Arrhythmias

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG.

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. Ho...

Transfer Learning in ECG Classification from Human to Horse Using a Novel Parallel Neural Network Architecture.

Automatic or semi-automatic analysis of the equine electrocardiogram (eECG) is currently not possible because human or small animal ECG analysis software is unreliable due to a different ECG morphology in horses resulting from a different cardiac innervation. Both filtering, beat detection to classification for eECGs are currently poorly or not described in the literature. There are also no public...

Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks.

This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true al...

Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation.

IMPORTANCE: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its early detection could lead to significant improvements i...

Avoiding Urinary Catheterization in Patients Undergoing Atrial Fibrillation Catheter Ablation.

PURPOSE: Indwelling urinary catheters are commonly inserted when administering general anesthesia. However, there are significant risks to routine IUC...

Applications of machine learning in decision analysis for dose management for dofetilide.

BACKGROUND: Initiation of the antiarrhythmic medication dofetilide requires an FDA-mandated 3 days of telemetry monitoring due to heightened risk of t...

Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.

OBJECTIVE: Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracki...

Polydopamine Nanoparticles for Deep Brain Ablation via Near-Infrared Irradiation.

Local resection or ablation remains an important approach to treat drug-resistant central neurological disease. Conventional surgical approaches are d...

Secretion of equine chorionic gonadotropin and its association with supplementary corpus luteum formation and progesterone concentration in Hokkaido native pony recipient mares.

The objectives of this study were to determine the plasma profile of equine chorionic gonadotropin (eCG) and its association with the formation of sup...

Recurrent ischemic stroke in patients with atrial fibrillation ablation and prior stroke: A study based on etiological classification.

BACKGROUND: Different subtypes of ischemic stroke may have different risk factors, clinical features, and prognoses. This study investigated the incid...

A 13.34 μW Event-Driven Patient-Specific ANN Cardiac Arrhythmia Classifier for Wearable ECG Sensors.

Artificial neural network (ANN) and its variants are favored algorithm in designing cardiac arrhythmia classifier (CAC) for its high accuracy. However...

[Predicting atrial fibrillation through a sinus-rhythm electrocardiogram; useful or not?].

In patients with cryptogenic stroke, the detection of atrial fibrillation (AF) is important, since it is an indication for the prescription of oral an...

Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram.

BACKGROUND: The electrocardiogram (ECG) has been widely used in the diagnosis of heart disease such as arrhythmia due to its simplicity and non-invasi...

An incremental learning system for atrial fibrillation detection based on transfer learning and active learning.

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is a type of arrhythmia with high incidence. Automatic AF detection methods have been studied in pr...

Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE.

Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis system minimizes the mortality rate of cardiac disease patien...

Early and Late Fusion Machine Learning on Multi-Frequency Electrical Impedance Data to Improve Radiofrequency Ablation Monitoring.

Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types...

High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning.

INTRODUCTION: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on ...

Predicting atrial fibrillation in primary care using machine learning.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of pat...

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

BACKGROUND: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed a...

A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG.

Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Rece...