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Critical Reviews™ in Biomedical Engineering

Published 6 issues per year

ISSN Print: 0278-940X

ISSN Online: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

Indexed in

Technologies for Developing Ambulatory Cough Monitoring Devices

Volume 41, Issue 6, 2013, pp. 457-468
DOI: 10.1615/CritRevBiomedEng.2014010886
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ABSTRACT

Cough is a prevailing symptom in most lung diseases. While cough sounds themselves can be very instrumental in the diagnosis of certain diseases, their intensity and frequency also infers the intensity of the particular illness. There is an imperative need for a robust system for identifying and analyzing cough sounds. In implementing such systems, researchers are confronted with technical challenges such as the choice of sensors and methods of signal acquisition, the real time analysis of the acquired signals, and the accurate identification of cough events, distinguishing them from similar sounds such as speech, laughing, throat clearing and sneezing. Previous approaches have employed external environmental sensing methods to achieve more accurate detections at the expense of mobility, scalability and real-time cough sensing. Alternative approaches have proposed wearable cough sensing methods, which, while mobile, can often face challenges in terms of robustness and obtrusiveness. In this paper, we explore the strengths and shortcomings of the various techniques that have been proposed for automatic detection and analysis of cough sounds. We also suggest the next steps in furthering the state of the art.

CITED BY
  1. Pires Ivan Miguel, Hussain Faisal, M. Garcia Nuno M., Lameski Petre, Zdravevski Eftim, Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification, Future Internet, 12, 11, 2020. Crossref

  2. Joshi Aditi, Kumar Rahul, Tiwari Chandni, Enhanced exploration of chronic cough using Improved Convolutional Neural Networks and remote monitoring harnessing Internet of Things (IoT), Materials Today: Proceedings, 46, 2021. Crossref

  3. Capris Ticiana, Melo Pedro, Pereira Pedro, Morgado José, Garcia Nuno M., Pires Ivan Miguel, Approach for the Development of a System for COVID-19 Preliminary Test, in Science and Technologies for Smart Cities, 372, 2021. Crossref

  4. Serrurier Antoine, Neuschaefer-Rube Christiane, Röhrig Rainer, Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review, Sensors, 22, 8, 2022. Crossref

  5. Ali Shan E., Khan Ali Nawaz, Zia Shafaq, Cough Detection Using Mobile Phone Accelerometer and Machine Learning Techniques, in The Science behind the COVID Pandemic and Healthcare Technology Solutions, 15, 2022. Crossref

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