Cardiovascular

Arrhythmias

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

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A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers.

Artificial intelligence-enhanced electrocardiography (AI-ECG) can identify hypertrophic cardiomyopat...

Machine Learning-Based Prediction of Death and Hospitalization in Patients With Implantable Cardioverter Defibrillators.

BACKGROUND: Predicting the clinical trajectory of individual patients with implantable cardioverter-...

Prediction and Elimination of Physiological Tremor During Control of Teleoperated Robot Based on Deep Learning.

Currently, teleoperated robots, with the operator's input, can fully perceive unknown factors in a c...

Foot fractures diagnosis using a deep convolutional neural network optimized by extreme learning machine and enhanced snow ablation optimizer.

The current investigation proposes a novel hybrid methodology for the diagnosis of the foot fracture...

Assessing operator stress in collaborative robotics: A multimodal approach.

In the era of Industry 4.0, the study of Human-Robot Collaboration (HRC) in advancing modern manufac...

Exploring ChatGPT's potential in ECG interpretation and outcome prediction in emergency department.

BACKGROUND: Approximately 20 % of emergency department (ED) visits involve cardiovascular symptoms. ...

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the ...

FlexPoints: Efficient electrocardiogram signal compression for machine learning.

The electrocardiogram (ECG) stands out as one of the most frequently used medical tests, playing a c...

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution-Pooling Method.

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power...

Automated arrhythmia classification based on a pyramid dense connectivity layer and BiLSTM.

BackgroundDeep neural networks (DNNs) have recently been significantly applied to automatic arrhythm...

Machine learning for improved medical device management: A focus on defibrillator performance.

BackgroundPoorly regulated and insufficiently maintained medical devices (MDs) carry high risk on sa...

Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model.

Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electroc...

ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors.

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acqui...

Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification.

In response to increasing data privacy regulations, this work examines the use of federated learning...

Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence-Enabled ECGs.

BACKGROUND: Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respec...

AI derived ECG global longitudinal strain compared to echocardiographic measurements.

Left ventricular (LV) global longitudinal strain (LVGLS) is versatile; however, it is difficult to o...

Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network.

The classification of ECG signals is a critical process because it guides the diagnosis of the prope...

Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals.

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and preci...

Automatic noise detection for ambulatory electrocardiogram in presence of ventricular arrhythmias through a machine learning approach.

Noise detection in ambulatory electrocardiography is investigated as a machine learning binary class...

Empirical investigation of multi-source cross-validation in clinical ECG classification.

Traditionally, machine learning-based clinical prediction models have been trained and evaluated on ...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascul...

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