AIMC Topic: Electrocardiography

Clear Filters Showing 621 to 630 of 1388 articles

Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction.

Journal of healthcare engineering
This paper proposes a representation learning framework HE-LSTM model for heterogeneous temporal events, which can automatically adapt to the multiscale sampling frequency of multisource heterogeneous data. The proposed model also demonstrates its su...

Weak Supervision for Affordable Modeling of Electrocardiogram Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose heart disease. ECG studies using Machine Learning to automatically detect abnormal heartbeats so far depend on large, manually annotated datasets. Wh...

A deep learning-based system capable of detecting pneumothorax via electrocardiogram.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: To determine if an electrocardiogram-based artificial intelligence system can identify pneumothorax prior to radiological examination.

Early heart rate variability evaluation enables to predict ICU patients' outcome.

Scientific reports
Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such var...

Artificial intelligence for the detection, prediction, and management of atrial fibrillation.

Herzschrittmachertherapie & Elektrophysiologie
The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and ...

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.

Computational and mathematical methods in medicine
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. T...

Electrocardiogram Signal Classification in the Diagnosis of Heart Disease Based on RBF Neural Network.

Computational and mathematical methods in medicine
Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG d...

A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network.

Sensors (Basel, Switzerland)
The heart is one of the human body's vital organs. An electrocardiogram (ECG) provides continuous tracings of the electrophysiological activity originated from heart, thus being widely used for a variety of diagnostic purposes. This study aims to des...

Electrocardiogram Quality Assessment Using Unsupervised Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: Noise and disturbances hinder effective interpretation of recorded ECG. To identify the clean parts of a recording, free from such disturbances, various quality indicators have been developed. Previous instances of these indicators focus o...