Myocardial Infarction

Practical fine-grained learning based anomaly classification for ECG image.

As a widely used vital sign within cardiology, Electrocardiography (ECG) provides the basis for asse...

MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.

Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields indu...

ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features.

Background and Objective Electrocardiogram (ECG) quality assessment is significant for automatic dia...

Exploration of physiological sensors, features, and machine learning models for pain intensity estimation.

In current clinical settings, typically pain is measured by a patient's self-reported information. T...

The effect of cardiac rhythm on artificial intelligence-enabled ECG evaluation of left ventricular ejection fraction prediction in cardiac intensive care unit patients.

The presence of left ventricular systolic dysfunction (LVSD) alters clinical management and prognosi...

ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome.

This study presents and evaluates the mathematical model to estimate the mean and variance of single...

Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study.

Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is ...

Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks.

In this study, we conducted a comparative analysis of deep convolutional neural network (CNN) models...

Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features.

The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) sign...

Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction.

Machine learning (ML) has been suggested to improve the performance of prediction models. Neverthele...

A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.

Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity...

A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome.

OBJECTIVE: Some researchers have studied about early prediction and diagnosis of major adverse cardi...

Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks.

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG...

Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.

PURPOSE: The purpose of this study was to develop and evaluate an algorithm that can automatically e...

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals.

The mental stress faced by many people in modern society is a factor that causes various chronic dis...

Enhanced Diagnosis of Pneumothorax with an Improved Real-Time Augmentation for Imbalanced Chest X-rays Data Based on DCNN.

Pneumothorax is a common pulmonary disease that can lead to dyspnea and can be life-threatening. X-r...

Explaining deep neural networks for knowledge discovery in electrocardiogram analysis.

Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and ac...

Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography.

Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general popula...

Impact of ECG Characteristics on the Performance of an Artificial Intelligence Enabled ECG for Predicting Left Ventricular Dysfunction.

The application of deep learning in electrocardiogram: Where we came from and where we should go?

Electrocardiogram (ECG) is a commonly-used, non-invasive examination recording cardiac voltage versu...

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosi...