Myocardial Infarction

Utility of convolutional neural network-enhanced electrocardiogram to diagnose and predict mitral regurgitation in patients with chronic atrial fibrillation.

A convolutional neural network (CNN)-enhanced electrocardiogram (ECG) has been reported for detecting mitral regurgitation (MR). This tool may be particularly useful for identifying candidates for echocardiography in patients with chronic atrial fibrillation (AF) to detect atrial functional MR early. The data from a single-center, prospective cohort study (Shinken Database 2010-2017, n = 19,170) w...

Enhancing ECG Classification in Cardiac Diagnostics: A Novel Approach Using Adaptive Focal Cross-Entropy Loss Function.

Heart disease is the leading cause of mortality globally. Electrocardiograms (ECGs) are standard instruments for the examination of heart conditions, but traditional analysis is time-consuming and prone to errors. Novel advances in artificial intelligence have improved ECG classification. However, some limitations remain, such as poor interpretability, computational cost, and class imbalance. This...

Role of Mobile Health and Wearable Devices in Cardiovascular Disease Prevention: A Comprehensive Review.

Cardiovascular disease (CVD) is a leading cause of death worldwide. A key area of interest in CVD prevention is novel digital health technologies, pri...

Invited Article: Al guided Dual Antiplatelet Therapy and Anticoagulation.

Artificial intelligence (AI) has emerged as a transformative tool in healthcare through data analysis, pattern recognition and predictive modeling cap...

A Review on Intelligent Systems for ECG Analysis: From Flexible Sensing Technology to Machine Learning.

This paper conducts an extensive review of flexible cardiac sensing devices designed for electrocardiogram (ECG) acquisitions, with emphasis on their ...

TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG.

Noninvasive fetal ECG (FECG) monitoring holds significant importance in ensuring the normal development of the fetus. Since FECG is usually submerged ...

Cardiac biomarkers N-terminal fragment of the prohormone B-type natriuretic peptide and cardiac troponin I for characterization of the cardiac disease phenotype of the English Bull Terrier.

The N-terminal fragment of prohormone B-type natriuretic peptide (NT-proBNP) and cardiac troponin I (cTnI) contribute information regarding cardiac lo...

Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates.

A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac di...

Effects of anthropomorphic features of co-robots on users' perception and emotional responses during human-robot collaboration.

Human-robot collaboration (HRC) is growing across sectors like manufacturing and healthcare. This study explores how anthropomorphic features, specifi...

Balancing privacy and health integrity: A novel framework for ECG signal analysis in immersive environments.

The widespread use of immersive technologies such as Virtual Reality, Mixed Reality, and Augmented Reality has led to the continuous collection and st...

Risk Prediction Models for Contrast-associated Acute Kidney Injury After Percutaneous Coronary Intervention: A Systematic Review.

The aim of this review was to systematically review published studies on risk prediction models for contrast-associated acute kidney injury (CA-AKI) i...

Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.

BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the im...

Comparison of machine learning models with conventional statistical methods for prediction of percutaneous coronary intervention outcomes: a systematic review and meta-analysis.

INTRODUCTION: Percutaneous coronary intervention (PCI) has been the main treatment of coronary artery disease (CAD). In this review, we aimed to compa...

FADE: Forecasting for anomaly detection on ECG.

BACKGROUND AND OBJECTIVE: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to ...

Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.

INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. Th...

AI analysis for ejection fraction estimation from 12-lead ECG.

Heart failure (HF) remains a leading global cause of cardiovascular deaths, with its prevalence expected to rise in the upcoming decade. Measuring the...

Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm.

With an increased chronic disease and an ageing population, remote health monitoring is a substantial method to enhance the care of patients and decre...

Risk Factors and Outcomes of Hemorrhagic Transformation in Acute Ischemic Stroke Following Thrombolysis: Analysis of a Single-Center Experience and Review of the Literature.

: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, providing valuable insights into hemorrhagic transforma...

Bi-variational physics-informed operator network for fractional flow reserve curve assessment from coronary angiography.

The coronary angiography-derived fractional flow reserve (FFR) curve, referred to as the Angio-FFR curve, is crucial for guiding percutaneous coronary...

Predicting prolonged length of in-hospital stay in patients with non-ST elevation myocardial infarction (NSTEMI) using artificial intelligence.

BACKGROUND: Patients presenting with non-ST elevation myocardial infarction (NSTEMI) are typically evaluated using coronary angiography and managed th...