Cardiovascular

Myocardial Infarction

Latest AI and machine learning research in myocardial infarction for healthcare professionals.

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Premature Ventricular Contraction Recognition Based on a Deep Learning Approach.

Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose he...

A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification.

BACKGROUND AND OBJECTIVE: The automatic recognition of myocardial infarction (MI) by artificial inte...

Deep Learning-Based Electrocardiograph in Evaluating Radiofrequency Ablation for Rapid Arrhythmia.

This study is aimed at analyzing the important role of deep learning-based electrocardiograph (ECG) ...

Robust PVC Identification by Fusing Expert System and Deep Learning.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may caus...

Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier.

Millions of people around the world are affected by arrhythmias, which are abnormal activities of th...

Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion.

PURPOSE: Surface electromyography (sEMG) is vulnerable to environmental interference, low recognitio...

Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG.

OBJECTIVES: Sleep apnea is the most common sleep disorder that leads to serious health complications...

Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks.

Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart's surface using the poten...

Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical pract...

Robotic Assisted Versus Manual Percutaneous Coronary Intervention: Systematic Review and Meta-Analysis.

INTRODUCTION: Robotics in percutaneous coronary intervention (R-PCI) has been one such area of advan...

Deep learning for predicting respiratory rate from biosignals.

In the past decade, deep learning models have been applied to bio-sensors used in a body sensor netw...

The Identification of ECG Signals Using WT-UKF and IPSO-SVM.

The biometric identification method is a current research hotspot in the pattern recognition field. ...

LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators.

Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase t...

A regularization method to improve adversarial robustness of neural networks for ECG signal classification.

With the advancement of machine leaning technologies, Deep Neural Networks (DNNs) have been utilized...

Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device.

The importance of an embedded wearable device with automatic detection and alarming cannot be overst...

Weak Supervision for Affordable Modeling of Electrocardiogram Data.

Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose...

Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management.

This study was aimed at exploring the new management mode of medical information processing and emer...

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