Cardiovascular

Arrhythmias

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

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Higher-Order Spectral Analysis Combined with a Convolution Neural Network for Atrial Fibrillation Detection-Preliminary Study.

The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is...

Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism.

BACKGROUND: Automatic abnormalities detection based on Electrocardiogram (ECG) contributes greatly t...

Artificial neural networks for ECG interpretation in acute coronary syndrome: A scoping review.

INTRODUCTION: The electrocardiogram (ECG) is a crucial diagnostic tool in the Emergency Department (...

Intelligent deep model based on convolutional neural network's and multi-layer perceptron to classify cardiac abnormality in diabetic patients.

The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding ...

EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.

OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstru...

ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs.

Electrocardiogram (ECG) delineation to identify the fiducial points of ECG segments, plays an import...

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential dif...

A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach.

Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients w...

Deep learning based ECG segmentation for delineation of diverse arrhythmias.

Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features t...

xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.

Deep learning-based methods have demonstrated high classification performance in the detection of ca...

IoMT-Based Smart Healthcare Detection System Driven by Quantum Blockchain and Quantum Neural Network.

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification...

ECG autoencoder based on low-rank attention.

The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost ca...

Multiscale dilated convolutional neural network for Atrial Fibrillation detection.

Atrial Fibrillation (AF), a type of heart arrhythmia, becomes more common with aging and is associat...

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit.

Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain ...

A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption i...

ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization.

Cardiovascular diseases are a major cause of mortality globally, and electrocardiograms (ECGs) are c...

Characterization of Heart Diseases per Single Lead Using ECG Images and CNN-2D.

Cardiopathy has become one of the predominant global causes of death. The timely identification of d...

Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning.

Quantitative imaging in life sciences has evolved into a powerful approach combining advanced micros...

Electrocardiography Classification with Leaky Integrate-and-Fire Neurons in an Artificial Neural Network-Inspired Spiking Neural Network Framework.

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifyin...

Machine learning model with output correction: Towards reliable bradycardia detection in neonates.

Bradycardia is a commonly occurring condition in premature infants, often causing serious consequenc...

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