scholarly journals Single-beat analysis of ventricular late potentials in the surface electrocardiogram using the spectrotemporal pattern recognition algorithm in patients with coronary artery disease

1998 ◽  
Vol 19 (3) ◽  
pp. 435-446 ◽  
Author(s):  
P Steinbigler
Keyword(s):  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Coronary Artery ◽  
Recognition Algorithm ◽  
Late Potentials ◽  
Pattern Recognition Algorithm ◽  
Ventricular Late Potentials ◽  
Artery Disease ◽  
Surface Electrocardiogram

Prognostic significance of ventricular late potentials in coronary artery disease

1985 ◽  
Vol 109 (4) ◽  
pp. 725-732 ◽  
Author(s):  
Marc Zimmermann ◽  
Richard Adamec ◽  
Philippe Simonin ◽  
Jean Richez
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Prognostic Significance ◽  
Late Potentials ◽  
Ventricular Late Potentials ◽  
Artery Disease

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

Frequency of late potentials on signal-averaged electrocardiograms during thallium stress testing in coronary artery disease

1992 ◽  
Vol 70 (4) ◽  
pp. 432-435 ◽  
Author(s):  
Daniel J. Lenihan ◽  
Edmund Coyne ◽  
Barry Feldman ◽  
Richard Black ◽  
Gary Collins
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Stress Testing ◽  
Late Potentials ◽  
Artery Disease
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