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Development of Machine Learning for Asthmatic and Healthy Voluntary Cough Sounds: A Proof of Concept Study

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Department of Paediatric Anaesthesia, KK Women’s and Children’s Hospital, Singapore 229899, Singapore
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Anaesthesiology and perioperative Science, Duke-NUS Medical School, Singapore 169857, Singapore
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Information Systems, Technology, and Design, Singapore University of Technology and Design, Singapore 487372, Singapore
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Department of Respiratory Medicine, KK Women’s and Children’s Hospital, Singapore 229899, Singapore
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Department of Children’s Emergency, KK Women’s and Children’s Hospital, Singapore 229899, Singapore
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Science and Math, Singapore University of Technology and Design, Singapore 487372, Singapore
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2833; https://doi.org/10.3390/app9142833
Received: 20 May 2019 / Revised: 29 June 2019 / Accepted: 9 July 2019 / Published: 16 July 2019
(This article belongs to the Section Acoustics and Vibrations)
(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children’s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts—healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model–Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians’ labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81% and 84.76%, respectively, when differentiating coughs from asthmatic children versus coughs from ‘healthy’ children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children. View Full-Text
Keywords: cough; asthma; machine learning; computer assisted diagnosis cough; asthma; machine learning; computer assisted diagnosis
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Hee, H.I.; Balamurali, B.; Karunakaran, A.; Herremans, D.; Teoh, O.H.; Lee, K.P.; Teng, S.S.; Lui, S.; Chen, J.M. Development of Machine Learning for Asthmatic and Healthy Voluntary Cough Sounds: A Proof of Concept Study. Appl. Sci. 2019, 9, 2833.

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