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Coronary Artery Disease (CAD) is the root cause for chain of catastrophic heart diseases such as Ischemic Heart Disease (IHD), Myocardial Infarction (MI) or Heart Attack (HA) and Heart Failure (HF). Early detection and treatment of this CAD condition is essential and may help in preventing it from progressing further. However, faster and accurate identification of CAD from Electrocardiogram (ECG) signals using manual interpretations is not an easy task to achieve. Thus, computer-aided techniques are necessary for the automated characterization of CAD condition. Therefore, this work proposes application of Higher-Order Statistics and Spectra (HOS) for an automated classification of normal and CAD conditions using ECG signals. In this paper, 182,013 beats (137,587 normal beats and 44,426 beats with CAD) ECG beats are used. HOS bispectrum and cumulant features are extracted from each ECG beat. The features extracted are applied to Principal Component Analysis (PCA) dimension reduction technique. Then PCA coefficients are ranked using Bhattacharyya method, entropy, fuzzy Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristics (ROC), t -test, Wilcoxon ranking methods. All ranked features are subjected to k-Nearest Neighbors (KNN) and Decision Tree (DT) classifiers to obtain the highest classification performance. The proposed methodology has achieved 98.17% accuracy, 94.57% sensitivity, and 99.34% specificity, using KNN classifier using 13 bispectrum features. Similarly, we have obtained 98.99% average accuracy, 97.75% sensitivity, and 99.39% specificity using DT classifier with 31 cumulant features. In addition, we have formulated and developed an integrated index called Coronary Artery Disease Index (CADI) for automated characterization of normal and ECG signals with CAD condition using a single number. This proposed CADI works efficiently to discriminate normal and CAD ECG classes for the any dataset with priory knowledge of the database. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a P-QRS-T wave, representing the depolarization and repolarization mechanism of the heart. Among different cardiac abnormalities, the atrial fibrillation (AF) and atrial flutter (AFL) are frequently encountered medical emergencies with life threatening complications. The clinical features of ECG, the amplitude and intervals of different peaks depict the functioning of the heart. The changes in the morphological features during various pathological conditions help the physician to diagnose the abnormality. These changes, however, are very subtle and difficult to correlate with the abnormalities and demand a lot of clinical acumen. Hence a computer aided diagnosis (CAD) tool can help physicians significantly. In this paper, a general methodology is presented for automatic detection of the normal, AF and AFL beats of ECG. Four different methods are investigated for feature extraction: (1) the principal components (PCs) of discrete wavelet transform (DWT) coefficients, (2) the independent components (ICs) of DWT coefficients, (3) the PCs of discrete cosine transform (DCT) coefficients, and (4) the ICs of DCT coefficients. Three different classification techniques are explored: (1) K-nearest neighbor (KNN), (2) decision tree (DT), and (3) artificial neural network (ANN). The methodology is tested using data from MIT BIH arrhythmia and atrial fibrillation databases. DCT coupled with ICA and KNN yielded the highest average sensitivity of 99.61%, average specificity of 100%, and classification accuracy of 99.45% using ten fold cross validation. Thus, the proposed automated diagnosis system provides high reliability to be used by clinicians. The method can be extended for detection of other abnormalities of heart and to other physiological signals. [ABSTRACT FROM AUTHOR]

Highlights: [•] Normal, mild glaucoma and moderate/severe glaucoma classes are classified using digital fundus images. [•] Higher order spectra (HOS) cumulants and LDA are used to extract the features. [•] Proposed system can detect the early glaucoma stage with an average accuracy of 84.72%, and the three classes with an average accuracy of 92.65%, sensitivity of 100% and specificity of 92% using Naïve Bayesian classifier. [ABSTRACT FROM AUTHOR]

Highlights: [•] Paper compares the classification performance of normal, AF and AFL. [•] HOS cumulant and bispectrum methods are compared. [•] ICA coupled with KNN performed better. [•] It provided 99.50% accuracy and 100% sensitivity. [•] System has potential applications in cardiovascular screening and diagnosis. [ABSTRACT FROM AUTHOR]

Abstract: Electrocardiogram (ECG) is the P-QRS-T wave, representing the cardiac function. The information concealed in the ECG signal is useful in detecting the disease afflicting the heart. It is very difficult to identify the subtle changes in the ECG in time and frequency domains. The Discrete Wavelet Transform (DWT) can provide good time and frequency resolutions and is able to decipher the hidden complexities in the ECG. In this study, five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed namely: non-ectopic beats, supra-ventricular ectopic beats, ventricular ectopic beats, fusion betas and unclassifiable and paced beats. Three dimensionality reduction algorithms; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were independently applied on DWT sub bands for dimensionality reduction. These dimensionality reduced features were fed to the Support Vector Machine (SVM), neural network (NN) and probabilistic neural network (PNN) classifiers for automated diagnosis. ICA features in combination with PNN with spread value (σ) of 0.03 performed better than the PCA and LDA. It has yielded an average sensitivity, specificity, positive predictive value (PPV) and accuracy of 99.97%, 99.83%, 99.21% and 99.28% respectively using ten-fold cross validation scheme. [Copyright &y& Elsevier]

Abstract: The electrocardiogram (ECG) is the P-QRS-T wave representing the information about the condition of the heart. The shape and size of the ECG signal may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human eye and may indicate a particular cardiac abnormality. Also, the ECG is highly subjective, the symptoms may appear at random in the time scale. Hence computer assisted methods can help physicians to monitor cardiac health easily and accurately. The ECG signal is nonlinear and non-stationary in nature. These subtle variations can be captured using non-linear dynamical Higher Order Statistics (HOS) techniques. Bispectrum is the third order spectra which captures information beyond mean and standard deviation. In this work we have analyzed five types of beats namely: Normal, Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). The extracted bispectrum features are subjected to principal component analysis (PCA) for dimensionality reduction. These principal components were fed to four layered feed forward neural network and Least Square-Support Vector Machine (LS-SVM) for automated pattern identification. In our work, we have obtained highest average accuracy of 93.48%, average sensitivity and specificity of 99.27% and 98.31% respectively using LS-SVM with Radial Basis Function (RBF) kernel. Our system is clinically ready to run on large amount of data sets. [Copyright &y& Elsevier]