Arrhythmias

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the di...

System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning.

The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca signals predict hourly time, via a group decision-making mechanism coupled with a spatial...

Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review.

Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for pr...

MA-MIL: Sampling point-level abnormal ECG location method via weakly supervised learning.

BACKGROUND AND OBJECTIVE: Current automatic electrocardiogram (ECG) diagnostic systems could provide classification outcomes but often lack explanatio...

Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.

Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for d...

Automated detection of myocardial infarction based on an improved state refinement module for LSTM/GRU.

Myocardial infarction (MI) is a common cardiovascular disease caused by the blockages of coronary arteries. The visual inspection of electrocardiogram...

Prediction of adverse cardiovascular events in children using artificial intelligence-based electrocardiogram.

BACKGROUND: Convolutional neural networks (CNNs) have emerged as a novel method for evaluating heart failure (HF) in adult electrocardiograms (ECGs). ...

Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting.

The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (...

Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning.

Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular parameters, conventionally determined using cardiac magnet...

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG).

Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focu...

Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images.

Thoracic aorta calcium (TAC) can be assessed from cardiac computed tomography (CT) studies to improve cardiovascular risk prediction. The aim of this ...

Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three‑lead signals.

BACKGROUND: In the field of mobile health, portable dynamic electrocardiogram (ECG) monitoring devices often have a limited number of lead electrodes ...

Impact of ECG data format on the performance of machine learning models for the prediction of myocardial infarction.

Background We aim to determine which electrocardiogram (ECG) data format is optimal for ML modelling, in the context of myocardial infarction predicti...

Sensor-Based Measurement Method to Support the Assessment of Robot-Assisted Radiofrequency Ablation.

Digital surgery technologies, such as interventional robotics and sensor systems, not only improve patient care but also aid in the development and op...

Impact of artificial intelligence arrhythmia mapping on time to first ablation, procedure duration, and fluoroscopy use.

INTRODUCTION: Artificial intelligence (AI) ECG arrhythmia mapping provides arrhythmia source localization using 12-lead ECG data; whether this informa...

An interpretable shapelets-based method for myocardial infarction detection using dynamic learning and deep learning.

Myocardial infarction (MI) is a prevalent cardiovascular disease that contributes to global mortality rates. Timely diagnosis and treatment of MI are ...

12-Lead ECG Reconstruction Based on Data From the First Limb Lead.

PURPOSE: Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system's (CVS...

Deep learning-based prediction of major arrhythmic events in dilated cardiomyopathy: A proof of concept study.

Prediction of major arrhythmic events (MAEs) in dilated cardiomyopathy represents an unmet clinical goal. Computational models and artificial intellig...

Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.

We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device ca...

Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network.

Previous studies have successfully predicted overweight status by applying deep learning to 12-lead electrocardiogram (ECG); however, models for predi...