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Cardiovascular diseases (CVDs) are main causes of death globally with coronary artery disease (CAD) being the most important. Timely diagnosis and treatment of CAD is crucial to reduce the incidence of CAD complications like myocardial infarction (MI) and ischemia-induced congestive heart failure (CHF). Electrocardiogram (ECG) signals are most commonly employed as the diagnostic screening tool to detect CAD. In this study, an automated system (AS) was developed for the automated categorization of electrocardiogram signals into normal, CAD, myocardial infarction (MI) and congestive heart failure (CHF) classes using convolutional neural network (CNN) and unique GaborCNN models. Weight balancing was used to balance the imbalanced dataset. High classification accuracies of more than 98.5% were obtained by the CNN and GaborCNN models respectively, for the 4-class classification of normal, coronary artery disease, myocardial infarction and congestive heart failure classes. GaborCNN is a more preferred model due to its good performance and reduced computational complexity as compared to the CNN model. To the best of our knowledge, this is the first study to propose GaborCNN model for automated categorizing of normal, coronary artery disease, myocardial infarction and congestive heart failure classes using ECG signals. Our proposed system is equipped to be validated with bigger database and has the potential to aid the clinicians to screen for CVDs using ECG signals., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Platelet gene polymorphisms are associated with variable on-treatment platelet reactivity and vary by race. Whether differences in platelet reactivity and aspirin or ticagrelor exist between African-American and European-Americans remains poorly understood. Biological samples from three prior prospective antiplatelet challenge studies at the Duke Clinical Research Unit were used to compare platelet reactivity between African-American and European-American subjects. Platelet reactivity at baseline, on-aspirin, on-ticagrelor, and the treatment effect of aspirin or ticagrelor were compared between groups using an adjusted mixed effects model. Compared with European-Americans (n = 282; 50% female; mean ± standard deviation age, 50 ± 16), African-Americans (n = 209; 67% female; age 48 ± 12) had lower baseline platelet reactivity with platelet function analyzer-100 (PFA-100) (p < 0.01) and with light transmission aggregometry (LTA) in response to arachidonic acid (AA), adenosine diphosphate (ADP), and epinephrine agonists (p < 0.05). African-Americans had lower platelet reactivity on aspirin in response to ADP, epinephrine, and collagen (p < 0.05) and on ticagrelor in response to AA, ADP, and collagen (p < 0.05). The treatment effect of aspirin was greater in European-Americans with an AA agonist (p = 0.002). Between-race differences with in vitro aspirin mirrored those seen in vivo. The treatment effect of ticagrelor was greater in European-Americans in response to ADP (p < 0.05) but with collagen, the treatment effect was greater for African-Americans (p < 0.05). Platelet reactivity was overall lower in African-Americans off-treatment, on aspirin, and on ticagrelor. European-Americans experienced greater platelet suppression on aspirin and on ticagrelor. The aspirin response difference in vivo and in vitro suggests a mechanism intrinsic to the platelet. Whether the absolute level of platelet reactivity or the degree of platelet suppression after treatment is more important for clinical outcomes is uncertain.

Hypertension (HPT), also known as high blood pressure, is a precursor to heart, brain or kidney diseases. Some symptoms of HPT include headaches, dizziness and fainting. The potential diagnosis of masked hypertension is of specific interest in this study. In masked hypertension (MHPT), the instantaneous blood pressure appears normal, but the 24-h ambulatory blood pressure is abnormal. Hence patients with MHPT are difficult to identify and thus remain untreated or are treated insufficiently. Hence, a computational intelligence tool (CIT) using electrocardiograms (ECG) signals for HPT and possible MHPT detection is proposed in this work. Empirical mode decomposition (EMD) is employed to decompose the pre-processed signals up to five levels. Nonlinear features are extracted from the five intrinsic mode functions (IMFs) thereafter. Student's t-test is subsequently applied to select a set of highly discriminatory features. This feature set is then input to various classifiers, in which, the best accuracy of 97.70% is yielded by the k-nearest neighbor (k-NN) classifier. The developed tool is evaluated by the 10-fold cross validation technique. Our findings suggest that the developed system is useful for diagnostic computational intelligence tool in hospital settings, and that it enables the automatic classification of HPT versus normal ECG signals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified during manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross-validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The heart muscle pumps blood to vital organs, which is indispensable for human life. Congestive heart failure (CHF) is characterized by the inability of the heart to pump blood adequately throughout the body without an increase in intracardiac pressure. The symptoms include lung and peripheral congestion, leading to breathing difficulty and swollen limbs, dizziness from reduced delivery of blood to the brain, as well as arrhythmia. Coronary artery disease, myocardial infarction, and medical co-morbidities such as kidney disease, diabetes, and high blood pressure all take a toll on the heart and can impair myocardial function. CHF prevalence is growing worldwide. It afflicts millions of people globally, and is a leading cause of death. Hence, proper diagnosis, monitoring and management are imperative. The importance of an objective CHF diagnostic tool cannot be overemphasized. Standard diagnostic tests for CHF include chest X-ray, magnetic resonance imaging (MRI), nuclear imaging, echocardiography, and invasive angiography. However, these methods are costly, time-consuming, and they can be operator-dependent. Electrocardiography (ECG) is inexpensive and widely accessible, but ECG changes are typically not specific for CHF diagnosis. A properly designed computer-aided detection (CAD) system for CHF, based on the ECG, would potentially reduce subjectivity and provide quantitative assessment for informed decision-making. Herein, we review existing CAD for automatic CHF diagnosis, and highlight the development of an ECG-based CAD diagnostic system that employs deep learning algorithms to automatically detect CHF., (Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The system was validated and tested with data from the MIT-BIH Atrial Fibrillation Database. It achieved 98.51% accuracy with 10-fold cross-validation (20 subjects) and 99.77% with blindfold validation (3 subjects). The proposed system structure is straight forward, because there is no need for information reduction through feature extraction. All the complexity resides in the deep learning system, which gets the entire information from a signal block. This setup leads to the robust performance for unknown data, as measured with the blind fold validation. The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart. To the best of our knowledge, the proposed system is the first to incorporate deep learning for AF beat detection., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: Patients with left ventricular (LV) systolic dysfunction, coronary artery disease (CAD), and angina are often thought to have a worse prognosis and a greater prognostic benefit from coronary artery bypass graft (CABG) surgery than those without angina., Objectives: This study investigated: 1) whether angina was associated with a worse prognosis; 2) whether angina identified patients who had a greater survival benefit from CABG; and 3) whether CABG improved angina in patients with LV systolic dysfunction and CAD., Methods: We performed an analysis of the STICH (Surgical Treatment for Ischemic Heart Failure) trial, in which 1,212 patients with an ejection fraction ≤35% and CAD were randomized to CABG or medical therapy. Multivariable Cox and logistic models were used to assess long-term clinical outcomes., Results: At baseline, 770 patients (64%) reported angina. Among patients assigned to medical therapy, all-cause mortality was similar in patients with and without angina (hazard ratio [HR]: 1.05; 95% confidence interval [CI]: 0.79 to 1.38). The effect of CABG was similar whether the patient had angina (HR: 0.89; 95% CI: 0.71 to 1.13) or not (HR: 0.68; 95% CI: 0.50 to 0.94; p interaction = 0.14). Patients assigned to CABG were more likely to report improvement in angina than those assigned to medical therapy alone (odds ratio: 0.70; 95% CI: 0.55 to 0.90; p < 0.01)., Conclusions: Angina does not predict all-cause mortality in medically treated patients with LV systolic dysfunction and CAD, nor does it identify patients who have a greater survival benefit from CABG. However, CABG does improve angina to a greater extent than medical therapy alone. (Comparison of Surgical and Medical Treatment for Congestive Heart Failure and Coronary Artery Disease [STICH]; NCT00023595)., (Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

The recent discovery of heterozygous human mutations that truncate full-length titin (TTN, an abundant structural, sensory, and signaling filament in muscle) as a common cause of end-stage dilated cardiomyopathy (DCM) promises new prospects for improving heart failure management. However, realization of this opportunity has been hindered by the burden of TTN-truncating variants (TTNtv) in the general population and uncertainty about their consequences in health or disease. To elucidate the effects of TTNtv, we coupled TTN gene sequencing with cardiac phenotyping in 5267 individuals across the spectrum of cardiac physiology and integrated these data with RNA and protein analyses of human heart tissues. We report diversity of TTN isoform expression in the heart, define the relative inclusion of TTN exons in different isoforms (using the TTN transcript annotations available at http://cardiodb.org/titin), and demonstrate that these data, coupled with the position of the TTNtv, provide a robust strategy to discriminate pathogenic from benign TTNtv. We show that TTNtv is the most common genetic cause of DCM in ambulant patients in the community, identify clinically important manifestations of TTNtv-positive DCM, and define the penetrance and outcomes of TTNtv in the general population. By integrating genetic, transcriptome, and protein analyses, we provide evidence for a length-dependent mechanism of disease. These data inform diagnostic criteria and management strategies for TTNtv-positive DCM patients and for TTNtv that are identified as incidental findings., (Copyright © 2015, American Association for the Advancement of Science.)

We report a young man who has persistent truncus arteriosus (TA), severe truncal regurgitation and unilateral pulmonary hypertension. Our patient had palliative main pulmonary artery (PA) banding done during infancy that was not followed by definitive corrective surgery. Unilateral irreversible left sided pulmonary hypertension developed due to migration of the PA band to the right PA. The patient presented to us with infective endocarditis of the truncal valve. This had resolved with medical treatment. Discussion was made on general management of TA and specific difficult management issues of palliated TA in adult, as found in our patient.