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The Electrocardiogram (ECG) is the P-QRS-T wave depicting the cardiac activity of the heart. The subtle changes in the electric potential patterns of repolarization and depolarization are indicative of the disease afflicting the patient. These clinical time domain features of the ECG waveform can be used in cardiac health diagnosis. Due to the presence of noise and minute morphological parameter values, it is very difficult to identify the ECG classes accurately by the naked eye. Various computer aided cardiac diagnosis (CACD) systems, analysis methods, challenges addressed and the future of cardiovascular disease screening are reviewed in this paper. Methods developed for time domain, frequency transform domain, and time-frequency domain analysis, such as the wavelet transform, cannot by themselves represent the inherent distinguishing features accurately. Hence, nonlinear methods which can capture the small variations in the ECG signal and provide improved accuracy in the presence of noise are discussed in greater detail in this review. A CACD system exploiting these nonlinear features can help clinicians to diagnose cardiovascular disease more accurately., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Electrocardiogram (ECG) is the electrical activity of the heart indicated by P, Q-R-S and T wave. The minute changes in the amplitude and duration of ECG depicts a particular type of cardiac abnormality. It is very difficult to decipher the hidden information present in this nonlinear and nonstationary signal. An automatic diagnostic system that characterizes cardiac activities in ECG signals would provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect cardiac abnormalities in ECG recordings. Application of higher order spectra (HOS) features is a seemingly promising approach because it can capture the nonlinear and dynamic nature of the ECG signals. In this paper, we have automatically classified five types of beats using HOS features (higher order cumulants) using two different approaches. The five types of ECG beats are normal (N), right bundle branch block (RBBB), left bundle branch block (LBBB), atrial premature contraction (APC) and ventricular premature contraction (VPC). In the first approach, cumulant features of segmented ECG signal were used for classification; whereas in the second approach cumulants of discrete wavelet transform (DWT) coefficients were used as features for classifiers. In both approaches, the cumulant features were subjected to data reduction using principal component analysis (PCA) and classified using three layer feed-forward neural network (NN) and least square-support vector machine (LS-SVM) classifiers. In this study, we obtained the highest average accuracy of 94.52%, sensitivity of 98.61% and specificity of 98.41% using first approach with NN classifier. The developed system is ready clinically to run on large datasets.

The electrocardiogram (ECG) is being commonly used as a diagnostic tool to distinguish different types of atrial tachyarrhythmias. The inherent complexity and mechanistic and clinical inter-relationships often brings about diagnostic difficulties to treating physicians and primary health care professionals creating frequent misdiagnoses and cross classifications using visual criteria. The current paper presents a methodology for ECG based pattern analysis for detection of atrial flutter, atrial fibrillation and normal sinus rhythm beats. ECG is an inherently non-linear and non-stationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. Routinely used time domain and frequency domain methods will not be able to capture the hidden information present in the ECG beats. In the present study, we have used non-linear features of higher order spectra (HOS) to differentiate the normal, atrial fibrillation and atrial flutter ECG beats. The bispectrum features were subjected to independent component analysis (ICA) for data reduction. The ICA coefficients were subsequently subjected to K-nearest-neighbor (KNN), classification and regression tree (CART) and neural network (NN) classifiers to evaluate the best automated classifier. We have obtained an average accuracy of 97.65%, sensitivity and specificity of 98.75% and 99.53% respectively using ten-fold cross validation. Overall, the results show that application of higher order spectra statistics is useful for the classification of atrial tachyarrhythmias with reasonably high accuracies. Further validation of the proposed technique will yield acceptable results for clinical implementation.

Purpose: Accurate detection of Atrial Fibrillation (AF) has great significance in the field of medical science which can reduce the rate of mortality and morbidity. The present study focuses on Electrocardiography (ECG) signal classification using dimensionality reduction techniques combined with R wave to R wave interval (RR interval) features. Materials and Methods: In the first approach, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and Probabilistic Principal Component Analysis (PPCA) are performed independently on denoised ECG signal using Discrete Wavelet Transform (DWT) for the classification of ECG signal. In the second approach, the dimensionality reduction techniques combined with RR interval features are used for the classification of ECG signal. Results: Machine Learning (ML) algorithms such as Decision Tree (DT), Support Vector Machine (SVM), and Deep Learning (DL) algorithms such as Long Short Term Memory (LSTM) and Bi-Directional LSTM (BiLSTM) are used for classification purposes. Conclusion: The proposed methodology provided an overall accuracy of 93.65% with PCA and LSTM classifier and an overall accuracy of 99.45% with PCA combined with RR interval features and LSTM classifier. The developed technology has potential applications in many practical solutions. [ABSTRACT FROM AUTHOR]

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) signal is a non-invasive method, used to diagnose the patients with cardiac abnormalities. The subjective evaluation of interval and amplitude of ECG by physician can be tedious, time consuming, and susceptible to observer bias. ECG signals are generated due to the excitation of many cardiac myocytes and hence resultant signals are non-linear in nature. These subtle changes can be well represented and discriminated in transform and non-linear domains. In this paper, performance of Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) methods are compared for automated diagnosis of five classes namely Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) beats. Six different approaches: (i) Principal Components (PCs) on DCT, (ii) Independent Components (ICs) on DCT, (iii) PCs on DWT, (iv) ICs on DWT, (v) PCs on EMD and (vi) ICs on EMD are employed in this work. Clinically significant features are selected using ANOVA test () and fed to k-Nearest Neighbor ( k-NN) classifier. We have obtained a classification accuracy of 99.77% using ICs on DWT method. Consistency of performance is evaluated using Cohen's kappa statistic. Developed approach is robust, accurate and can be employed for mass diagnosis of cardiac healthcare. [ABSTRACT FROM AUTHOR]

Atrial Fibrillation (A-Fib), Atrial Flutter (AFL) and Ventricular Fibrillation (V-Fib) are fatal cardiac abnormalities commonly affecting people in advanced age and have indication of life-threatening condition. To detect these abnormal rhythms, Electrocardiogram (ECG) signal is most commonly visualized as a significant clinical tool. Concealed non-linearities in the ECG signal can be clearly unraveled using Recurrence Quantification Analysis (RQA) technique. In this paper, RQA features are applied for classifying four classes of ECG beats namely Normal Sinus Rhythm (NSR), A-Fib, AFL and V-Fib using ensemble classifiers. The clinically significant () features are ranked and fed independently to three classifiers viz. Decision Tree (DT), Random Forest (RAF) and Rotation Forest (ROF) ensemble methods to select the best classifier. The training and testing of the feature set is accomplished using 10-fold cross-validation strategy. The RQA coefficients using ROF provided an overall accuracy of 98.37% against 96.29% and 94.14% for the RAF and DT, respectively. The results achieved evidently ratify the superiority of ROF ensemble classifier in the diagnosis of A-Fib, AFL and V-Fib. Precision of four classes is measured using class-specific accuracy (%) and reliability of the performance is assessed using Cohen's kappa statistic (). The developed approach can be used in therapeutic devices and help the physicians in automatic monitoring of fatal tachycardia rhythms. [ABSTRACT FROM AUTHOR]

Abstract: Electrocardiogram (ECG) is widely used as a diagnostic tool to identify atrial tachyarrhythmias such as atrial fibrillation. The ECG signal is a P-QRS-T wave representing the cardiac function. The minute variations in the durations and amplitude of these waves cannot be easily deciphered by the naked eye. Hence, there is a need for computer aided diagnosis (CAD) of cardiac healthcare. The current paper presents a methodology for ECG based pattern analysis of normal sinus rhythm and atrial fibrillation (AF) beats. The denoised and registered ECG beats were subjected to independent component analysis (ICA) for data reduction. The weights of ICA were used as features for classification using Naive Bayes and Gaussian mixture model (GMM) classifiers. The performance and the upper bound on probability of error in classification were analyzed using Chernoff and Bhattacharyya bounds. The Naive Bayes classifier provided an average sensitivity of 99.32%, specificity of 99.33% and accuracy of 99.33%, while the GMM provided an average sensitivity of 100%, specificity of 99% and accuracy of 99.42%. The probability of error during classification was less for GMM compared to Naive Bayes classifier (NBC) as GMM provided higher performance than the NBC. [Copyright &y& Elsevier]

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: Electrocardiogram is the P-QRS-T wave representing the cardiac depolarization and re-polarization, recorded at the body surface. The subtle changes in amplitude and duration of these waves indicate various pathological conditions. It is very difficult to decipher minute changes in the ECG wave by naked eye. Hence a computer aided diagnosis tool to classify various cardiac diseases will assist the doctors in their ECG reading. In this paper, five types of ECG beats (ANSI/AAMI EC57:1998 standard) of MIT–BIH arrhythmia database were automatically classified. Our proposed methodology involves computation of Discrete Cosine Transform (DCT) coefficients from the segmented beats of ECG, which were then subjected for principal component analysis for dimensionality reduction. Then the clinically significant principal components were fed to (i) feed forward neural network, (ii) least square support vector machine with different kernel functions, and (iii) Probabilistic Neural Network (PNN) for automatic classification. We have obtained the highest average sensitivity of 98.69%, specificity of 99.91%, and classification accuracy of 99.52% with the developed knowledge based system. The developed system is clinically ready to deploy for mass screening programs. [Copyright &y& Elsevier]

In this paper, an electrocardiogram (ECG)-based pattern analysis methodology is presented for the detection of artrial flutter and atrial fibrillation using fractal dimension (FD) of continuous wavelet transform (CWT) coefficients of raw ECG signals, sample entropy of heart beat interval time series, and mean heart beat interval features. Accurate diagnosis of atrial tachyarrhythmias is important, as they have different therapeutic options and clinical decisions. In view of this, we have made an attempt to develop a discrimination mechanism between artrial flutter and atrial fibrillation. The methodology consists of mean heart beat interval detection using Pan Tompkins algorithm, calculation of sample entropy of heart beat interval time series, computation of box counting FD from CWT coefficients of raw ECG, statistical significance test, and subsequent pattern classification using different classifiers. Different wavelet basis functions like Daubechies-4, Daubechies-6, Symlet-2, Symlet-4, Symlet-6, Symlet-8, Coiflet-2, Coiflet-5, Biorthogonal-1.3, Biorthogonal-3.1, and Mayer wavelet have been used to compute CWT coefficients. Features are evaluated using statistical analysis and subsequently two-class pattern classification is done using unsupervised (k-means, fuzzy c-means, and Gaussian mixture model) and supervised (error back propagation neural network and support vector machine) techniques. In order to reduce the bias in choosing the training and testing set, k-fold cross validation is used. The obtained results are compared and discussed. It is found that the supervised classifiers provide higher accuracy in comparison to the set of unsupervised classifiers. [ABSTRACT FROM AUTHOR]

Abstract: Electrocardiogram (ECG) is the P, QRS, T wave indicating the electrical activity of the heart. The subtle changes in amplitude and duration of ECG cannot be deciphered precisely by the naked eye, hence imposing the need for a computer assisted diagnosis tool. In this paper we have automatically classified five types of ECG beats of MIT-BIH arrhythmia database. The five types of beats are Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). In this work, we have compared the performances of three approaches. The first approach uses principal components of segmented ECG beats, the second approach uses principal components of error signals of linear prediction model, whereas the third approach uses principal components of Discrete Wavelet Transform (DWT) coefficients as features. These features from three approaches were independently classified using feed forward neural network (NN) and Least Square-Support Vector Machine (LS-SVM). We have obtained the highest accuracy using the first approach using principal components of segmented ECG beats with average sensitivity of 99.90%, specificity of 99.10%, PPV of 99.61% and classification accuracy of 98.11%. The system developed is clinically ready to deploy for mass screening programs. [Copyright &y& Elsevier]

The sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P-QRS-T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P-QRS-T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis. [ABSTRACT FROM AUTHOR]

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers. [ABSTRACT FROM AUTHOR]

Abstract: An automatic classifier for electrocardiogram (ECG) based cardiac abnormality detection using Gaussian mixture model (GMM) is presented here. In first stage, pre-processing that includes re-sampling, QRS detection, linear prediction (LP) model estimation, residual error signal computation and principal component analysis (PCA) has been used for registration of linearly independent ECG features. GMM is here used for classification based on the registered features in a two-class pattern classification problem using 730 ECG segments from MIT-BIH Arrhythmia and European ST-T Ischemia datasets. A set of 12 features explaining 99.7% of the data variability is obtained using PCA from residual error signals for GMM based classification. Sixty percent of the data is used for training the classifier and 40% for validating. It is observed that the overall accuracy of the proposed strategy is 94.29%. As an advantage, it is also verified that Chernoff bound and Bhattacharya bounds lead to minimum error for GMM based classifier. In addition, a comparative study is done with the standard classification techniques with respect to its overall accuracy. [Copyright &y& Elsevier]

Abstract: Coronary Artery Disease (CAD) is the narrowing of the blood vessels that supply blood and oxygen to the heart. Electrocardiogram (ECG) is an important cardiac signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insights into the state of health and nature of the disease afflicting the heart. However, it is very difficult to perceive the subtle changes in ECG signals which indicate a particular type of cardiac abnormality. Hence, we have used the heart rate signals from the ECG for the diagnosis of cardiac health. In this work, we propose a methodology for the automatic detection of normal and Coronary Artery Disease conditions using heart rate signals. The heart rate signals are decomposed into frequency sub-bands using Discrete Wavelet Transform (DWT). Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were applied on the set of DWT coefficients extracted from particular sub-bands in order to reduce the data dimension. The selected sets of features were fed into four different classifiers: Support Vector Machine (SVM), Gaussian Mixture Model (GMM), Probabilistic Neural Network (PNN) and K-Nearest Neighbor (KNN). Our results showed that the ICA coupled with GMM classifier combination resulted in highest accuracy of 96.8%, sensitivity of 100% and specificity of 93.7% compared to other data reduction techniques (PCA and LDA) and classifiers. Overall, compared to previous techniques, our proposed strategy is more suitable for diagnosis of CAD with higher accuracy. [Copyright &y& Elsevier]

Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence, there is a need of computer aided techniques to be applied for the ECG signal analysis process. Going further, there is a need for incorporating this computerized software into the ECG equipment, so as to enable automated detection of MIs in clinics. Therefore, this paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis. In our study, a total of 200 twelve lead ECG subjects (52 normal and 148 with MI) involving 611,405 beats (125,652 normal beats and 485,753 beats of MI ECG) are segmented from the 12 lead ECG signals. Firstly, ECG signal obtained from 12 ECG leads are subjected to discrete wavelet transform (DWT) up to four levels of decomposition. Then, 12 nonlinear features namely, approximate entropy ( E a x ), signal energy ( Ω x ), fuzzy entropy ( E f x ), Kolmogorov–Sinai entropy ( E k s x ), permutation entropy ( E p x ), Renyi entropy ( E r x ), Shannon entropy ( E s h x ), Tsallis entropy ( E t s x ), wavelet entropy ( E w x ), fractal dimension ( F D x ), Kolmogorov complexity ( C k x ), and largest Lyapunov exponent ( E L L E x ) are extracted from these DWT coefficients. The extracted features are then ranked based on the t value. Then these features are fed into the k -nearest neighbor (KNN) classifier one by one to get the highest classification performance by using minimum number of features. Our proposed method has achieved the highest average accuracy of 98.80%, sensitivity of 99.45% and specificity of 96.27% in classifying normal and MI ECG (two classes), by using 47 features obtained from lead 11 (V 5 ). We have also obtained the highest average accuracy of 98.74%, sensitivity of 99.55% and specificity of 99.16% in differentiating the 10 types of MI and normal ECG beats (11 class), by using 25 features obtained from lead 9 (V 3 ). In addition, our study results achieved an accuracy of 99.97% in locating inferior posterior infarction by using only lead 9 (V 3 ) ECG signal. Our proposed method can be used as an automated diagnostic tool for (i) the detection of different (10 types of) MI by using 12 lead ECG signal, and also (ii) to locate the MI by analyzing only one lead without the need to analyze other leads. Thus, our proposed algorithm and computerized system software (incorporated into the ECG equipment) can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision. [ABSTRACT FROM AUTHOR]

Abstract: Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD. [Copyright &y& Elsevier]