Myocardial Infarction

Artificial-intelligence-enhanced ECG: a potential solution to limitations of ECG in detecting left ventricular dysfunction after chemotherapy.

ZnO nanoflower-mediated paper-based electrochemical biosensor for perfect classification of cardiac biomarkers with physics-informed machine learning.

The widespread exposure of acute myocardial infarction globally demands an ultrasensitive, rapid, an...

Synthetic ECG signal generation using generative neural networks.

Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases....

A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods.

BACKGROUND: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular con...

Automated Identification of Stroke Thrombolysis Contraindications from Synthetic Clinical Notes: A Proof-of-Concept Study.

INTRODUCTION: Timely thrombolytic therapy improves outcomes in acute ischemic stroke. Manual chart r...

Extraction of fetal heartbeat locations in abdominal phonocardiograms using deep attention transformer.

Assessing fetal health traditionally involves techniques like echocardiography, which require skille...

Automated detection of arrhythmias using a novel interpretable feature set extracted from 12-lead electrocardiogram.

The availability of large-scale electrocardiogram (ECG) databases and advancements in machine learni...

Multimodal machine learning for deception detection using behavioral and physiological data.

Deception detection is crucial in domains like national security, privacy, judiciary, and courtroom ...

A deep Bi-CapsNet for analysing ECG signals to classify cardiac arrhythmia.

- In recent times, the electrocardiogram (ECG) has been considered as a significant and effective sc...

A brief history and clinical use of robotic procedures in the cardiovascular system.

Robotic-assisted percutaneous coronary intervention (r-PCI) exemplifies the advancement of intervent...

Machine learning analysis of integrated ABP and PPG signals towards early detection of coronary artery disease.

Every year, Coronary Artery Disease (CAD) claims lives of over a million people. CAD occurs when the...

Reconstruction of ECG from ballistocardiogram using generative adversarial networks with attention.

Electrocardiogram (ECG) is widely used to provide early warning signals for cardiovascular diseases....

A deep-learning model to predict the completeness of cytoreductive surgery in colorectal cancer with peritoneal metastasis☆.

BACKGROUND: Colorectal cancer (CRC) with peritoneal metastasis (PM) is associated with poor prognosi...

Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram.

Portable and wearable electrocardiogram (ECG) devices are increasingly utilized in healthcare for mo...

An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels.

Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for b...

Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery.

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation stra...

Rapid Blood Clot Removal via Remote Delamination and Magnetization of Clot Debris.

Micro/nano-scale robotic devices are emerging as a cutting-edge approach for precision intravascular...

Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks.

Detection and classification of cardiovascular diseases are crucial for early diagnosis and predicti...

A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform.

This study presents a novel hybrid deep learning model for arrhythmia classification from electrocar...

Joint fusion of EHR and ECG data using attention-based CNN and ViT for predicting adverse clinical endpoints in percutaneous coronary intervention patients.

Predicting post-Percutaneous Coronary Intervention (PCI) outcomes is crucial for effective patient m...

Circadian rhythm modulation in heart rate variability as potential biomarkers for major depressive disorder: A machine learning approach.

Major depressive disorder (MDD) is associated with reduced heart rate variability (HRV), but its lin...