2019年冠状病毒病(COVID-19)
计算机科学
鉴定(生物学)
恶化
集合(抽象数据类型)
人工智能
模式识别(心理学)
采样(信号处理)
大流行
医学
数据挖掘
病理
内科学
疾病
滤波器(信号处理)
生物
计算机视觉
植物
传染病(医学专业)
程序设计语言
作者
Zahra Sabouri,Abbas Ghadimi,Azadeh Kiani-Sarkaleh,Kamrad Khoshhal Roudposhti
标识
DOI:10.1177/09544119221112523
摘要
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.
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