Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images

2013 ◽  
Vol 112 (3) ◽  
pp. 624-632 ◽  
Author(s):  
U. Rajendra Acharya ◽  
S. Vinitha Sree ◽  
M. Muthu Rama Krishnan ◽  
N. Krishnananda ◽  
Shetty Ranjan ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Left Ventricle ◽  
Automated Classification ◽  
Artery Disease ◽  
Echocardiographic Images

scholarly journals CHRONIC POSTINFARCTION ANEURYSMS OF THE LEFT VENTRICLE: HISTORY, CLINICAL FEATURES, DIAGNOSTIC METHODS. NEW CLASSIFICATION OF CHRONIC POSTINFARCTION ANEURYSMS OF THE LEFT VENTRICLE IN PATIENTS WITH CORONARY ARTERY DISEASE.

2021 ◽  
Author(s):  
Aleksandr V. Bocharov ◽  
Leonid V. Popov ◽  
Astan K Mittsiev ◽  
Magomet D Lagkuev
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Left Ventricle ◽  
Left Ventricular ◽  
Diagnostic Methods ◽  
New Classification ◽  
Historical Aspects ◽  
Artery Disease ◽  
Optimal Treatment Strategy

the article highlights the historical aspects of the development of the concept of diagnosis and treatment of post-infarct left ventricular aneurysms, the possibilities of the main diagnostic methods, and also offers a new classification of post-infarct left ventricular aneurysms, taking into account the type of blood supply and the nature of the coronary lesion, which allows determining the optimal treatment strategy

scholarly journals Discordance between Body-Mass Index and Body Adiposity Index in the Classification of Weight Status of Elderly Patients with Stable Coronary Artery Disease

2021 ◽  
Vol 10 (5) ◽  
pp. 943
Author(s):  
Bartosz Hudzik ◽  
Justyna Nowak ◽  
Janusz Szkodzinski ◽  
Aleksander Danikiewicz ◽  
Ilona Korzonek-Szlacheta ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Body Mass Index ◽  
Coronary Artery ◽  
Weight Status ◽  
Stable Coronary Artery Disease ◽  
Reference Method ◽  
Normal Weight ◽  
Body Adiposity ◽  
Artery Disease

Background and Aims: Body-mass index (BMI) is a popular method implemented to define weight status. However, describing obesity by BMI may result in inaccurate assessment of adiposity. The Body Adiposity Index (BAI) is intended to be a directly validated method of estimating body fat percentage. We set out to compare body weight status assessment by BMI and BAI in a cohort of elderly patients with stable coronary artery disease (CAD). Methods: A total of 169 patients with stable CAD were enrolled in an out-patient cardiology clinic. The National Research Council (US) Committee on Diet and Health classification was used for individuals older than 65 years as underweight BMI < 24 kg/m2, normal weight BMI 24–29 kg/m2, overweight BMI 29–35 kg/m2, and obesity BMI > 35 kg/m2. In case of BAI, we used sex- and age-specific classification of weight status. In addition, body fat was estimated by bioelectrical impedance analysis (BImpA). Results: Only 72 out of 169 patients (42.6%) had concordant classification of weight status by both BMI and BAI. The majority of the patients had their weight status either underestimated or overestimated. There were strong positive correlations between BMI and BImpA (FAT%) (R = 0.78 p < 0.001); BAI and BImpA (FAT%) (R = 0.79 p < 0.001); and BMI and BAI (R = 0.67 p < 0.001). BMI tended to overestimate the rate of underweight, normal weight or overweight, meanwhile underestimating the rate of obesity. Third, BMI exhibited an average positive bias of 14.4% compared to the reference method (BImpA), whereas BAI exhibited an average negative bias of −8.3% compared to the reference method (BImpA). Multivariate logistic regression identified independent predictors of discordance in assessing weight status by BMI and BAI: BImpA (FAT%) odds ratio (OR) 1.29, total body water (%) OR 1.61, fat mass index OR 2.62, and Controlling Nutritional Status (CONUT) score OR 1.25. Conclusions: There is substantial rate of misclassification of weight status between BMI and BAI. These findings have significant implications for clinical practice as the boundary between health and disease in malnutrition is crucial to accurately define criteria for intervention. Perhaps BMI cut-offs for classifying weight status in the elderly should be revisited.

scholarly journals Association of 25 bp Deletion in MYBPC3 Gene with Left Ventricle Dysfunction in Coronary Artery Disease Patients

PLoS ONE ◽  
2011 ◽  
Vol 6 (9) ◽  
pp. e24123 ◽  
Author(s):  
Anshika Srivastava ◽  
Naveen Garg ◽  
Tulika Mittal ◽  
Roopali Khanna ◽  
Shipra Gupta ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Left Ventricle ◽  
Artery Disease ◽  
Mybpc3 Gene ◽  
Left Ventricle Dysfunction

scholarly journals e0496 Comparative study of impaired with normal left ventricle function patients with triple coronary artery disease

Heart ◽  
2010 ◽  
Vol 96 (Suppl 3) ◽  
pp. A154-A155
Author(s):  
G. Zhan ◽  
Y. Yue-Jin ◽  
X. Bo ◽  
C. Ji-Lin ◽  
Q. Shu-Bin ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Left Ventricle ◽  
Comparative Study ◽  
Left Ventricle Function ◽  
Artery Disease ◽  
Ventricle Function

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 35-43
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Multilayer Perceptron ◽  
Artificial Neural ◽  
Artery Disease ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

Ischaemic heart disease in women

ESC CardioMed ◽  
2018 ◽  
pp. 2836-2840
Author(s):  
Martha Gulati
Keyword(s):  
Myocardial Infarction ◽  
Coronary Artery Disease ◽  
Heart Disease ◽  
Coronary Artery ◽  
Ischaemic Heart Disease ◽  
Obstructive Coronary Artery Disease ◽  
Diagnostic Testing ◽  
Atypical Presentation ◽  
Artery Disease

The more atypical presentation of women makes the diagnostic evaluation of symptomatic women challenging and results in more frequent referral for diagnostic testing to improve the precision of the ischaemic heart disease likelihood estimate. The classification of ischaemic heart disease and myocardial infarction has moved beyond the diagnosis of obstructive coronary artery disease and encompasses ischaemia that can occur in the presence and absence of obstructive coronary artery disease. Consideration of the different pathophysiology of ischaemia that may occur in women needs to be considered in the evaluation and treatment of ischaemic heart disease in women.

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