Cardiovascular

Arrhythmias

Latest AI and machine learning research in arrhythmias for healthcare professionals.

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Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients.

Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settin...

Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence.

BACKGROUND: Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized...

A novel deep learning package for electrocardiography research.

. In recent years, deep learning has blossomed in the field of electrocardiography (ECG) processing,...

Detection of arrhythmia in 12-lead varied-length ECG using multi-branch signal fusion network.

Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early pr...

In-sensor neural network for high energy efficiency analog-to-information conversion.

This work presents an on-chip analog-to-information conversion technique that utilizes analog hyper-...

A Neuromorphic Processing System With Spike-Driven SNN Processor for Wearable ECG Classification.

This paper presents a neuromorphic processing system with a spike-driven spiking neural network (SNN...

[Robot-assisted Mediastinal Surgery].

Because of the many important anatomical structures located closely together at very small distances...

DDCNN: A Deep Learning Model for AF Detection From a Single-Lead Short ECG Signal.

With the popularity of the wireless body sensor network, real-time and continuous collection of sing...

Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image Denoising for Smart Laser Osteotomy.

Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence...

Ensemble classification combining ResNet and handcrafted features with three-steps training.

This work presents an ECG classifier for variable leads as a contribution to the Computing in Cardio...

A novel P-QRS-T wave localization method in ECG signals based on hybrid neural networks.

As the number of people suffering from cardiovascular diseases increases every year, it becomes esse...

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning.

Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart ...

A Prediction Algorithm for Hypoglycemia Based on Support Vector Machine Using Glucose Level and Electrocardiogram.

A prediction algorithm for hypoglycemic events is proposed using glucose levels and electrocardiogra...

Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images.

The level of patient's illness is determined by diagnosing the problem through different methods lik...

Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency Catheter Ablation in Patients with Atrial Fibrillation.

Current guidelines on atrial fibrillation (AF) emphasized that radiofrequency catheter ablation (RFC...

Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation.

We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead electrocardiogr...

A Deep Neural Network Ensemble Classifier with Focal Loss for Automatic Arrhythmia Classification.

Automated electrocardiogram classification techniques play an important role in assisting physicians...

Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model.

An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various he...

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