Cardiovascular

Myocardial Infarction

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

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Long-duration electrocardiogram classification based on Subspace Search VMD and Fourier Pooling Broad Learning System.

Detecting early stages of cardiovascular disease from short-duration Electrocardiogram (ECG) signals...

An ECG-based machine-learning approach for mortality risk assessment in a large European population.

AIMS: Through a simple machine learning approach, we aimed to assess the risk of all-cause mortality...

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...

Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms.

ST elevation myocardial infarction (STEMI), a subtype of acute coronary syndrome, is one of the lead...

Role of Robotics in Image-Guided Trans-Arterial Interventions.

The integration of robotic systems in image-guided trans-arterial interventions has revolutionized t...

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 ...

Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients.

Inflammatory prognostic index (IPI), has been shown to be related with poor outcomes in cancer pati...

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...

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...

Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.

BACKGROUND: Acute kidney injury (AKI) and contrast-induced nephropathy (CIN) are common complication...

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...

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