegne, CNRS, UMR 7338 Biomechanics and Bioengineering, 60200 Compiegne, France
^b Université Grenoble Alpes (UGA), AGEIS, Faculté de Médecine, 38700 La Tronche, France
^c National School of Engineers of Tunis, Signals and Systems Research Unit, University of Tunis El Manar, Tunisia

^⁎Corresponding author.

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Abstract

Background

Aiming for autonomous living for the people after a stroke is the challenge these days especially for swallowing disorders or dysphagia. The most common cause of dysphagia is stroke. In France, stroke occurs every 4 minutes, which implies 13000 hospitalizations per year. Currently, continuous medical home monitoring of patients is not available. The patient must be hospitalized or visit the medical community for possible follow-up. It is in this context that E-SwallHome (Swallowing & Breathing: Modelling and e-Health at Home) project proposes to develop tools, from hospital care until the patient returns home, which are able to monitor in real time the process of swallowing.

Method

This paper presents a relevant health problem affecting patient recovering from stroke. We propose a frequency acoustical analysis for automatic detection of swallowing process and a non-invasive acoustic based method to differentiate between swallowing sounds and other sounds in normal ambient environment during food intake.

Result

The proposal algorithm for events detection gives a global rate of good detection of 87.31%. Classification of sounds of swallowing and other sounds based on Gaussian Mixture Models (GMM), using the leave-one-out approach according to the small amount of data in our database, gives a good recognition rate of swallowing sounds of 84.57%.

Conclusion

The proposal method has great potential to assist in the clinical evaluation using only swallowing sounds, which is a non-invasive technic for swallowing studies.

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Graphical abstract

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Highlights

•	Detection of useful signals from continuous sound recording and its classification.
•	Automatic detection is efficient and allows global good detection rate of 87.31%.
•	GMM based swallowing sounds classification shows a good recognition rate of 85.57%.
•	The algorithm evaluation was performed on data from different sensors and conditions.
•	Swallowing sound isolation was good for 95.94% but for other sounds was deteriorated.