Myocardial Infarction

Regional End-Systolic Circumferential Strain Demonstrates Reduced Function in Remote Myocardium after Anterior STEMI

Anterior ST-segment elevation myocardial infarction (STEMI) is associated with severe adverse remo...

ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts; A Foundation for Computerized ECG Image Digitization and Analysis

We introduce the ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) ima...

A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption

Machine Learning (ML) has become one of the most impactful fields of data science in recent years....

Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review

The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining comb...

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...

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. ...

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...

[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...

[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...

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...

VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation

An electrocardiogram (ECG) captures the heart's electrical signal to assess various heart conditio...

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...

Quantifying variabilities in cardiac digital twin models of the electrocardiogram

Cardiac digital twins (CDTs) of human cardiac electrophysiology (EP) are digital replicas of patie...

External validation of the myocardial-ischaemic-injury-index machine learning algorithm for the early diagnosis of myocardial infarction: a multicentre cohort study.

BACKGROUND: The myocardial-ischaemic-injury-index (MI) is a novel machine learning algorithm for the...

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is...

Through the Looking Glass Darkly: How May AI Models Influence Future Underwriting?

Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditio...

Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG).

Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancem...

ECG-based Daily Activity Recognition Using 1D Convolutional Neural Networks.

This study presents an approach to human activity recognition (HAR) using electrocardiogram (ECG) si...

Federated Learning for Enhanced ECG Signal Classification with Privacy Awareness.

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare a...

Explainable Multimodal Deep Learning for Heart Sounds and Electrocardiogram Classification.

We introduce a Gradient-weighted Class Activation Mapping (Grad-CAM) methodology to assess the perfo...