Automatic detection and recognition of swallowing sounds

Khlaifi, H., Badii, A., Istrate, D. and Demongeot, J. (2019) Automatic detection and recognition of swallowing sounds. In: Moucek, R., Fred, A. and Gamboa, H. (eds.) Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies. SciTePress, Prague, Czech Republic, pp. 221-229. ISBN 9789897583537 doi: 10.5220/0007310802210229

Abstract/Summary

This paper proposes a non-invasive, acoustic-based method to i) automatically detect sounds through a neckworn microphone providing a stream of acoustic input comprising of a) swallowing-related, b) speech and c) other ambient sounds (noise); ii) classify and detect swallowing-related sounds, speech or ambient noise within the acoustic stream. The above three types of acoustic signals were recorded from subjects, without any clinical symptoms of dysphagia, with a microphone attached to the neck at a pre-studied position midway between the Laryngeal Prominence and the Jugular Notch. Frequency-based analysis detection algorithms were developed to distinguish the above three types of acoustic signals with an accuracy of 86.09%. Integrated automatic detection algorithms with classification based on Gaussian Mixture Model (GMM) using the Expectation Maximisation algorithm (EM), achieved an overall validated recognition rate of 87.60% which increased to 88.87 recognition accuracy if the validated false alarm classifications were also to be included. The proposed approach thus enables the recovery from ambient signals, detection and time-stamping of the acoustic footprints of the swallowing process chain and thus further analytics to characterise the swallowing process in terms of consistency, normality and possibly risk-assessing and localising the level of any swallowing abnormality i.e. the dysphagia. As such this helps reduce the need for invasive techniques for examination and evaluation of patient’s swallowing process and enables diagnostic clinical evaluation based only on acoustic data analytics and non-invasive clinical observations.

Item Type	Book or Report Section
URI	https://reading-clone.eprints-hosting.org/id/eprint/88155
Identification Number/DOI	10.5220/0007310802210229
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	SciTePress
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