Coronavirus Disease 2019 and Heart Failure: A Multiparametric Approach

Oliveros, Estefanía; Brailovsky, Yevgeniy; Scully, Paul; Nikolou, Evgenia; Rajani, Ronak; Grapsa, Julia

doi:10.15420/cfr.2020.09

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…Thus, it is hoped that when research is expanded, it will shed light on the development of effective treatment methods. The behavior changes of this virus and its effects on the human body constituted the main target of many other medical studies (Rajapakse and Dixit 2020 ; Guo et al 2019 ; Oliveros et al 2019 ; Paterson et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Manshouri

2021

Cogn Neurodyn

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Sound signals from the respiratory system are largely taken as tokens of human health. Early diagnosis of respiratory tract diseases is of great importance because, if delayed, it exerts irreversible effects on human health. The Coronavirus pandemic, which is deeply shaking the world, has revealed the importance of this diagnosis even more. During the pandemic, it has become the focus of researchers to differentiate symptoms from similar diseases such as influenza. Among these symptoms, the difference in cough sound played a distinctive role in research. Clinical data collected under the supervision of doctors in a reliable environment were used as the dataset consisting of 16 subjects suspected of COVID-19 with a specific patient demographic. Using the polymerase chain reaction test, the suspected subjects were divided into two groups as negative and positive. The negative and positive labels represent the patients with non-COVID and with a COVID-19 cough, respectively. Using the 3D plot or waterfall representation of the signal frequency spectrum, the salient features of the cough data are revealed. In this way, COVID-19 can be differentiated from other coughs by applying effective feature extraction and classification techniques. Power spectral density based on short-time Fourier transform and mel-frequency cepstral coefficients (MFCC) were chosen as the efficient feature extraction method. From among the classification techniques, the support vector machine (SVM) algorithm was applied to the processed signals in order to identify and classify COVID-19 cough. In terms of results evaluation, the cough of subjects with COVID-19 was detected with 95.86% classification accuracy thanks to the radial basis function (RBF) kernel function of SVM and the MFCC method. The diagnosis of COVID-19 coughs was performed with 98.6% and 91.7% sensitivity and specificity, respectively.

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Section: Introductionmentioning

confidence: 99%

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Manshouri

2021

Cogn Neurodyn

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“…characteristics of cardiovascular complications associated with coronavirus disease 2019 (COVID-19) and the use of telehealth in patients with heart failure. 1 This review should be highly valued considering that the rather high prevalence of cardiovascular comorbidities, including heart failure, among patients with COVID-19 is now recognised. Individuals with underlying cardiovascular conditions are at increased risk of myocardial injury associated with COVID-19, as reported previously, possibly due to reduced cardiorespiratory reserves.…”

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confidence: 99%

“…2 We would like to complement the discussion of Oliveros et al on acute heart failure and the use of renin-angiotensin system (RAS) inhibitors. 1 Acute heart failure represents a common cardiovascular complication of COVID-19, which may be precipitated by acute illness in patients with pre-existing known or undiagnosed heart disease or incident acute myocardial injury. blockers.…”

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confidence: 99%

Acute Heart Failure in Coronavirus Disease 2019 and the Management of Comedications

Kow

Hasan

2020

Card Fail Rev

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The Relationship of Behavioral, Social and Diabetes Factors with LVEF Measured Using Machine Learning Techniques

et al. 2022

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Purpose: Using a data and machine learning approach, from classical to complex, we aim to approximate the relationship between factors such as behavioral, social or comorbidity and the ejection fraction for hospitalized patients. To measure how much the independent variables influence the left ventricular ejection fraction (LVEF), classification models will be made and the influences of the independent variables will be interpreted. Through the data obtained, it is desired to improve the management of patients with heart failure (treatment, monitoring in primary medicine) in order to reduce morbidity and mortality.Patients and Methods: In this study, we enrolled 201 patients hospitalized withdecompensated chronic heart failure. The models used are extreme gradient boosting (XGB) and logistic regression (LR). To have a deeper analysis of the independent variables, their influences will be analyzed in two ways. The first is a modern technique, Shapley values, from game theory, adapted in the context of Machine Learning for XGB;and the second, the classical approach, is by analysis of Logistic Regression coefficients.Results: The importance of several factors related to behavior, social and diabetes are measured. Smoking, low education and obesity are the most harmful factors, while diabetes controlled by diet or medication does not significantly affect LVEF, indeed, there is a tendency to increase the LVEF. Conclusions: Using machine learning techniques, we can better understand to what extent certain factors affect LVEF in this sample. Following furtherstudies on larger groups and from different regions, prevention could be better understood and applied.

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Coronavirus Disease 2019 and Heart Failure: A Multiparametric Approach

Cited by 3 publications

References 35 publications

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Acute Heart Failure in Coronavirus Disease 2019 and the Management of Comedications

The Relationship of Behavioral, Social and Diabetes Factors with LVEF Measured Using Machine Learning Techniques

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