Linear discriminant analysis of regional ejection fractions in the diagnosis of coronary artery disease

1987 ◽  
Vol 12 (12) ◽  
pp. 602-604 ◽  
Author(s):  
K. H. Douglas ◽  
J. M. Links ◽  
D. C. P. Chen ◽  
D. F. Wong ◽  
Henry N. Wagner
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Linear Discriminant ◽  
Artery Disease

scholarly journals Linear discriminant analysis and principal component analysis to predict coronary artery disease

2020 ◽  
Vol 26 (3) ◽  
pp. 2181-2192 ◽  
Author(s):  
Carlo Ricciardi ◽  
Antonio Saverio Valente ◽  
Kyle Edmund ◽  
Valeria Cantoni ◽  
Roberta Green ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Artery Disease ◽  
Principal Component Analysis ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Discriminant ◽  
Artery Disease

Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.

scholarly journals Detection of coronary artery disease by reduced features and extreme learning machine

2018 ◽  
Vol 91 (2) ◽  
pp. 166-175 ◽  
Author(s):  
Ram Sewak Singh ◽  
Barjinder Singh Saini ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Hidden Node ◽  
Artery Disease ◽  
Learning Machine

Objective. Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.Methodology. For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from  a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.Results. Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

scholarly journals Coronary Artery Disease Detection by Machine Learning with Coronary Bifurcation Features

2020 ◽  
Vol 10 (21) ◽  
pp. 7656
Author(s):  
Xueping Chen ◽  
Yi Fu ◽  
Jiangguo Lin ◽  
Yanru Ji ◽  
Ying Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Expansion Ratio ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Coronary Bifurcation ◽  
Linear Discriminant ◽  
Accurate Detection ◽  
Artery Disease

Background: Early accurate detection of coronary artery disease (CAD) is one of the most important medical research areas. Researchers are motivated to utilize machine learning techniques for quick and accurate detection of CAD. Methods: To obtain the high quality of features used for machine learning, we here extracted the coronary bifurcation features from the coronary computed tomography angiography (CCTA) images by using the morphometric method. The machine learning classifier algorithms, such as logistic regression (LR), decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), artificial neural network (ANN), and support vector machine (SVM) were applied for estimating the performance by using the measured features. Results: The results showed that in comparison with other machine learning methods, the polynomial-SVM with the use of the grid search optimization method had the best performance for the detection of CAD and had yielded the classification accuracy of 100.00%. Among six examined coronary bifurcation features, the exponent of vessel diameter (n) and the area expansion ratio (AER) were two key features in the detection of CAD. Conclusions: This study could aid the clinicians to detect CAD accurately, which may probably provide an alternative method for the non-invasive diagnosis in clinical.

Elevated level of circulatory sTLT1 induces inflammation through SYK/MEK/ERK signalling in coronary artery disease

2019 ◽  
Vol 133 (22) ◽  
pp. 2283-2299
Author(s):  
Apabrita Ayan Das ◽  
Devasmita Chakravarty ◽  
Debmalya Bhunia ◽  
Surajit Ghosh ◽  
Prakash C. Mandal ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Chronic Inflammation ◽  
Ex Vivo ◽  
Human Subjects ◽  
Critical Role ◽  
Atherosclerotic Cardiovascular Disease ◽  
Tnf Α ◽  
Artery Disease

Abstract The role of inflammation in all phases of atherosclerotic process is well established and soluble TREM-like transcript 1 (sTLT1) is reported to be associated with chronic inflammation. Yet, no information is available about the involvement of sTLT1 in atherosclerotic cardiovascular disease. Present study was undertaken to determine the pathophysiological significance of sTLT1 in atherosclerosis by employing an observational study on human subjects (n=117) followed by experiments in human macrophages and atherosclerotic apolipoprotein E (apoE)−/− mice. Plasma level of sTLT1 was found to be significantly (P<0.05) higher in clinical (2342 ± 184 pg/ml) and subclinical cases (1773 ± 118 pg/ml) than healthy controls (461 ± 57 pg/ml). Moreover, statistical analyses further indicated that sTLT1 was not only associated with common risk factors for Coronary Artery Disease (CAD) in both clinical and subclinical groups but also strongly correlated with disease severity. Ex vivo studies on macrophages showed that sTLT1 interacts with Fcɣ receptor I (FcɣRI) to activate spleen tyrosine kinase (SYK)-mediated downstream MAP kinase signalling cascade to activate nuclear factor-κ B (NF-kB). Activation of NF-kB induces secretion of tumour necrosis factor-α (TNF-α) from macrophage cells that plays pivotal role in governing the persistence of chronic inflammation. Atherosclerotic apoE−/− mice also showed high levels of sTLT1 and TNF-α in nearly occluded aortic stage indicating the contribution of sTLT1 in inflammation. Our results clearly demonstrate that sTLT1 is clinically related to the risk factors of CAD. We also showed that binding of sTLT1 with macrophage membrane receptor, FcɣR1 initiates inflammatory signals in macrophages suggesting its critical role in thrombus development and atherosclerosis.

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