Towards cough sound analysis using the Internet of things and deep learning for pulmonary disease prediction,Transactions on Emerging Telecommunications Technologies

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Towards cough sound analysis using the Internet of things and deep learning for pulmonary disease prediction
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-12-01 , DOI: 10.1002/ett.4184
Ajay Kumar ₁ , Kumar Abhishek ₁ , Muhammad R. Ghalib ₂ , Pranav Nerurkar _{3,

4} , Kunjal Shah ₃ , Madhav Chandane ₃ , Sunil Bhirud ₃ , Dhiren Patel ₃ , Yann Busnel ₅

Affiliation

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.

中文翻译：

使用物联网和深度学习进行咳嗽声音分析以预测肺部疾病

咳嗽是一百多种呼吸道疾病的症状。咳嗽信号中的音频特征包含了关于呼吸系统困境的博学。使用深度学习或信号处理，这些特征可用于构建有效的疾病预测系统。然而，咳嗽分析仍然是一个很少受到机器学习研究人员关注的领域。这可以归因于几个因素，例如效率低下的辅助系统、获取数据集的高成本或构建分类器的困难。本文对咳嗽音频分析在肺部疾病分类中的研究进展进行了分类和综述。它还探讨了研究中潜在的未来问题。此外，还提出了印度青少年常见的 10 种严重肺部疾病的分类模型。

更新日期：2020-12-01

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