Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review
Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/8/2896 |
| _version_ | 1797443792425451520 |
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| author | Antoine Serrurier Christiane Neuschaefer-Rube Rainer Röhrig |
| author_facet | Antoine Serrurier Christiane Neuschaefer-Rube Rainer Röhrig |
| author_sort | Antoine Serrurier |
| collection | DOAJ |
| description | Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health. |
| first_indexed | 2024-03-09T13:03:07Z |
| format | Article |
| id | doaj.art-c2196f3f95f343c98f0f1844a362ec7a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T13:03:07Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c2196f3f95f343c98f0f1844a362ec7a2023-11-30T21:52:30ZengMDPI AGSensors1424-82202022-04-01228289610.3390/s22082896Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative ReviewAntoine Serrurier0Christiane Neuschaefer-Rube1Rainer Röhrig2Institute of Medical Informatics, University Hospital of the RWTH Aachen, 52057 Aachen, GermanyClinic for Phoniatrics, Pedaudiology & Communication Disorders, University Hospital of the RWTH Aachen, 52057 Aachen, GermanyInstitute of Medical Informatics, University Hospital of the RWTH Aachen, 52057 Aachen, GermanyCough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.https://www.mdpi.com/1424-8220/22/8/2896cough sound acquisitionautomatic cough sound processingcough diagnosiscough recognitionliterature reviewmachine learning |
| spellingShingle | Antoine Serrurier Christiane Neuschaefer-Rube Rainer Röhrig Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review Sensors cough sound acquisition automatic cough sound processing cough diagnosis cough recognition literature review machine learning |
| title | Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review |
| title_full | Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review |
| title_fullStr | Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review |
| title_full_unstemmed | Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review |
| title_short | Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review |
| title_sort | past and trends in cough sound acquisition automatic detection and automatic classification a comparative review |
| topic | cough sound acquisition automatic cough sound processing cough diagnosis cough recognition literature review machine learning |
| url | https://www.mdpi.com/1424-8220/22/8/2896 |
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