Myocardial Infarction

Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity.

Artificial intelligence (AI)-enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can ...

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study.

BACKGROUND: Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict ri...

Diagnostic Accuracy of AI Algorithms in Aortic Stenosis Screening: A Systematic Review and Meta-Analysis.

Aortic stenosis (AS) is frequently identified at an advanced stage after clinical symptoms appear. ...

LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular dise...

Clinical decision support using machine learning and cardiac troponin for the diagnosis of myocardial infarction.

Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.

BACKGROUND: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to de...

[Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks].

Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatmen...

[Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network].

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life...

A Novel Method and Python Library for ECG Signal Quality Assessment.

Electrocardiogram (ECG) is one of the reference cardiovascular diagnostic exams. However, the ECG si...

Assessing the Reliability of Machine Learning Explanations in ECG Analysis Through Feature Attribution.

Feature attribution methods stand as a popular approach for explaining the decisions made by convolu...

Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.

BACKGROUND: Stent malapposition (SM) following percutaneous coronary intervention (PCI) for myocardi...

HRV-based Monitoring of Neonatal Seizures with Machine Learning.

With the rapid development of machine learning (ML) in biomedical signal processing, ML-based neonat...

ECG Beat-By-Beat Classification Using Hybrid Transformer Neural Network Model in Smart Health.

Wearable cardiac monitors can be used to detect potential heart attack by syncing with smartphone ap...

Electrocardiographic Classification using Deep Learning with Lead Switching.

The classification algorithms of rhythm and morphology abnormalities in electrocardiogram (ECG) sign...

Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning.

Common artefacts such as baseline drift, rescaling, and noise critically limit the performance of ma...

Enhancing explainability in ECG analysis through evidence-based AI interpretability.

While pre-trained neural networks, e.g., for diagnosis from electrocardiograms (ECGs), are already a...

Personality Trait Recognition using ECG Spectrograms and Deep Learning.

This paper presents an innovative approach to recognizing personality traits using deep learning (DL...

Advancements in Continuous Glucose Monitoring: Integrating Deep Learning and ECG Signal.

This paper presents a novel approach to noninvasive hyperglycemia monitoring utilizing electrocardio...

A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals.

In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, part...

ECG Abnormality Detection Using MIMIC-IV-ECG Data Via Supervised Contrastive Learning.

Electrocardiogram data provide a tremendous opportunity for the detection of various types of cardia...

A Hybrid GCN-LSTM Model for Ventricular Arrhythmia Classification Based on ECG Pattern Similarity.

Accurate differentiation between Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) is e...