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.

Heart sound signal processing is a low-cost, and noninvasive method for the early diagnosis of various types of cardiovascular diseases. In this study, a parallel diagnosing method was proposed to detect various types of heart diseases and healthy heart samples. The proposed system can detect a person who might be simultaneously suffering from two or more heart diseases. Contributing to this line of investigation, effective features were obtained from the morphological and statistical features extracted from five frequency ranges of heart sounds. Applying such features in diagnosing any heart disease acts as a fingerprint specific to that disease. Therefore, the investigation of selected features, especially in each of the frequency ranges of heart sounds and murmurs, provided us with valuable information about the behavior of the diagnostic system in the detection of heart diseases. In addition to using features related to the nature of heart sounds, the proposed method of this study got rid of both the need to apply different filters needed to remove noise and dependence on a specific dataset. With the aid of the effective features in the parallel diagnosis of 15 different types of important and common heart diseases and a healthy class from each other, the diagnostic system of the present study was able to achieve the average accuracy of 97.06%, the average sensitivity of 97.99%, and the average specificity of 96.18% in the shortest possible time. The proposed approach is an important step in the screening and remote monitoring and tracking of disease progression.

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