This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is ...one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the world. Although automatic analysis of ECG signal is very popular, current methods are not satisfactory. The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias. Described research are based on 1000 ECG signal fragments from the MIT - BIH Arrhythmia database for one lead (MLII) from 45 persons. Approach based on the analysis of 10-s ECG signal fragments (not a single QRS complex) is applied (on average, 13 times less classifications/analysis). A complete end-to-end structure was designed instead of the hand-crafted feature extraction and selection used in traditional methods. Our main contribution is to design a new 1D-Convolutional Neural Network model (1D-CNN). The proposed method is 1) efficient, 2) fast (real-time classification) 3) non-complex and 4) simple to use (combined feature extraction and selection, and classification in one stage). Deep 1D-CNN achieved a recognition overall accuracy of 17 cardiac arrhythmia disorders (classes) at a level of 91.33% and classification time per single sample of 0.015 s. Compared to the current research, our results are one of the best results to date, and our solution can be implemented in mobile devices and cloud computing.
•New approach based on long-duration (10 s) ECG signal fragments based on one lead is proposed.•Involves 17 ECG classes (normal sinus rhythm, 15 cardiac arrhythmias, pacemaker rhythm).•1D-CNN is employed.•Obtained overal accuracy of 91.33%.•Can be used in tele-medicine especially in mobile devices and cloud computing due to its low computational complexity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
Ischemic heart disease (IHD) is the leading cause of disability and mortality worldwide. Reactive oxygen species (ROS) have been shown to play key roles in the progression of diabetes, hypertension, ...and hypercholesterolemia, which are independent risk factors that lead to atherosclerosis and the development of IHD. Engineered biomaterial‐based nanomedicines are under extensive investigation and exploration, serving as smart and multifunctional nanocarriers for synergistic therapeutic effect. Capitalizing on cell/molecule‐targeting drug delivery, nanomedicines present enhanced specificity and safety with favorable pharmacokinetics and pharmacodynamics. Herein, the roles of ROS in both IHD and its risk factors are discussed, highlighting cardiovascular medications that have antioxidant properties, and summarizing the advantages, properties, and recent achievements of nanomedicines that have ROS scavenging capacity for the treatment of diabetes, hypertension, hypercholesterolemia, atherosclerosis, ischemia/reperfusion, and myocardial infarction. Finally, the current challenges of nanomedicines for ROS‐scavenging treatment of IHD and possible future directions are discussed from a clinical perspective.
The current developments, achievements, challenges, and future directions of reactive oxygen species (ROS)‐scavenging nanomedicine for the treatment of ischemic heart disease (IHD), as well as their risk factors, are reviewed. The properties, generation, and physiological roles of ROS in the process of IHD development and progression are discussed.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•High performance classification of arrhythmia types with LSTM and CAE-LSTM based structures.•Signal sizes of arrhythmic beats are reduced by convolutional autoencoders.•ECG signals were compressed ...by an average 0.70% PRD rate, and an accuracy of over 99.0%.•Significant improvement in time cost of LSTM networks for ECG data analysis.
For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.
A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
The role of myocardial viability assessment in identifying patients with ischemic cardiomyopathy who will benefit from surgical revascularization is controversial. This study assessed myocardial ...viability and its relationship to long-term outcomes in 601 patients with ischemic cardiomyopathy who were assigned to surgical revascularization plus medical therapy or medical therapy alone.
Myocardial infarction (MI) accounts for a high number of deaths globally. In acute MI, accurate electrocardiography (ECG) is important for timely diagnosis and intervention in the emergency setting. ...Machine learning is increasingly being explored for automated computer-aided ECG diagnosis of cardiovascular diseases. In this study, we have developed DenseNet and CNN models for the classification of healthy subjects and patients with ten classes of MI based on the location of myocardial involvement. ECG signals from the Physikalisch-Technische Bundesanstalt database were pre-processed, and the ECG beats were extracted using an R peak detection algorithm. The beats were then fed to the two models separately. While both models attained high classification accuracies (more than 95%), DenseNet is the preferred model for the classification task due to its low computational complexity and higher classification accuracy than the CNN model due to feature reusability. An enhanced class activation mapping (CAM) technique called Grad-CAM was subsequently applied to the outputs of both models to enable visualization of the specific ECG leads and portions of ECG waves that were most influential for the predictive decisions made by the models for the 11 classes. It was observed that Lead V4 was the most activated lead in both the DenseNet and CNN models. Furthermore, this study has also established the different leads and parts of the signal that get activated for each class. This is the first study to report features that influenced the classification decisions of deep models for multiclass classification of MI and healthy ECGs. Hence this study is crucial and contributes significantly to the medical field as with some level of visible explainability of the inner workings of the models, the developed DenseNet and CNN models may garner needed clinical acceptance and have the potential to be implemented for ECG triage of MI diagnosis in hospitals and remote out-of-hospital settings.
•This is the first study to explain the inner workings of the DenseNet and CNN models developed for MI detection.•DenseNet is a better model than CNN, for rapid classification of MI.•Model is developed with ten-fold cross-validation. Hence, it is robust and accurate.•Obtained high accuracy of 98.9% for the classification of ten MI classes with DenseNet model.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that ...refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.
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•Convolutional neural network (CNN) is used to classify 5 ECG classes.•9-layer deep CNN is implemented.•Generated synthetic data to overcome imbalance problem.•Accuracy of 94.03% and 93.47% with and without noise removal respectively.
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Abnormality of the cardiac conduction system can induce arrhythmia ― abnormal heart rhythm ― that can frequently lead to other cardiac diseases and complications, and are sometimes life-threatening. ...These conduction system perturbations can manifest as morphological changes on the surface electrocardiographic (ECG) signal. Assessment of these morphological changes can be challenging and time-consuming, as ECG signal features are often low in amplitude and subtle. The main aim of this study is to develop an automated computer aided diagnostic (CAD) system that can expedite the process of arrhythmia diagnosis, as an aid to clinicians to provide appropriate and timely intervention to patients. We propose an autoencoder of ECG signals that can diagnose normal sinus beats, atrial premature beats (APB), premature ventricular contractions (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB). Apart from the first, the rest are morphological beat-to-beat elements that characterize and constitute complex arrhythmia. The novelty of this work lies in how we modified the U-net model to perform beat-wise analysis on heterogeneously segmented ECGs of variable lengths derived from the MIT-BIH arrhythmia database. The proposed system has demonstrated self-learning ability in generating class activations maps, and these generated maps faithfully reflect the cardiac conditions in each ECG cardiac cycle. It has attained a high classification accuracy of 97.32% in diagnosing cardiac conditions, and 99.3% for R peak detection using a ten-fold cross validation strategy. Our developed model can help physicians to screen ECG accurately, potentially resulting in timely intervention of patients with arrhythmia.
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•Novel modified U-net model is proposed to classify nomal, APB, PVC, LBBB and RBBB ECG beats.•Heterogeneously segmented ECGs of variable lengths are used.•Developed model demonstrates self-learning ability in generating class activations maps.•These generated maps faithfully reflects the cardiac conditions in each ECG cardiac cycle.•Obtained classification accuracy of 97.32% and 99.3% for diagnosing cardiac conditions and R peak detection respectively.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
β-blockers are a heterogeneous class, with individual agents distinguished by selectivity for β
- vs. β
- and α-adrenoceptors, presence or absence of partial agonist activity at one of more ...β-receptor subtype, presence or absence of additional vasodilatory properties, and lipophilicity, which determines the ease of entry the drug into the central nervous system. Cardioselectivity (β
-adrenoceptor selectivity) helps to reduce the potential for adverse effects mediated by blockade of β
-adrenoceptors outside the myocardium, such as cold extremities, erectile dysfunction, or exacerbation of asthma or chronic obstructive pulmonary disease. According to recently updated guidelines from the European Society of Hypertension, β-blockers are included within the five major drug classes recommended as the basis of antihypertensive treatment strategies. Adding a β-blocker to another agent with a complementary mechanism may provide a rational antihypertensive combination that minimizes the adverse impact of induced sympathetic overactivity for optimal blood pressure-lowering efficacy and clinical outcomes benefit.
The world has been suffering from the COVID-19 pandemic since 2019. More than 5 million people have died. Pneumonia is caused by the COVID-19 virus, which can be diagnosed using chest X-ray and ...computed tomography (CT) scans. COVID-19 also causes clinical and subclinical cardiovascular injury that may be detected on electrocardiography (ECG), which is easily accessible.
For ECG-based COVID-19 detection, we developed a novel attention-based 3D convolutional neural network (CNN) model with residual connections (RC). In this paper, the deep learning (DL) approach was developed using 12-lead ECG printouts obtained from 250 normal subjects, 250 patients with COVID-19 and 250 with abnormal heartbeat. For binary classification, the COVID-19 and normal classes were considered; and for multiclass classification, all classes. The ECGs were preprocessed into standard ECG lead segments that were channeled into 12-dimensional volumes as input to the network model. Our developed model comprised of 19 layers with three 3D convolutional, three batch normalization, three rectified linear unit, two dropouts, two additional (for residual connections), one attention, and one fully connected layer. The RC were used to improve gradient flow through the developed network, and attention layer, to connect the second residual connection to the fully connected layer through the batch normalization layer.
A publicly available dataset was used in this work. We obtained average accuracies of 99.0% and 92.0% for binary and multiclass classifications, respectively, using ten-fold cross-validation. Our proposed model is ready to be tested with a huge ECG database.
•Automated classification of abnormal heartbeat, Covid-19 and normal groups using 12-lead ECG signals.•3D CNN model comprising of attention mechanism and residual connections is employed.•Variational autoencoder is used for data augmentation.•Obtained average accuracies of 99.0% for binary classification and 92.0% for multiclass classifications.
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Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be ...challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG.
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•Classification of 5-classes arrhythmias using variable length heart beats.•Five classes are Normal, PVC, LBBB, APB, RBBB.•Employed combination of CNN and LSTM techniques.•Noise filtering, feature extraction and selection are not required.•Obtained 98.10% accuracy, 97.50% sensitivity and 98.70% specificity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
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