Cardiovascular

Myocardial Infarction

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

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Showing 967-987 of 6,892 articles
Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks.

This paper proposes deep learning methods with signal alignment that facilitate the end-to-end class...

Analytical Concordance of Diverse Point-of-Care and Central Laboratory Troponin I Assays.

BACKGROUND: Cardiac troponin I (cTnI) 99th percentile cutoffs, used in the diagnosis of acute myocar...

Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG.

UNLABELLED: The automated detection of arrhythmia in a Holter ECG signal is a challenging task due t...

Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device.

OBJECTIVE: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal qualit...

A novel training method to preserve generalization of RBPNN classifiers applied to ECG signals diagnosis.

In this paper a novel training technique is proposed to offer an efficient solution for neural netwo...

A Wearable Multi-Modal Bio-Sensing System Towards Real-World Applications.

Multi-modal bio-sensing has recently been used as effective research tools in affective computing, a...

Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation.

OBJECTIVE: The prevalence of atrial fibrillation (AF) in the general population is 0.5%-1%. As AF is...

Temporal Performance of Laplacian Eigenmaps and 3D Conduction Velocity in Detecting Ischemic Stress.

BACKGROUND: Myocardial ischemia has a complex and time-varying electrocardiographic signature that i...

Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.

The development of new technology such as wearables that record high-quality single channel ECG, pro...

Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings.

Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly p...

Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine.

Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural...

Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor.

Routine stress monitoring in daily life can predict potentially serious health impacts. Effective st...

Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks.

Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregula...

A support vector machine approach for AF classification from a short single-lead ECG recording.

OBJECTIVE: In this paper, a support vector machine (SVM) approach using statistical features, P wave...

Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals.

Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption of breathing resulting ...

A novel application of deep learning for single-lead ECG classification.

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac ...

Effect of Health and Training on Ultrasensitive Cardiac Troponin in Marathon Runners.

PURPOSE: Cardiac troponin (cTn) is the gold standard biomarker for assessing cardiac damage. Previou...

Heart Rate Estimated from Body Movements at Six Degrees of Freedom by Convolutional Neural Networks.

Cardiac activity has been monitored continuously in daily life by virtue of advanced medical instrum...

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from...

Extracting Healthcare Quality Information from Unstructured Data.

Healthcare quality research is a fundamental task that involves assessing treatment patterns and mea...

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