Cardiovascular

Arrhythmias

Latest AI and machine learning research in arrhythmias for healthcare professionals.

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A CNN-based Local-Global Self-Attention via Averaged Window Embeddings for Hierarchical ECG Analysis

Cardiovascular diseases remain the leading cause of global mortality, emphasizing the critical nee...

Application of Contrastive Learning on ECG Data: Evaluating Performance in Japanese and Classification with Around 100 Labels

The electrocardiogram (ECG) is a fundamental tool in cardiovascular diagnostics due to its powerfu...

(1)
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning

Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but cl...

An Empirical Investigation of Reconstruction-Based Models for Seizure Prediction from ECG Signals

Epileptic seizures are sudden neurological disorders characterized by abnormal, excessive neuronal...

ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation

Camouflaged object segmentation presents unique challenges compared to traditional segmentation ta...

A Systematic Literature Review of Unmanned Aerial Vehicles for Healthcare and Emergency Services

Unmanned aerial vehicles (UAVs), initially developed for military applications, are now used in va...

Dense Neural Network Based Arrhythmia Classification on Low-cost and Low-compute Micro-controller

The electrocardiogram (ECG) monitoring device is an expensive albeit essential device for the trea...

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Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases

Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for ...

Susceptibility of Large Language Models to User-Driven Factors in Medical Queries

Large language models (LLMs) are increasingly used in healthcare, but their reliability is heavily...

The Role of Computational Modeling in Enhancing Thermal Safety During Cardiac Ablation

Objective: In this review, we aim to provide an analysis of current cardiac ablation techniques, s...

Integrating Notch Filtering and Statistical Methods for Improved Cardiac Diagnostics Using MATLAB

A Notch Filter is essential in ECG signal processing to eliminate narrowband noise, especially pow...

Enhanced ECG Arrhythmia Detection Accuracy by Optimizing Divergence-Based Data Fusion

AI computation in healthcare faces significant challenges when clinical datasets are limited and h...

Neuro-Informed Adaptive Learning (NIAL) Algorithm: A Hybrid Deep Learning Approach for ECG Signal Classification

The detection of cardiac abnormalities using electrocardiogram (ECG) signals is crucial for early ...

Towards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring

Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such ...

(1)
TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis

Electrocardiogram (ECG) signals are widely shared across multiple clinical applications for diagno...

Are ECGs enough? Deep learning classification of cardiac anomalies using only electrocardiograms

Electrocardiography (ECG) is an essential tool for diagnosing multiple cardiac anomalies: it provi...

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 arrhythmia...

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 pa...

(1)
Mar 2025 40110765
GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images

While recent multimodal large language models (MLLMs) have advanced automated ECG interpretation, ...

(1)
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