Myocardial Infarction

Developing and validating an artificial intelligence-based electronic triage model for predicting clinical outcomes among cardiac-suspected patients in the emergency department.

AIMS: Emergency department overcrowding, especially in cardiac units, delays care and raises mortality. Conventional triage is error-prone. We developed an AI-based model integrating routine data and automated ECGs to improve early risk classification. METHODS AND RESULTS: This retrospective cross-sectional study involved 600 medical records of patients presenting with suspected cardiac symptoms. ...

Heart disease diagnosis and categorization from ECG signals using hybrid Fuzzy-CNN machine optimized by meta-heuristic algorithms.

Cardiovascular diseases are among the most important causes of global mortality, and their diagnosis is mainly based on ECG signals. The complexity and nonlinearity of these signals have limited the effectiveness of classical machine learning methods. In this study, an optimized hybrid model based on Fuzzy-CNN is presented using meta-heuristic algorithms for classifying 2D images obtained from ECG...

Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI.

Delirium is a frequent and clinically consequential complication among patients admitted to the intensive care unit (ICU). Early risk stratification i...

Multimodal Cardiovascular Disease Detection Using ECG Image and EHR.

ECG is an important signal for cardiovascular disease prediction. Since the ECG signals are often stored as images in clinical practice, we transforme...

Generalization of ML Models Between ECG and VCG Representation.

Integrating heterogeneous data sources is vital for developing and validating robust medical machine learning models. Although the 12-lead format is s...

Architecture-Specific Impact of Preprocessing on Machine Learning Models for ECG Classification.

Automated analysis of electrocardiograms relies increasingly on deep learning models. In these models, preprocessing steps may often be applied under ...

Interpreting ECG Images with Multimodal Large Language Models.

Cardiac diseases are one of the leading causes of death worldwide. Electrocardiography (ECG) is one of the major diagnostic methods to detect cardiac ...

Stabilizing neuromorphic ECG processors via adaptive fractional fatigue.

Spiking Neural Networks (SNNs) deployed on wearable devices can exhibit runaway firing when processing noisy electrocardiogram (ECG) signals, increasi...

Relationship between the diagnostic probability of chronic kidney disease calculated using artificial intelligence-enhanced electrocardiography and the incidence of cardiovascular events.

AIMS: A low estimated glomerular filtration rate (eGFR) is the primary diagnostic criterion for chronic kidney disease (CKD), a known risk factor for ...

A reproducible benchmark of QRS detection algorithms across diverse ECG datasets and noise conditions.

Accurate R-peak detection in electrocardiograms is critical for heart rate monitoring, heart rate variability analysis, and cardiac condition diagnosi...

Optimizing single-lead ECG axis for AI-based detection of myocardial diseases.

Wearable devices enable electrocardiograms (ECGs) outside traditional healthcare settings. While these devices are usually equipped with single-lead E...

A Multimodal Learning Framework for Detecting Systemic Hypertension in Sleep Apnea Patients Using ECG and PPG Signals.

Systemic hypertension (HTN) is a major cardiovascular comorbidity in patients with obstructive sleep apnea (OSA), yet these conditions are often diagn...

Composite small vessel disease scores predict hemorrhagic transformation after thrombectomy: a machine learning study.

BACKGROUND: Hemorrhagic transformation (HT) is a major complication of acute ischemic stroke (AIS), especially after mechanical thrombectomy (MT) and ...

Feasibility study of non-invasive blood pressure measurement based on multidimensional characteristics of radial artery pressure pulse wave.

OBJECTIVE: This study focuses on a non-invasive blood pressure prediction method based on radial artery pulse wave feature analysis, aiming to achieve...

Multimodal Artificial Intelligence for Early Detection and Precision Management of Inflammatory and Infiltrative Cardiomyopathies.

BACKGROUND: Inflammatory and infiltrative cardiomyopathies, including cardiac sarcoidosis, transthyretin amyloidosis, and autoimmune myocarditis, are ...

EdgeECG: a lightweight edge-oriented network with dual criterion pruning for real-time ECG arrhythmia classification.

Objective.Miniature electrocardiogram (ECG) devices can rapidly and accurately acquire real-time cardiac signals, enabling timely warnings for patient...

Accelerated biological aging in bipolar disorder as determined by artificial intelligence-based electrocardiographic assessment.

BACKGROUND: Individuals with bipolar disorder (BD) are at increased risk for major adverse cardiovascular events. Recent evidence suggests that the di...

Artificial intelligence in the differential diagnosis of hypertrophic cardiomyopathy and physiological hypertrophy: a scoping review.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most common inherited myocardial disorder and a major cause of sudden cardiac death in young adul...

Deep learning electrocardiogram model for risk stratification of coronary revascularization need in the emergency department.

BACKGROUND AND AIMS: Identification of patients with acute coronary syndrome requiring coronary revascularization can be challenging due to inconclusi...

Signal-Based Assessment of Postural Stability Using ECG, EMG, and Center of Pressure Measures.

Postural stability reflects the integrated function of autonomic, neuromuscular, and postural control systems and deteriorates with aging and reduced ...