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Dysphagia
Published in: Dysphagia 6/2022

Open Access 29-01-2022 | Foreign Body Aspiration | Original Article

Using an Automated Speech Recognition Approach to Differentiate Between Normal and Aspirating Swallowing Sounds Recorded from Digital Cervical Auscultation in Children

Authors: Thuy T. Frakking, Anne B. Chang, Christopher Carty, Jade Newing, Kelly A. Weir, Belinda Schwerin, Stephen So

Published in: Dysphagia | Issue 6/2022

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Abstract

Use of machine learning to accurately detect aspirating swallowing sounds in children is an evolving field. Previously reported classifiers for the detection of aspirating swallowing sounds in children have reported sensitivities between 79 and 89%. This study aimed to investigate the accuracy of using an automatic speaker recognition approach to differentiate between normal and aspirating swallowing sounds recorded from digital cervical auscultation in children. We analysed 106 normal swallows from 23 healthy children (median 13 months; 52.1% male) and 18 aspirating swallows from 18 children (median 10.5 months; 61.1% male) who underwent concurrent videofluoroscopic swallow studies with digital cervical auscultation. All swallowing sounds were on thin fluids. A support vector machine classifier with a polynomial kernel was trained on feature vectors that comprised the mean and standard deviation of spectral subband centroids extracted from each swallowing sound in the training set. The trained support vector machine was then used to classify swallowing sounds in the test set. We found high accuracy in the differentiation of aspirating and normal swallowing sounds with 98% overall accuracy. Sensitivity for the detection of aspiration and normal swallowing sounds were 89% and 100%, respectively. There were consistent differences in time, power spectral density and spectral subband centroid features between aspirating and normal swallowing sounds in children. This study provides preliminary research evidence that aspirating and normal swallowing sounds in children can be differentiated accurately using machine learning techniques.
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Metadata
Title
Using an Automated Speech Recognition Approach to Differentiate Between Normal and Aspirating Swallowing Sounds Recorded from Digital Cervical Auscultation in Children
Authors
Thuy T. Frakking
Anne B. Chang
Christopher Carty
Jade Newing
Kelly A. Weir
Belinda Schwerin
Stephen So
Publication date
29-01-2022
Publisher
Springer US
Published in
Dysphagia / Issue 6/2022
Print ISSN: 0179-051X
Electronic ISSN: 1432-0460
DOI
https://doi.org/10.1007/s00455-022-10410-y

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