Arrhythmias

A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility

The classification of electrocardiogram (ECG) signals is crucial for early detection of arrhythmias and other cardiac conditions. However, despite advances in machine learning, many studies fail to follow standardization protocols, leading to inconsistencies in performance evaluation and real-world applicability. Additionally, hardware constraints essential for practical deployment, such as in p...

Predicting early recurrence of hepatocellular carcinoma after thermal ablation based on longitudinal MRI with a deep learning approach.

BACKGROUND: Accurate prediction of early recurrence (ER) is essential to improve the prognosis of patients with hepatocellular carcinoma (HCC) underwent thermal ablation (TA). Therefore, a deep learning model system using longitudinal magnetic resonance imaging (MRI) was developed to predict ER of patients with HCC.

GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images

While recent multimodal large language models (MLLMs) have advanced automated ECG interpretation, they still face two key limitations: (1) insuffici...

Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment

Pulmonary hypertension (PH) is frequently underdiagnosed in low- and middle-income countries (LMICs) primarily due to the scarcity of advanced diagn...

Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders

Cardiovascular diseases (CVDs) are disorders impacting the heart and circulatory system. These disorders are the foremost and continuously escalatin...

ECG-EmotionNet: Nested Mixture of Expert (NMoE) Adaptation of ECG-Foundation Model for Driver Emotion Recognition

Driver emotion recognition plays a crucial role in driver monitoring systems, enhancing human-autonomy interactions and the trustworthiness of Auton...

Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study.

BACKGROUND AND AIMS: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study ...

Estimating Blood Pressure with a Camera: An Exploratory Study of Ambulatory Patients with Cardiovascular Disease

Hypertension is a leading cause of morbidity and mortality worldwide. The ability to diagnose and treat hypertension in the ambulatory population is...

OpenECG: Benchmarking ECG Foundation Models with Public 1.2 Million Records

This study introduces OpenECG, a large-scale benchmark of 1.2 million 12-lead ECG recordings from nine centers, to evaluate ECG foundation models (E...

Segmenting Bi-Atrial Structures Using ResNext Based Framework

Atrial fibrillation (AF) is the most common cardiac arrhythmia, significantly contributing to mortality, particularly in older populations. While pu...

Parallel-Learning of Invariant and Tempo-variant Attributes of Single-Lead Cardiac Signals: PLITA

Wearable sensing devices, such as Holter monitors, will play a crucial role in the future of digital health. Unsupervised learning frameworks such a...

SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning

Cardiovascular diseases are a leading cause of death and disability worldwide. Electrocardiogram (ECG) recordings are critical for diagnosing and mo...

Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputs

Recent advances in multimodal ECG representation learning center on aligning ECG signals with paired free-text reports. However, suboptimal alignmen...

Arrhythmia Classification from 12-Lead ECG Signals Using Convolutional and Transformer-Based Deep Learning Models

In Romania, cardiovascular problems are the leading cause of death, accounting for nearly one-third of annual fatalities. The severity of this situa...

[The joint analysis of heart health and mental health based on continual learning].

Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (EC...

[Research on arrhythmia classification algorithm based on adaptive multi-feature fusion network].

Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides...

CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals

This study addresses the challenge of reconstructing unseen ECG signals from PPG signals, a critical task for non-invasive cardiac monitoring. While...

rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics

The variability in ECG readings influenced by individual patient characteristics has posed a considerable challenge to adopting automated ECG analys...

Detecting Long QT Syndrome and First-Degree Atrioventricular Block using Single-Lead AI-ECG: A Multi-Center Real-World Study

Home-based single-lead AI-ECG devices have enabled continuous, real-world cardiac monitoring. However, the accuracy of parameter calculations from s...

Cardiac Evidence Backtracking for Eating Behavior Monitoring using Collocative Electrocardiogram Imagining

Eating monitoring has remained an open challenge in medical research for years due to the lack of non-invasive sensors for continuous monitoring and...