Arrhythmias

Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.

BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cau...

FADE: Forecasting for anomaly detection on ECG.

BACKGROUND AND OBJECTIVE: Cardiovascular diseases, a leading cause of noncommunicable disease-relate...

Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin.

BACKGROUND: It is unclear, whether photoplethysmography (PPG) waveforms from wearable devices can di...

AI analysis for ejection fraction estimation from 12-lead ECG.

Heart failure (HF) remains a leading global cause of cardiovascular deaths, with its prevalence expe...

An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).

The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, r...

Clinical-level screening of sleep apnea syndrome with single-lead ECG alone is achievable using machine learning with appropriate time windows.

PURPOSE: To establish a simple and noninvasive screening test for sleep apnea (SA) that imposes less...

Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks.

BACKGROUND: Thyroid nodules are frequent in clinical settings, and their diagnosis in adults is grow...

Enhanced electrocardiogram classification using Gramian angular field transformation with multi-lead analysis and segmentation techniques.

Conventional manual or feature-based ECG analysis methods are limited by time inefficiencies and hum...

ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an in...

Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.

Advancements in medical technology have extended long-term electrocardiogram (ECG) monitoring from t...

ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking.

Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and co...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest...

An Energy-Efficient Configurable 1-D CNN-Based Multi-Lead ECG Classification Coprocessor for Wearable Cardiac Monitoring Devices.

Many electrocardiogram (ECG) processors have been widely used for cardiac monitoring. However, most ...

Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach.

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular r...

Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is a common but often undiagnosed condition, increasing the ris...

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analys...

Artificial-intelligence-enhanced ECG: a potential solution to limitations of ECG in detecting left ventricular dysfunction after chemotherapy.

A prediction model based on machine learning: prognosis of HBV-induced HCC male patients with smoking and drinking habits after local ablation treatment.

BACKGROUND: Liver cancer, particularly hepatocellular carcinoma (HCC), is a major health concern glo...

Synthetic ECG signal generation using generative neural networks.

Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases....

Machine learning models for prediction of NPVR ≥80% with HIFU ablation for uterine fibroids.

BACKGROUND: Currently high-intensity focused ultrasound (HIFU) is widely used to treat uterine fibro...

A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods.

BACKGROUND: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular con...