AIMC Topic: Atrial Fibrillation

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A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning.

IEEE reviews in biomedical engineering
Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causesĀ of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and ep...

Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection.

Medical & biological engineering & computing
Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely...

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks.

Sensors (Basel, Switzerland)
Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The s...

Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram.

International journal of cardiology
INTRODUCTION: Early detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have been developed, but they could not be applied in clinical practi...

Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI-enabled electrocardiogr...

Machine learning for nocturnal mass diagnosis of atrial fibrillation in a population at risk of sleep-disordered breathing.

Physiological measurement
OBJECTIVE: In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing.

Development of a Portable Tool to Identify Patients With Atrial Fibrillation Using Clinical Notes From the Electronic Medical Record.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone.

Long-term PM exposure and the clinical application of machine learning for predicting incident atrial fibrillation.

Scientific reports
Clinical impact of fine particulate matter (PM) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurem...

Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypoth...