Cardiovascular

Arrhythmias

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

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Showing 274-294 of 1,699 articles
MPCNN: A Novel Matrix Profile Approach for CNN-based Single Lead Sleep Apnea in Classification Problem.

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. ...

Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.

AIM: Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sen...

A Deep-Learning-Based CPR Action Standardization Method.

In emergency situations, ensuring standardized cardiopulmonary resuscitation (CPR) actions is crucia...

Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) ...

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker impla...

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model.

BACKGROUND: Sleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive c...

Advancing laser ablation assessment in hyperspectral imaging through machine learning.

Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative applicat...

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and aut...

Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder-Decoders with Residual and Recurrent Connections.

The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder-de...

AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment...

Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.

This research study demonstrates an efficient scheme for early detection of cardiorespiratory compli...

Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function.

This study aims to address the challenges of imbalanced heartbeat classification using electrocardio...

Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications.

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potent...

A novel way to prospectively evaluate of AI-enhanced ECG algorithms.

Significant strides will be made in the field of computerized electrocardiology through the developm...

A Novel Instruction Driven 1-D CNN Processor for ECG Classification.

Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and cha...

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient...

Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks.

OBJECTIVE: wearable sensor technology has progressed significantly in the last decade, but its clini...

Machine learning-based atrial fibrillation detection and onset prediction using QT-dynamicity.

. This study examines the value of ventricular repolarization using QT dynamicity for two different ...

Wearable ECG Device and Machine Learning for Heart Monitoring.

With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for moni...

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