Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals

2016 ◽  
Author(s):  
Chaitra Sridhar ◽  
U Rajendra Acharya ◽  
Hamido Fujita ◽  
G. Muralidhar Bairy
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Automated Diagnosis ◽  
Ecg Signals ◽  
Nonlinear Features ◽  
Artery Disease

NONLINEAR ANALYSIS OF CORONARY ARTERY DISEASE, MYOCARDIAL INFARCTION, AND NORMAL ECG SIGNALS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740006 ◽  
Author(s):  
YUKI HAGIWARA ◽  
OLIVER FAUST
Keyword(s):  
Myocardial Infarction ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Extraction Methods ◽  
Ecg Signals ◽  
Nonlinear Features ◽  
Quantification Analysis ◽  
Artery Disease ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

In this study, we analyze nonlinear feature extraction methods in terms of their ability to support the diagnosis of coronary artery disease (CAD) and myocardial infarction (MI). The nonlinear features were extracted from electrocardiogram (ECG) signals that were measured from CAD patients, MI patients as well as normal controls. We tested 34 recurrence quantification analysis (RQA) features, 14 bispectrum, and 136 cumulant features. The features were extracted from 10,546 normal, 41,545 CAD, and 40,182 MI heart beats. The feature quality was assessed with Student’s [Formula: see text]-test and the [Formula: see text]-value was used for feature ranking. We found that nonlinear features can effectively represent the physiological realities of the human heart.

1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals

2020 ◽  
Vol 70 ◽  
pp. 39-48 ◽  
Author(s):  
Ertan Butun ◽  
Ozal Yildirim ◽  
Muhammed Talo ◽  
Ru-San Tan ◽  
U. Rajendra Acharya
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Disease Detection ◽  
One Dimensional ◽  
Ecg Signals ◽  
Artery Disease ◽  
Coronary Artery Disease Detection

Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study

2017 ◽  
Vol 377 ◽  
pp. 17-29 ◽  
Author(s):  
U Rajendra Acharya ◽  
Hamido Fujita ◽  
Muhammad Adam ◽  
Oh Shu Lih ◽  
Vidya K Sudarshan ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Comparative Study ◽  
Ecg Signals ◽  
Artery Disease

NOVEL CLASSIFICATION OF CORONARY ARTERY DISEASE USING HEART RATE VARIABILITY ANALYSIS

2012 ◽  
Vol 12 (04) ◽  
pp. 1240017 ◽  
Author(s):  
SUMEET DUA ◽  
XIAN DU ◽  
S. VINITHA SREE ◽  
THAJUDIN AHAMED V. I.
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Principal Component ◽  
Heart Rate Variability Analysis ◽  
Fluctuation Analysis ◽  
Non Invasive ◽  
Nonlinear Features ◽  
Artery Disease

Coronary artery disease (CAD) is a leading cause of death worldwide. Heart rate variability (HRV) has been proven to be a non-invasive marker of the autonomic modulation of the heart. Nonlinear analyses of HRV signals have shown that the HRV is reduced significantly in patients with CAD. Therefore, in this work, we extracted nonlinear features from the HRV signals using the following techniques: recurrence plots (RP), Poincare plots, and detrended fluctuation analysis (DFA). We also extracted three types of entropy, namely, Shannon entropy (ShanEn), approximation entropy (ApEn), and sample entropy (SampEn). These features were subjected to principal component analysis (PCA). The significant principal components were evaluated using eight classification techniques, and the performances of these techniques were evaluated to determine which presented the highest accuracy in classifying normal and CAD classes. We observed that the multilayer perceptron (MLP) method resulted in the highest classification accuracy (89.5%) using our proposed technique.

AUTOMATED IDENTIFICATION OF CORONARY ARTERY DISEASE FROM SHORT-TERM 12 LEAD ELECTROCARDIOGRAM SIGNALS BY USING WAVELET PACKET DECOMPOSITION AND COMMON SPATIAL PATTERN TECHNIQUES

2017 ◽  
Vol 17 (07) ◽  
pp. 1740007 ◽  
Author(s):  
SHU LIH OH ◽  
MUHAMMAD ADAM ◽  
JEN HONG TAN ◽  
YUKI HAGIWARA ◽  
VIDYA K. SUDARSHAN ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Spatial Pattern ◽  
Wavelet Packet ◽  
Ecg Signal ◽  
Wavelet Packet Decomposition ◽  
Common Spatial Pattern ◽  
Ecg Signals ◽  
Computer Aided ◽  
Artery Disease

The occlusion of the coronary arteries commonly known as coronary artery disease (CAD) restricts the normal blood circulation required to the heart muscles, thus results in an irreversible myocardial damage or death (myocardial infarction). Clinically, electrocardiogram (ECG) is performed as a primary diagnostic tool to capture these cardiac activities and detect the presence of CAD. However, the use of computer-aided techniques can reduce the visual burden and manual time required for the analysis of complex ECG signals in order to identify the CAD affected subjects from normal ones. Therefore, in this study, a novel computer-aided technique is proposed using 2[Formula: see text]s of 12 lead ECG signals for the identification of CAD affected patients. Each of the 2[Formula: see text]s 12 lead ECG signal beats (3791 normal and 12308 CAD ECG signal beats) are implemented with four levels of wavelet packet decomposition (WPD) to obtain various coefficients. Using the fourth-level coefficients obtained for each lead ECG signal beat, new 2[Formula: see text]s. ECG signal beats are reconstructed. Later, the reconstructed signals are split into two-fold data sets, in which one set is used for acquiring common spatial pattern (CSP) filter and the other for obtaining features vector (vice versa). The obtained features are one by one fed into k-nearest neighbors (KNN) classifier for automated classification. The proposed system yielded maximum average classification results of 99.65% accuracy, 99.64% sensitivity and 99.7% specificity using 10 features. Our proposed algorithm is highly efficient and can be used by the clinicians as an aiding system in their CAD diagnosis, thus, assisting in faster treatment and avoiding the progression of CAD condition.

Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling

2008 ◽  
Vol 12 (4) ◽  
pp. 447-458 ◽  
Author(s):  
M.G. Tsipouras ◽  
T.P. Exarchos ◽  
D.I. Fotiadis ◽  
A.P. Kotsia ◽  
K.V. Vakalis ◽  
...  
Keyword(s):  
Data Mining ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Fuzzy Modeling ◽  
Automated Diagnosis ◽  
Artery Disease
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