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Frakking TT, Chang AB, Carty C, Newing J, Weir KA, Schwerin B, So S. Using an Automated Speech Recognition Approach to Differentiate Between Normal and Aspirating Swallowing Sounds Recorded from Digital Cervical Auscultation in Children. Dysphagia 2022; 37:1482-1492. [PMID: 35092488 PMCID: PMC9643257 DOI: 10.1007/s00455-022-10410-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 01/19/2022] [Indexed: 12/16/2022]
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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Affiliation(s)
- Thuy T. Frakking
- Research Development Unit, Caboolture Hospital, Metro North Hospital & Health Service, McKean St, Caboolture, QLD 4510 Australia ,Centre for Clinical Research, School of Medicine, The University of Queensland, Herston, QLD 4029 Australia ,Speech Pathology Department, Gold Coast University Hospital, Gold Coast Hospital & Health Service, 1 Hospital Boulevard, Southport, QLD 4215 Australia
| | - Anne B. Chang
- Department of Respiratory Medicine, Queensland Children’s Hospital, 501 Stanley St, South Brisbane, QLD 4101 Australia ,Child Health Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia ,Australian Centre for Health Services Innovation, Queensland University of Technology, Level 7, 62 Graham St, South Brisbane, QLD 4101 Australia
| | - Christopher Carty
- Research Development Unit, Caboolture Hospital, Metro North Hospital & Health Service, McKean St, Caboolture, QLD 4510 Australia ,Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, 4222 Australia
| | - Jade Newing
- School of Engineering and Built Environment, Griffith University, Parklands Dr, Southport, QLD 4215 Australia
| | - Kelly A. Weir
- Menzies Health Institute QLD & School of Health Sciences & Social Work, Griffith University, Gold Coast Campus, 1 Parklands Avenue, Southport, QLD 4222 Australia ,Allied Health Research, Gold Coast University Hospital, Gold Coast Hospital & Health Service, 1 Hospital Boulevard, Southport, QLD 4215 Australia
| | - Belinda Schwerin
- School of Engineering and Built Environment, Griffith University, Parklands Dr, Southport, QLD 4215 Australia
| | - Stephen So
- School of Engineering and Built Environment, Griffith University, Parklands Dr, Southport, QLD 4215 Australia
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Frakking TT, Chang AB, David M, Orbell‐Smith J, Weir KA. Clinical feeding examination with cervical auscultation for detecting oropharyngeal aspiration: A systematic review of the evidence. Clin Otolaryngol 2019; 44:927-934. [DOI: 10.1111/coa.13402] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Revised: 05/16/2019] [Accepted: 07/07/2019] [Indexed: 02/02/2023]
Affiliation(s)
- Thuy T. Frakking
- Speech Pathology Department & Research Development Unit, Metro North Hospital & Health Service, Queensland Health Caboolture Hospital Caboolture QLD Australia
- School of Health and Rehabilitation Sciences The University of Queensland St Lucia QLD Australia
| | - Anne B. Chang
- Department of Respiratory Medicine Queensland Children's Hospital QLD Australia
- Child Health Division, Menzies School of Health Research Charles Darwin University Casuarina NT Australia
- Centre for Children's Health Research Queensland University of Technology QLD Australia
| | - Michael David
- School of Medicine and Public Health The University of Newcastle Gosford NSW Australia
| | - Jane Orbell‐Smith
- Library Services, Metro North Hospital & Health Service Caboolture Hospital Caboolture QLD Australia
| | - Kelly A. Weir
- School of Allied Health Sciences, Menzies Health Institute Queensland Griffith University Brisbane QLD Australia
- Clinical Governance, Education & Research Gold Coast University Hospital Southport QLD Australia
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