Abstract
Cough is a common symptom presenting in asthmatic children. In this investigation, an audio-based classification model is presented that can differentiate between healthy and asthmatic children, based on the combination of cough and vocalised /ɑ:/ sounds. A Gaussian mixture model using mel-frequency cepstral coefficients and constant-Q cepstral coefficients was trained. When comparing the predicted labels with the clinician's diagnosis, this cough sound model reaches an overall accuracy of 95.3%. The vocalised /ɑ:/ model reaches an accuracy of 72.2%, which is still significant because the dataset contains only 333 /ɑ:/ sounds versus 2029 cough sounds.
Highlights
Asthma typically evokes symptoms such as cough, wheeze, and dyspnea
We present a novel cough sound and vocalised /A:/ sound dataset, and a machine learning model that accurately differentiates between asthmatic and non-asthmatic children
In order to understand if vocalised /A:/ sound helps to differentiate healthy from asthmatic children, formants were extracted from vocalised /A:/ sounds for some of these children from various age groups
Summary
Asthma typically evokes symptoms such as cough, wheeze, and dyspnea. In this study, following our earlier report (Hee et al, 2019), we built a system to automatically screen patients to assist clinicians’ decision-making when cough presents as a symptom. Cough is a primary symptom of many other respiratory infections such as COVID-19 (alongside fever and fatigue), and potentially shares acoustic information in a manner similar to that reported here. Given the importance of forming accurate diagnoses based on cough sounds (Amrulloh et al, 2015; Chang, 1999; Infante et al, 2017; Todokoro et al, 2003) [even normal healthy children can exhibit cough epochs (Chang, 2005)], there has been some research on creating automatic classification models to characterize and differentiate various lung diseases. We expect that our resulting system will be rather more relevant and representative for clinicians “in the field.”
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call