Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model.
Proc Inst Mech Eng H
; 236(9): 1430-1448, 2022 Sep.
Article
in English
| MEDLINE | ID: covidwho-1956987
ABSTRACT
Incidence and exacerbation of some of the cardiovascular diseases in the presence of the coronavirus will lead to an increase in the mortality rate among patients. Therefore, early diagnosis of such diseases is critical, especially during the COVID-19 pandemic (mild COVID-19 infection). Thus, for diagnosing the heart diseases related to the COVID-19, an automatic, non-invasive, and inexpensive method based on the heart sound processing approach is proposed. In the present study, a set of features related to the nature of heart signals is defined and extracted. The investigated features included morphological and statistical features in the heart sound frequencies. By extracting and selecting a set of effective features related to the mentioned diseases, and avoiding to use different segmentation and filtering techniques, dependence on a limited dataset and specific sampling procedures has been eliminated. Different classifiers with various kernels are applied for diagnosis in data unbalanced and balanced conditions. The results showed 93.15% accuracy and 93.72% F1-score using 60 effective features in data balanced conditions. The identification system using the extracted features from Azad dataset is able to achieve the desired results in a generalized dataset. In this way, in the shortest possible sampling time, the present system provided an effective and generalizable method and a practical model for diagnosing important cardiovascular diseases in the presence of coronavirus in the COVID-19 pandemic.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Cardiovascular Diseases
/
Heart Sounds
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Proc Inst Mech Eng H
Journal subject:
Biomedical Engineering
Year:
2022
Document Type:
Article
Affiliation country:
09544119221112523
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