Arrhythmias

Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images.

Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and comprehensive bone metastases of breast cancer. WBS images with breast cancer bone metastasis have the characteristics of low resolution, small foreground, and multiple lesions, hindering the widespread application of deep learning-based models. Automatically detecting a large number of densely small ...

A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform.

This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequency domain to extract relevant features. Subsequently, a CNN is employed to capture local patterns, while a transformer architecture learns long-term de...

Joint fusion of EHR and ECG data using attention-based CNN and ViT for predicting adverse clinical endpoints in percutaneous coronary intervention patients.

Predicting post-Percutaneous Coronary Intervention (PCI) outcomes is crucial for effective patient management and quality improvement in healthcare. H...

Circadian rhythm modulation in heart rate variability as potential biomarkers for major depressive disorder: A machine learning approach.

Major depressive disorder (MDD) is associated with reduced heart rate variability (HRV), but its link to circadian rhythm modulation (CRM) of HRV is u...

Classifying metro drivers' cognitive distractions during manual operations using machine learning and random forest-recursive feature elimination.

Metro drivers are more likely to trigger accidents if they suffer from cognitive distractions during manual driving. However, identifying metro driver...

Reliability and validity of a novel single-lead portable electrocardiogram device for pregnant women: a comparative study.

BACKGROUND: WenXinWuYang, a novel portable Artificial Intelligence Electrocardiogram (AI-ECG) device, can detect many kinds of abnormal heart disease ...

Deep Learning-Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study.

BACKGROUND: In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory fu...

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women.

Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditio...

Investigation of Inter-Patient, Intra-Patient, and Patient-Specific Based Training in Deep Learning for Classification of Heartbeat Arrhythmia.

Effective diagnosis of electrocardiogram (ECG) is one of the simplest and fastest ways to assess the heart's function. In the recent decade, various a...

An advanced robotic system incorporating haptic feedback for precision cardiac ablation procedures.

This study introduces an innovative master-slave cardiac ablation catheter robot system that employs magnetorheological fluids. The system incorporate...

A Novel Framework for Quantum-Enhanced Federated Learning with Edge Computing for Advanced Pain Assessment Using ECG Signals via Continuous Wavelet Transform Images.

Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level asse...

Author name disambiguation based on heterogeneous graph neural network.

With the dramatic increase in the number of published papers and the continuous progress of deep learning technology, the research on name disambiguat...

Hypoglycemia Prediction in Type 1 Diabetes With Electrocardiography Beat Ensembles.

INTRODUCTION: Current methods to detect hypoglycemia in type 1 diabetes (T1D) require invasive sensors (ie, continuous glucose monitors, CGMs) that ge...

Deep Learning Approach for Automatic Heartbeat Classification.

Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram ...

Atrial fibrillation risk model based on LASSO and SVM algorithms and immune infiltration of key mitochondrial energy metabolism genes.

Atrial fibrillation (AF) is a predominant cardiac arrhythmia with unclear etiology. This study used bioinformatics and machine learning to explore the...

The Evolving Paradigm of Myocardial Infarction in the Era of Artificial Intelligence.

The classification and treatment of myocardial infarction (MI) have evolved significantly over the past few decades, with the ST-segment elevation myo...

EffNet: an efficient one-dimensional convolutional neural networks for efficient classification of long-term ECG fragments.

Early Diagnosis of Cardiovascular disease (CVD) is essential to prevent a person from death in case of a cardiac arrhythmia. Automated ECG classificat...

Ventricular Arrhythmia Classification Using Similarity Maps and Hierarchical Multi-Stream Deep Learning.

OBJECTIVE: Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventr...

AI-Cirrhosis-ECG (ACE) score for predicting decompensation and liver outcomes.

BACKGROUND & AIMS: Accurate prediction of disease severity and prognosis are challenging in patients with cirrhosis. We evaluated whether the deep lea...

Protocol for AI-based segmentation and quantification of interstitial cells of Cajal in murine gastric muscle.

Interstitial cells of Cajal (ICCs), pacemaker and neuromodulator cells in the gastrointestinal (GI) tract, play an important role in GI motility. Howe...