Novel COVID-19 Screening Using Cough Recordings of A Mobile Patient Monitoring System.
Annu Int Conf IEEE Eng Med Biol Soc
; 2021: 2353-2357, 2021 11.
Article
in English
| MEDLINE | ID: covidwho-1566225
ABSTRACT
Since the COVID-19 pandemic began, research has shown promises in building COVID-19 screening tools using cough recordings as a convenient and inexpensive alternative to current testing techniques. In this paper, we present a novel and fully automated algorithm framework for cough extraction and COVID-19 detection using a combination of signal processing and machine learning techniques. It involves extracting cough episodes from audios of a diverse real-world noisy conditions and then screening for the COVID-19 infection based on the cough characteristics. The proposed algorithm was developed and evaluated using self-recorded cough audios collected from COVID-19 patients monitored by Biovitals® Sentinel remote patient management platform and publicly available datasets of various sound recordings. The proposed algorithm achieves a duration Area Under Receiver Operating Characteristic curve (AUROC) of 98.6% in the cough extraction task and a mean cross-validation AUROC of 98.1% in the COVID-19 classification task. These results demonstrate high accuracy and robustness of the proposed algorithm as a fast and easily accessible COVID-19 screening tool and its potential to be used for other cough analysis applications.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
Annu Int Conf IEEE Eng Med Biol Soc
Year:
2021
Document Type:
Article
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