Towards cough sound analysis using the Internet of things and deep learning for pulmonary disease prediction. Issue 10 (1st December 2020)

• Record Type: Journal Article
• Authors: Kumar, Ajay; Abhishek, Kumar; Ghalib, Muhammad R.; Nerurkar, Pranav; Shah, Kunjal; Chandane, Madhav; Bhirud, Sunil; Patel, Dhiren; Busnel, Yann
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 33:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-01
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.4184
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Cough is a symptom in over a hundred respiratory diseases. The audio features in cough signals contain erudition about the predicament of the respiratory system. Using deep learning or signal processing, these features can be used to build an effective disease prediction system. However, cough analysis remains an area that has received scant attention from machine learning researchers. This can be attributed to several factors such as inefficient ancillary systems, high expenses in obtaining datasets, or difficulty in building classifiers. This article categorized and reviewed the current progress on cough audio analysis for the classification of pulmonary diseases. It also explored potential future issues in research. In addition, it proposed a model for the classification of 10 serious pulmonary ailments commonly seen in Indian adolescents. The proposed model is evaluated against four existing state‐of‐the‐art techniques in the literature. Abstract : Signal processing of cough audio signals can be used reliably to classify cough sounds from non‐cough events. Comparatively, the domain of cough classification was relatively unexplored. An end to end machine learning model was proposed.