Myocardial Infarction

Four-electrode ECG reconstruction using anatomically grounded synthetic leads: a physiological measurement framework with hybrid CNN-transformer mapping.

The standard 12-lead electrocardiogram (ECG) remains essential for cardiac diagnosis but requires ten physical electrodes, limiting long-term and wearable monitoring applications. We developed an anatomically grounded and physiologically interpretable framework to reconstruct the complete 12-lead ECG from four synthetic chest-torso electrodes derived using geometric vector principles and card...

The Harvard-Emory ECG Database.

The Harvard-Emory ECG Database (HEEDB) is currently the largest open-access collection of 12-lead electrocardiogram (ECG) recordings, developed through a collaboration between Harvard and Emory University. The database consists of 10,608,417 ECG recordings from 1,818,247 patients from Massachusetts General Hospital (MGH) and 998,844 recordings from 349,548 patients from Emory University Hospital (...

AI-ECG classification for Brugada syndrome: A study of machine learning techniques to optimise for limited datasets.

Deep neural networks can classify ECGs with high accuracy when training data is abundant. Rare conditions like Brugada syndrome, an inherited arrhythm...

Non-pharmacological treatment of AECOPD.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) are acute events characterized by rapid worsening of dyspnea, cough, and sputum...

Robust learning framework for a scalable remote monitoring of autonomic dysreflexia: use-case in spinal cord injury.

The autonomous nervous system (ANS) response in neurological disorders is a direct modifiable risk factor for cardiovascular health, however, difficul...

Machine learning models enhance detection of arrhythmogenic right ventricular cardiomyopathy.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable cardiac disorder associated with sudden cardiac death, yet its diagnosis remains...

Reliable detection of focal onset impaired awareness seizures in patients with epilepsy using wearable ECG: Development and validation study.

BACKGROUND: Underreporting of seizures, particularly focal onset impaired awareness seizures (FIAS), compromises the effectiveness of patient care and...

High Sensitivity Cardiac Troponin I Detection via MP-Locked Aptamer and Multimeric DNAzyme-Coupled Hyperbranched Hybridization Chain Reaction.

Timely and sensitive detection of cardiac troponin I (cTnI) is critical for early diagnosis of myocardial infarction, particularly at the point-of-car...

Artificial Intelligence-Enabled ECG Analysis to Predict Incident Heart Failure.

BACKGROUND: ECG-based artificial intelligence may enable efficient prediction of incident heart failure (HF) risk to facilitate preventive efforts. Pr...

AI-Based STroke Risk fActor Classification and Treatment (ABSTRACT) study.

BACKGROUND: Stroke is a leading cause of death and disability worldwide, costing the UK approximately £26 billion annually. While lifestyle modificati...

First-line risk stratification with machine learning models facilitates rapid triage for non-ST-elevation myocardial infarction.

Timely diagnosis of non-ST-elevation myocardial infarction (NSTEMI) remains challenging, as current protocols rely on serial high-sensitivity cardiac ...

A real-time detection framework for road cracks in noisy and morphologically complex environments.

An automated road defect detection system is a key part of intelligent traffic infrastructure maintenance. Existing object detection models have slow ...

Inadequate Preventive Dental Care Is Associated With Higher Cardiovascular Risk Identified by ECG-Based Artificial Intelligence Algorithms.

BACKGROUND: Inadequate preventive dental care may contribute to inflammatory conditions such as periodontitis, increasing cardiovascular disease risk....

Higher artificial intelligence-ECG atrial fibrillation prediction model output and estimated physiologic aging predict higher risk of adverse vascular events in patients with migraine.

OBJECTIVE: To investigate the ability of artificial intelligence-enabled electrocardiogram (AI-ECG) atrial fibrillation (AF) prediction model output a...

Interpretable machine learning for predicting in-hospital bleeding after PCI in patients with diabetes and acute coronary syndrome: a retrospective cohort study.

Assessment of Predictive Factors That Shorten Duration of Treatment in Patients With Multiple Myeloma Using AI: Real-World Longitudinal Study Using Data From Medical Data Vision Claims Database.

BACKGROUND: With the availability of newer therapies, the duration of therapy (DoT) shortens with each increasing line of treatment in Japanese patien...

Clinical History, Spirometry, and CT Features Can Predict Dyspnea in Smokers with and without Spirometry-Defined COPD.

Interpretable Machine Learning for Stroke Recovery: Predicting Discharge and 3-Month Functional Outcomes.

IntroductionStroke is a leading cause of disability worldwide. This study uses Machine Learning models to investigate factors influencing modified Ran...

Investigating explainable arrhythmia classification using class-specific ensemble model from segmented ECG signals.

Accurate arrhythmia classification from short clinical ECGs is hard to achieve and explain. Prior studies are often single-lead, use uniform model fus...

Predicting cardiopulmonary exercise testing outcomes in congenital heart disease through multimodal data integration and geometric learning.

Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables including ...