Myocardial Infarction

Towards seamless myocardial infarction and cardiac rhythm detection: a deep learning approach with automated paper-electrocardiogram digitisation.

PURPOSE: In India, myocardial infarction (MI) is a significant cause of mortality related to cardiovascular diseases. Timely diagnosis is critical for addressing this issue. While prior studies have concentrated on digital electrocardiogram (ECG) data, the presence of noise and artifacts in paper electrocardiogram is not well addressed in the literature. So, it is important to incorporate a more d...

Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.

The validation of promising clinical biomarkers, molecular mechanisms, and novel drug targets in cardiovascular disease (CVD) is hindered by a vast and fragmented biomedical literature, which now exceeds 38 million publications indexed in PubMed. To address the central challenge of navigating and synthesizing a huge fragmented biomedical literature base, we applied our validated machine learning-b...

XNet: Enhancing Physical Activity Intensity Assessment With Attentional Multidomain Fusion and Visual Analytics.

Sedentary behavior (SB) is a major global health concern, necessitating accurate physical activity (PA) intensity monitoring. Conventional machine-lea...

Multimodal Wearable Sensor-Based Stress Detection: Machine Learning Pipeline with Systematic Feature Selection and Key Biomarker Insights.

The increasing awareness of stress-related health impacts has driven demand for accurate, non-invasive stress detection methods, particularly those le...

Toward robust automated cardiovascular arrhythmia detection using self-supervised learning and 1-dimensional vision transformers.

Cardiovascular diseases are the leading cause of death worldwide. With electrocardiogram (ECG) machines becoming more accessible, passive monitoring f...

Artificial intelligence-enabled electrocardiography to predict impending episodes of ventricular tachycardia in individuals under continuous heart rhythm monitoring.

This is a protocol for a Cochrane Review (prognosis). The objectives are as follows: To identify and evaluate AI-based prognostic models, in developme...

BioFPT: Biosignal Feature Pyramid Transformer for self-supervised representation learning from ECGsignals.

Electrocardiogram (ECG) analysis represents a promising field for deep learning applications in clinical diagnostics. However, practical use of curren...

Feasibility of Implicit Neural Representation Learned Motion Compensation for 3D Stack-of-Spirals Free-Breathing Cardiac Quantitative Susceptibility Mapping.

PURPOSE: Differential blood oxygenation between the right and left heart (ΔSO2) is an indicator of cardiovascular function currently assessed in clini...

Hybrid Residual Neural Network for Obstructive Sleep Apnea Detection Using ECG Scalogram.

The electrocardiogram (ECG) has emerged as a viable alternative to polysomnography (PSG) for the detection of obstructive sleep apnea (OSA). Given the...

Adversarial Debiasing for Equitable and Fair Detection of Acute Coronary Syndrome Using 12-Lead ECG.

OBJECTIVE: Acute coronary syndrome (ACS) is a life-threatening condition requiring accurate diagnosis for better outcomes. However, variability in sig...

Artificial Intelligence-Enhanced Electrocardiography for Predicting Paroxysmal Atrial Fibrillation From Sinus Rhythm: Impact of Data Integration Across Institutions and Devices.

BACKGROUND: Artificial intelligence (AI)-enhanced electrocardiography (ECG) has been developed to detect paroxysmal atrial fibrillation (AF) from sinu...

Updates in Central Retinal Artery Occlusion.

BACKGROUND: Central retinal artery occlusion (CRAO) is a vision-threatening neuro-ophthalmic emergency, analogous to acute ischemic stroke. Delayed pr...

Automated detection of Chagas disease from ECG signals using wavelet scattering transform and RUSBoost classifier.

Early diagnosis of Chagas disease plays a vital role in enabling timely treatment and reducing the likelihood of underlying severe cardiovascular comp...

Deep Learning Based Approach for Lossless ECG Compression.

OBJECTIVE: Tele-monitoring is a useful platform for remote monitoring of cardiac patients, where compression plays a significant role in reducing the ...

Evaluation of artificial intelligence-based electrocardiogram analysis tools in patients with hypertrophic cardiomyopathy.

AIMS: Artificial intelligence (AI)-based electrocardiogram (ECG) analysis tools have shown promise in detecting various cardiac conditions. However, t...

Two stage fine-tuned multimodal generative AI for automated ECG based cardiovascular report generation.

The increasing prevalence of cardiovascular diseases (CVDs) calls for innovative diagnostic solutions that are both accurate and scalable. ElectroCard...

MEETI: A Multimodal ECG Dataset from MIMIC-IV-ECG with Signals, Images, Features and Interpretations.

Electrocardiograms (ECGs) are essential for diagnosing arrhythmias, myocardial ischemia, and conduction disorders. While machine learning has achieved...

Longitudinal Strain by Artificial Intelligence-Driven Automated Strain Analysis for Left Ventricular Function Evaluation and Infarct Region Estimation.

PURPOSE: Accurate evaluation of left ventricular (LV) dysfunction and infarct localization in acute myocardial infarction (AMI) remains challenging du...

A joint CNN-Bi-LSTM-transformer architecture with SHAP explanations for multi-label arrhythmia detection from 12-lead ECGs.

Interpretable, automated Artificial Intelligence (AI) solutions are essential for accurate 12-lead electrocardiogram (ECG) arrhythmia classification b...

Explainable machine learning framework based on blood biomarkers and routine health information for assistive diagnosis and risk stratification of acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) presents a critical clinical challenge due to its rapid progression and high mortality, compounded by di...