Romanian Journal of Information Science and Technology (ROMJIST)

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ROMJIST is a publication of Romanian Academy,
Section for Information Science and Technology

Editor – in – Chief:
Academician Dan Dascalu

Secretariate (office):
Adriana Neagu
Adress for correspondence: romjist@nano-link.net (after 1st of January, 2019)

Editing of the printed version: Mihaela Marian (Publishing House of the Romanian Academy, Bucharest)

Technical editor
of the on-line version:
Lucian Milea (University POLITEHNICA of Bucharest)

Sponsors:
• National Institute for R & D
in Microtechnologies
(IMT Bucharest), www.imt.ro
• Association for Generic
and Industrial Technologies (ASTEGI), www.astegi.ro

ROMJIST Volume 23, No. S, 2020, pp. S77-S88, Paper no. 656/2020
 

Gheorghe POP, Horia CUCU, Dragoş BURILEANU and Corneliu BURILEANU
Cough Sound Recognition in Respiratory Disease Epidemics

ABSTRACT: The new coronavirus epidemic, which outbroke in 2019, has now grown into a full-blown pandemic, raising global concerns by its high infection speed and mortality rate. People developing the disease fill emergency hospitals, while the problem may deepen if worried general population cluster emergency rooms just for diagnosis. To control such respiratory disease epidemic, governments and medical staff usually decide to reduce virus transmission by enforcing social distance and placing in quarantine all persons suspected of carrying the virus. Everyone else is asked to stay insulated as long as possible, and refrain from calling emergency services unless relevant symptoms appear. With the medical staff shortage forced by such an epidemic, it would be very useful to have a diagnosis system capable of checking people for symptoms. As the direct contact of patients with objects used in common may raise virus transmission concerns, non-contact devices are accepted for use in evaluating a person’s health condition. Under these limitations, we present a cough sound recognition method, which, as new relevant data become available, can be extended to work more as a respiratory disease diagnostic tool.

KEYWORDS: neural computing, respiratory disease epidemics, cough sound recognition

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