Myocardial Infarction

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 attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40-79 years, naïv...

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 sleep and is associated with many other medical conditions, including hypertension, coronary heart disease, and depression. Clinically, the standard for diagnosing OSA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the avail...

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 (TSS). However, TSS may not be readily available in u...

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

Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response...

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

Treatment and prevention of cardiovascular diseases often rely on Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, EC...

Accessory pathway analysis using a multimodal deep learning model.

Cardiac accessory pathways (APs) in Wolff-Parkinson-White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there ar...

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 various features, the iterative algorithms (i.e., supp...

Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography.

Although digoxin is important in heart rate control, the utilization of digoxin is declining due to its narrow therapeutic window. Misdiagnosis or del...

A New ECG Denoising Framework Using Generative Adversarial Network.

This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated wi...

CEFEs: A CNN Explainable Framework for ECG Signals.

In the healthcare domain, trust, confidence, and functional understanding are critical for decision support systems, therefore, presenting challenges ...

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier.

Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital f...

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 prediction method for ECG signals in this paper. The prop...

Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study.

Recurrent angina (RA) after percutaneous coronary intervention (PCI) has few known risk factors, hampering the identification of high-risk populations...

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary...

Identifying Heart Failure in ECG Data With Artificial Intelligence-A Meta-Analysis.

Electrocardiography (ECG) is a quick and easily accessible method for diagnosis and screening of cardiovascular diseases including heart failure (HF)...

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning.

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary...

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders, and the most deadly: Cardiovascular diseases. Due...

Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.

BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted,...

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in...

A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

OBJECTIVE: This study aims to develop an artificial intelligence-based method to screen patients with left ventricular ejection fraction (LVEF) of 50%...