Myocardial Infarction

Testing Near-Field Radio-Frequency Sensors to Predict Respiratory Distress in Patients Living With Dementia: A Pilot Feasibility and Acceptability Study.

BackgroundPeople living with dementia (PLWD) with advanced illness are prone to respiratory distress yet often cannot self-report dyspnea, delaying recognition and treatment. Near-field radio-frequency (NFRF) sensors offer touchless, covert cardiopulmonary monitoring that may be better tolerated than tethered devices.ObjectivesTo assess the feasibility and acceptability of NFRF bed sensor for home...

Machine learning-based information flow analysis of ECG signals for long QT syndrome.

Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for non-invasive and cost-effective risk assessment tools. Biological systems, including the heart, exhibit complex nonlinear dynamics arising from interactions between their subsystems. Information-theoretic measures, particularly entropy-based methods, provide a framework to quantify these interactions. Us...

MORGAS (MORtality ORiented Glucose, Age, Smoking, and Stent Score) predicts mortality after percutaneous coronary intervention.

BACKGROUND: Accurate prediction of mortality after percutaneous coronary intervention (PCI) remains a clinical challenge. Existing risk scores often l...

Use of Machine-Learning Models to Identify Clinical Features Associated With A Future Clinical Worsening Event in Patients With Pulmonary Arterial Hypertension.

Clinical worsening events are increasingly recognized as a meaningful outcome in pulmonary arterial hypertension (PAH). We applied machine-learning mo...

Evaluating AI-Enabled Technologies for Atrial Fibrillation Detection: A Systematic Review of Diagnostic Performance.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained arrhythmia worldwide and a major contributor to stroke and cardiovascular morbidity....

A large-scale 12-lead electrocardiogram dataset for acute coronary syndrome prediction containing 19,955 ECGs.

Acute coronary syndrome(ACS) is a common cardiovascular disease and a severe type of coronary heart disease. Electrocardiograms(ECGs) are the initial ...

An integrated microneedle device for skin interstitial fluid multiplexed detection and deep learning-based analysis of acute myocardial infarction biomarkers.

Acute myocardial infarction (AMI) remains difficult to diagnose rapidly outside hospital settings because current evaluation still relies mainly on el...

Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data.

Seizure forecasting and affective state analysis using EEG-ECG data play a pivotal role in advancing neurological and mental health monitoring. Howeve...

Extending Multiscale Characterization of Heart Rate Variability via Deep Learning for Mortality Risk Prediction.

OBJECTIVE: To improve mortality risk prediction from heart rate variability (HRV) signals by capturing nonlinear scaling patterns often overlooked by ...

Stabilizing Extreme Few-Shot ECG Classification via Self-Supervised Contrastive Pretraining.

In clinical electrocardiogram (ECG) analysis, high-quality annotations are expensive and difficult to scale, leaving many tasks in an extreme few-shot...

Prehospital Risk Stratification Using Unsupervised Machine Learning in STEMI.

BACKGROUND: ST-elevation myocardial infarction (STEMI) exhibits substantial clinical heterogeneity complicating prehospital risk stratification. Tradi...

Prediction of shock-refractory ventricular fibrillation in patients with out-of-hospital cardiac arrest: external validation of an ECG-based approach.

BACKGROUND: Shock-refractory ventricular fibrillation (VF) patients can be defined as those requiring at least three defibrillation attempts. Patients...

Empowering atrial fibrillation detection with TabPFN and SHAP interpretation based on ECG-derived features: a dual-center temporal validation study.

Atrial fibrillation (AF) remains a leading driver of stroke and heart failure, yet timely diagnosis is frequently hindered by its asymptomatic nature ...

Assessment models for driving comfort and fatigue status based on heart rate variability.

OBJECTIVES: This study, from an interdisciplinary perspective of human factors engineering and biomedical engineering, aims to develop a real-time ass...

Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation.

BACKGROUND: Accurately differentiating severe from nonsevere COVID-19 clinical types is critical for the health care system to optimize workflow. Curr...

A teacher-student deep learning framework for enhanced clinical screening of heart failure from 12-lead electrocardiograms.

OBJECTIVE: Identifying heart failure (HF) from electrocardiograms (ECG) is challenging due to the lack of definitive features. This study aims to deve...

Exploratory association between multimodal AI-derived digital biomarkers and in-hospital mortality in adult patients with pneumonia: A proof-of-concept study.

Pneumonia remains a leading cause of in-hospital mortality worldwide. Current prognostic tools such as the IDSA/ATS severity score have meaningful lim...

The Rlign algorithm for enhanced electrocardiogram analysis through heart rate-corrected ECG alignment for explainable classification and clustering.

AIMS: Electrocardiogram (ECG) recordings are fundamental for diagnosing cardiac conditions. Recent advances in automatic ECG analysis have been domina...

Early emergency department decision support for heart failure hospitalization using triage-level unstructured and structured data: a retrospective cohort study.

BACKGROUND: Early identification of patients at risk for heart failure (HF) hospitalization in the emergency department (ED) is challenging because de...

Decoding Kounis syndrome with artificial intelligence: from diagnosis to precision medicine.

Kounis syndrome-acute coronary events triggered by allergic or hypersensitivity reactions-remains underrecognized across emergency and cardiology sett...