Myocardial Infarction

Computerized Self-Reported Medical History Taking to Support Early Rule Out of Major Adverse Cardiac Events in Patients With Acute Chest Pain: Post Hoc Analysis of the CLEOS-CPDS Prospective Cohort Study.

BACKGROUND: Self-reported, computerized history taking (CHT) may enable efficient collection of medical histories for acute chest pain management. OBJECTIVE: The primary aim is to determine the diagnostic performance of 4 CHT-derived chest pain risk scores for ruling out 30-day major adverse cardiac events (MACEs) or acute coronary syndrome (ACS). The secondary aim is to assess their impact on pat...

Stress Detection Using Heart Rate Variability and Respiratory Signals Derived From a Single-Lead ECG.

Stress detection is a widely studied field due to its significant implications for mental and physical health. While multimodal approaches show promising results, they present challenges related to hardware constraints and computational requirements that limits real time implementation in wearable devices. We propose a hybrid methodology combining feature extraction with ma chine learning (ML) for...

A novel approach for atrial fibrillation-related obstructive sleep apnea detection using enhanced single-lead electrocardiogram features with customized deep learning algorithm.

STUDY OBJECTIVES: Atrial fibrillation (AF) and obstructive sleep apnea (OSA) are interrelated conditions that substantially increase the risk of cardi...

Rosette Cardiac MR Fingerprinting for Simultaneous T1, T2, T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and Fat Fraction Mapping Using a Multi-Echo Deep Image Prior Reconstruction.

PURPOSE: Quantitative mapping of cardiac tissue properties is used clinically in diagnosis and monitoring of a wide variety of cardiac pathologies. Ca...

Current and future use of artificial intelligence in valvular heart disease imaging.

Valvular heart disease (VHD) remains significantly underdiagnosed and undertreated. This review examines an artificial intelligence (AI)-enhanced 'spo...

Prehospital Blood Collection for High Sensitivity Cardiac Troponin Measurement in Patients with Acute Chest Pain.

OBJECTIVES: High sensitivity cardiac troponin (hs-cTn) measures are used in the emergency department (ED) to evaluate patients with acute chest pain. ...

MM-GradCAM: an improved multimodal GradCAM method with 1D and 2D ECG data for detection of cardiac arrhythmia.

As cardiac arrhythmia remains one of the leading causes of death worldwide, early and accurate diagnosis of cardiac arrhythmia is critical to improvin...

[Application of molecularly imprinted polymers-based sensors for determination of acute coronary syndrome biomarkers].

Cardiovascular diseases （CVDs） are among the leading cause of global morbidity and mortality. Due to their high prevalence and often asymptomatic prog...

Letter: Deep Learning Model Significantly Improves MAGGIC Score for Predicting Contrast-Induced Nephropathy in STEMI Patients.

Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System Without Electroencephalography.

STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-in...

Developing and validating a machine learning model for predicting post-thrombolysis seizures in acute ischemic stroke.

Post-stroke seizures (PSS) manifests variably due to ischemic brain injury, yet its risk factors remain unclear. This study developed a machine learni...

A cascaded CNN-LSTM framework for quantifying respiratory motion from surface electromyographic signals.

OBJECTIVE: Surface electromyographic (sEMG) signals of the diaphragm provide a valuable physiological signal for real-time respiratory monitoring, par...

Evaluation and application of electrocardiographic age model for children.

ECG-age, derived from ECG signals using deep neural networks (DNNs), correlates with health status but has been predominantly studied in adults, negle...

xGNN4MI: explainability of graph neural networks in 12-lead electrocardiography for cardiovascular disease classification.

The clinical deployment of artificial intelligence (AI) solutions for assessing cardiovascular disease (CVD) risk in 12-lead electrocardiography (ECG)...

A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet.

Electrocardiography is a cornerstone in the diagnosis of cardiovascular diseases; however, accurate interpretation demands expert knowledge and is oft...

Deep learning for estimating right ventricular function from routine coronary angiography.

AIMS: Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sough...

LSTM-GPT-4 Integration for Interpretable Biomedical Signal Classification.

BACKGROUND: Approximately 3.8 billion people lack access to essential health services, and diagnostic interpretation remains a major bottleneck in rem...

A novel ECG QRS complex detection algorithm based on dynamic Bayesian network.

Accurate detection of the QRS complex, a crucial reference for heartbeat localization in electrocardiogram (ECG) signals, remains inadequate in wearab...

Artificial intelligence in estimating instantaneous wave-free ratio: a systematic literature review of techniques.

Fractional flow reserve (FFR) is an essential tool for evaluating coronary artery disease and directing percutaneous coronary intervention (PCI). The ...

Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation and Clinical Outcomes in Kidney Transplant Recipients.

BACKGROUND: Incident atrial fibrillation (AF) is common following kidney transplantation (KTx) and is associated with worse clinical outcomes. Artific...