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Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review.
Serrurier, Antoine; Neuschaefer-Rube, Christiane; Röhrig, Rainer.
  • Serrurier A; Institute of Medical Informatics, University Hospital of the RWTH Aachen, 52057 Aachen, Germany.
  • Neuschaefer-Rube C; Clinic for Phoniatrics, Pedaudiology & Communication Disorders, University Hospital of the RWTH Aachen, 52057 Aachen, Germany.
  • Röhrig R; Clinic for Phoniatrics, Pedaudiology & Communication Disorders, University Hospital of the RWTH Aachen, 52057 Aachen, Germany.
Sensors (Basel) ; 22(8)2022 Apr 10.
Article in English | MEDLINE | ID: covidwho-1785900
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Crowdsourcing / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Reviews Topics: Long Covid Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: S22082896

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Crowdsourcing / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Reviews Topics: Long Covid Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: S22082896