AIMC Topic: Electrocardiography

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Contactless facial video recording with deep learning models for the detection of atrial fibrillation.

Scientific reports
Atrial fibrillation (AF) is often asymptomatic and paroxysmal. Screening and monitoring are needed especially for people at high risk. This study sought to use camera-based remote photoplethysmography (rPPG) with a deep convolutional neural network (...

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.

Computers in biology and medicine
The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limit...

Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study.

The Lancet. Digital health
BACKGROUND: Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aim...

System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics.

Sensors (Basel, Switzerland)
Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easil...

A CNN Model for Cardiac Arrhythmias Classification Based on Individual ECG Signals.

Cardiovascular engineering and technology
PURPOSE: Wearable devices in the scenario of connected home healthcare integrated with artificial intelligence have been an effective alternative to the conventional medical devices. Despite various benefits of wearable electrocardiogram (ECG) device...

Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG.

Computers in biology and medicine
Atrial fibrillation (AF) is the most common supraventricular cardiac arrhythmia, resulting in high mortality rates among affected patients. AF occurs as episodes coming from irregular excitations of the ventricles that affect the functionality of the...

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection.

Sensors (Basel, Switzerland)
Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios. To accomplish such frontiers, we propose three artificial intelligence models ...

Expert-enhanced machine learning for cardiac arrhythmia classification.

PloS one
We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic c...

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.

Scientific reports
In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical d...