Authors:
Hajer Khlaifi
1
;
Atta Badii
2
;
Dan Istrate
1
and
Jacques Demongeot
3
Affiliations:
1
University of Technology of Compiegne, UTC University, Compiegne and France
;
2
University of Reading, Department of Computer Science, School of Mathematical, Physical and Computational Sciences, Reading and U.K.
;
3
Laboratory AGEIS EA 7407, University Grenoble Alpes, Faculty of Medicine, Grenoble and France
Keyword(s):
Swallowing Sounds, Automatic Detection, Classification, Non-invasive Dysphagia Clinical Assessment Support.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Cloud Computing
;
e-Health
;
Health Information Systems
;
Healthcare Management Systems
;
Platforms and Applications
;
Telemedicine
Abstract:
This paper proposes a non-invasive, acoustic-based method to i) automatically detect sounds through a neck-worn 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 v
alidated 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 the examination and evaluation of patient’s swallowing process and enables diagnostic clinical evaluation based only on acoustic data analytics and non-invasive clinical observations.
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