Arrhythmias

Artificial intelligence approaches for non-invasive diabetes prediction using ECG signals: A systematic review.

Diabetes is a major global health challenge, with many individuals remaining undiagnosed due to the limitations of traditional screening methods. Artificial intelligence (AI)-based electrocardiogram (ECG) analysis offers a promising, non-invasive approach for the early detection of diabetes. This systematic review aims to critically evaluate machine learning (ML) and deep learning (DL) models deve...

Validation and longitudinal trajectory analysis of an AI-based ECG model for aortic stenosis: from community screening to pre-TAVR risk stratification.

AIMS: Early aortic stenosis (AS) detection remains challenging, with many patients presenting late when left ventricular dysfunction may be irreversible. We evaluated whether longitudinal AI-enhanced ECG patterns can predict outcomes years before intervention and assessed the community screening potential of the AK-AVS model. METHODS AND RESULTS: We conducted two complementary analyses: (1) commun...

Association and Agreement of Contact-Based Smartphone Photoplethysmography Compared With Electrocardiography: Protocol for a Systematic Review and Meta-Analysis.

BACKGROUND: Mobile health (mHealth), leveraging mobile devices for health measurement and promotion, is rapidly growing. Smartphone cameras can perfor...

FHGNet: A Feature-Centric Hierarchical Network with Graph Attention Layer for Supraventricular Tachycardia Classification.

Automated electrocardiogram (ECG) classification plays a critical role in arrhythmia diagnosis. However, current deep learning-based methodologies fre...

ECG and PPG Signals-Based Premature Ventricular Contraction Detection Methods: A Review, Key Challenges, and Future Directions.

Premature ventricular contraction (PVC) is a common cardiac arrhythmia, and its timely and automated detection is crucial for preventing life-threaten...

Reablation of atrial fibrillation targeting electrogram dispersion in patients with isolated veins: The RESTART trial.

BACKGROUND: Pulmonary vein (PV) isolation is a well-established treatment for atrial fibrillation (AF), however, strategies for patients with recurren...

Development and multicentre validation of an artificial intelligence electrocardiogram model for ventricular remodeling in repaired tetralogy of Fallot.

AIMS: Periodic cardiac MRI (CMR) is recommended to identify adverse ventricular remodelling in repaired tetralogy of Fallot (TOF), but access to CMR i...

Explainable multi-modal approach for uncovering key predictors of stroke-risk from ECG, EMG, blood pressure, and respiratory signals.

Accurate and timely stroke-risk prediction is necessary to help patients at risk take guided measures, as stroke remains a leading cause of death and ...

An Energy-Efficient ECG Classifier With On-Chip Learning Using Binarized Convolutional Neural Network.

In ECG classification applications, binarized convolutional neural networks (bCNNs) show great potential to achieve extremely low power consumption th...

MMRCL: An interpretable multi-modal deep learning framework for predicting hERG blockers.

The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel essential for cardiac action potential repolarization. Drug-indu...

A meta-heuristic aided arrhythmia classification model using advanced deep learning technique with multiple feature extraction mechanisms.

Cardiac arrhythmia poses an important threat to human life; hence it is an urge to diagnose properly. There are numerous mechanisms deployed for the i...

Data-related Ablation for Reinforcing Deep Learning in Explaining Complex Phenomena.

Deep Learning (DL) models excel at automatically learning intricate patterns within complex data, but their black box nature undermines human trust. T...

[Regression analysis to calculate the time point of ROSC-A feasibility study].

BACKGROUND: A regression model to estimate the duration from the onset of resuscitation efforts to the return of spontaneous circulation (ROSC) could ...

End-to-End Platform for Electrocardiogram Analysis and Model Fine-Tuning: Development and Validation Study.

BACKGROUND: Electrocardiogram (ECG) data constitutes one of the most widely available biosignal data in clinical and research settings, providing crit...

SLPM: a lightweight deep learning model for end-to-end paper ECG digitization.

OBJECTIVE: The digitization of paper electrocardiograms (ECGs) faces several challenges, including amplified errors during segmentation and signal ext...

Use of Electrocardiograms to Identify Coronary Artery Disease: Cross-Validation of an Artificial Intelligence Model.

BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure...

Roof-dependent atrial tachycardia after pulsed field ablation-mechanistic insights and predictive modeling for optimized ablation strategies: an ultra-high-density mapping study.

BACKGROUND AND AIMS: Pulsed field ablation (PFA) has emerged to an innovative approach to achieve pulmonary vein isolation (PVI) in atrial fibrillatio...

A hybrid CNN and reinforcement learning framework for speaker identification using Mel-Spectrogram and continuous wavelet transform features.

Speaker identification remains critical in biometric authentication systems, requiring robust feature extraction strategies that capture speaker-speci...

Unbiased Characterization of Atrial Fibrillation Phenotypic Architecture Provides Insight Into Genetic Liability and Clinically Relevant Outcomes.

BACKGROUND: Atrial fibrillation (AF) is a common and clinically heterogeneous arrhythmia. Machine learning algorithms can define data-driven disease s...

6G conditioned spatiotemporal graph neural networks for real time traffic flow prediction.

Accurate, low-latency traffic forecasting is a cornerstone capability for next-generation Intelligent Transportation Systems (ITS). This paper investi...