Myocardial Infarction

A novel method for EKG anomaly detection based on the double sliding window technique.

Traditional detection methods often rely on fixed thresholding or machine-learning models, which can be computationally expensive. This study introduces a double-sliding-window technique to detect anomalies in ECG signals through adaptive-signal analysis. This method uses two independent sliding windows to dynamically track signal variations, enabling real-time identification of deviations in hear...

Differentiating HFmr/rEF from HFpEF using standard 12-lead ECG measurements: an interpretable machine learning study.

BACKGROUND: Differentiating heart failure (HF) with mildly reduced/reduced ejection fraction (HFmr/rEF) from HF with preserved ejection fraction (HFpEF) guides therapy but echocardiography may be delayed or unavailable. We developed and validated machine learning models using routine 12-lead ECG data to classify HF phenotypes. METHODS: In this retrospective cohort of hospitalised patients with HF,...

Machine learning prediction of physician-assigned treatment categories from pre-procedural electronic medical record data in cardiac patients: A multiclass classification study.

Cardiovascular diseases (CVDs) remain a leading source of morbidity, mortality, and healthcare burden worldwide. In patients with coronary artery dise...

Machine Learning-Based Early Risk Stratification for Sepsis-Related Troponin-Defined Myocardial Injury Using Routine Metabolic Indicators.

BACKGROUND: Early recognition of sepsis-related myocardial injury during sepsis remains difficult, partly because harmonized echocardiographic phenoty...

A covalently adaptive polymer welding strategy enabling integrated electronics for pre-hospital monitoring of myocardial infarction.

Myocardial infarction (MI) is a major contributor to cardiovascular diseases (CVDs), creating an urgent demand for wireless, real-time, and continuous...

Comparative Interpretation of Pediatric Electrocardiograms by Large Language Models and a Pediatrician.

OBJECTIVE: Large language models (LLMs) have been explored for clinical applications, yet their reliability in pediatric electrocardiogram (ECG) inter...

Non-invasive arterial blood pressure waveform generation in critically ill patients: A sensor-based deep learning approach.

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of t...

Automated analysis of wearable ECG: machine learning methods, preprocessing pipelines, and benchmark datasets.

Wearable and mobile electrocardiography (ECG) has rapidly expanded access to rhythm monitoring outside of clinical settings, but the single-or few-lea...

Serum 1H NMR-Based Untargeted Metabolomics for Noninvasive Early Detection of Breast Cancer.

Breast cancer (BC) is the most common malignancy among women, and late-stage presentation remains common in Asia, highlighting the need for affordable...

A Dual-Modal Wearable PPG Smartwatch with AI-Enhanced Correction for High-Accuracy and Continuous AF Burden Assessment.

Atrial fibrillation (AF) increases the risk of stroke and heart failure, yet accurate quantification of AF burden in daily life remains difficult. Alt...

Transformer-DAPT: AI-based dynamic assessment of ischemic and bleeding risks in patients on DAPT following PCI.

Dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) is traditionally guided by rule-based scores providing static, sin...

Paintable on-skin dry electrodes with robust skin and device connection for wireless sensing and human-machine interfaces.

Reliable and continuous electrophysiological recording is important for health monitoring and human-machine interactions. However, most existing epide...

Deep Learning Models Capture Umbrella Sampling-Derived Energetic Trends: A Troponin C Case Study.

Deep learning models have transformed several fields lately. In the past, capturing thermodynamic trends from free energies has relied on computationa...

Impact of an AI algorithm for multi-day prediction of incident atrial fibrillation on clinical decision-making: PROVISION-AF study.

Atrial fibrillation (AF) is frequently asymptomatic and often remains undetected until complications arise. Although artificial intelligence (AI)-enab...

Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease.

Portable, scalable, and accessible artificial intelligence (AI)-enabled smartwatch technology shows promise as a cardiovascular risk stratification st...

A three-level risk stratification algorithm for arrhythmia based on CNN-LSTM palindromic structure.

Cardiovascular diseases are characterized by sudden onset, high mortality rates, and high recurrence rates, making early screening and timely interven...

AI-enhanced electrocardiography as a digital biomarker platform in cardiovascular medicine: clinical applications, validation gaps and future implementation pathways.

Artificial intelligence applied to the ECG is expanding the clinical role of this widely available diagnostic tool beyond conventional waveform interp...

Personalized antiplatelet therapy: are we ready for a precision medicine approach.

Dual antiplatelet therapy (DAPT), combining aspirin with a P2Y12 inhibitor, remains central to secondary prevention after acute coronary syndrome and ...

Deep learning-based treatment decision support framework for multi-vessel coronary artery disease using integrated coronary angiography and clinical data.

BACKGROUND: Treatment selection between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) for multi-vessel coronary ...

Cardio-respiratory deep learning model to predict hospital admission from emergency department electrocardiogram images and respiration videos: a prospective study.

BACKGROUND: Early prediction of hospital admission at the emergency department (ED) triage can improve patient flow and resource allocation. Most exis...