Cardiovascular

Myocardial Infarction

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

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Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL.

Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its interpretation...

AECG-DecompNet: abdominal ECG signal decomposition through deep-learning model.

The accurate decomposition of a mother's abdominal electrocardiogram (AECG) to extract the fetal ECG...

Conjunctival Provocation Test With .

Conjunctival provocation test (CPT) is used to demonstrate clinical relevance to a specific allerge...

Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

BACKGROUND: Numerous studies have revealed the relationship between lipid expression and increased c...

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-base...

An artificial intelligence-enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the 'Turing test'?

OBJECTIVE: To develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm capa...

ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 ...

Automated ECG classification based on 1D deep learning network.

The standard 12-lead electrocardiogram (ECG) records the heart's electrical activity from electrodes...

Pre-existing and machine learning-based models for cardiovascular risk prediction.

Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has...

A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network.

Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during slee...

Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time.

Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TS...

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement.

Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile devi...

Interpretable heartbeat classification using local model-agnostic explanations on ECGs.

Treatment and prevention of cardiovascular diseases often rely on Electrocardiogram (ECG) interpreta...

Accessory pathway analysis using a multimodal deep learning model.

Cardiac accessory pathways (APs) in Wolff-Parkinson-White (WPW) syndrome are conventionally diagnose...

Comparing performance of iterative and non-iterative algorithms on various feature schemes for arrhythmia analysis.

To evaluate the performance of the classic machine learning algorithms and the effectiveness of vari...

Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography.

Although digoxin is important in heart rate control, the utilization of digoxin is declining due to ...

A New ECG Denoising Framework Using Generative Adversarial Network.

This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adver...

CEFEs: A CNN Explainable Framework for ECG Signals.

In the healthcare domain, trust, confidence, and functional understanding are critical for decision ...

Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network.

To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid predict...

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