Arrhythmias

From Geometric Recovery to Causal Validation: A Reproducible Audit of Sparse Autoencoder Features, from Superposition Geometry to Causal Inertness

Sparse autoencoders (SAEs) are the standard for decomposing superposed neural representations into interpretable features, and evaluation relies predominantly on correlational recovery metrics -- cosine similarity between ground-truth directions and decoder atoms. We show this conflates two distinct claims: decoder-geometry alignment and encoder-activation behavior. We reproduce the superposition ...

Revealing Hidden Myocardial Infarction Signatures from Brief Single-Lead Electrocardiograms: A Novel Framework for Smart Wearable Applications

The electrocardiogram (ECG) contains rich nonlinear and non-stationary dynamic information that is only partly captured by conventional ECG interpretation and beat-to-beat metrics, and is increasingly analyzed using black-box artificial intelligence models that often lack interpretability. Here, we introduce the ECG time-frequency "eyeball", an interpretable framework that transforms a brief singl...

Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational Study

Background: Heart failure with reduced ejection fraction (HFrEF) remains a major global health burden. Most electrocardiogram (ECG)-based artificial i...

SPARC-Net: A Spectral, Causality-Aware, and Hard-Constrained Physics-Informed Architecture for Stiff and Shock-Dominated Partial Differential Equations

Physics-Informed Neural Networks (PINNs) provide a meshless approach for solving partial differential equations (PDEs), but suffer severe degradation ...

Alignment-Free RoPE-Based Dual-Stream Transformer for PPG-Guided Neonatal ECG Segment Inpainting in the NICU

Adhesive electrocardiography (ECG) electrodes used in neonatal intensive care units (NICUs) may cause skin injury in premature infants. Although photo...

Cross-Modal Generative Framework for Signal Translation from Fetal-Maternal Electrocardiograms to Fetal Doppler Waveforms

Fetal electrocardiogram (fECG) and Doppler ultrasound provide complementary views of fetal cardiovascular function: fECG captures electrical activity ...

ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening

Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper E...

Dual Attention Heads for Personalized Federated Learning in ECG Classification

Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data. However, the inherent heterog...

Robust Longitudinal Dementia Prediction under Systemic Missingness via Hierarchical Fusion and Test-Time Adaptation

Longitudinal dementia progression prediction is essential for clinical decision-making. However, models often degrade on external cohorts due to syste...

ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment

Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly...

OHCA-EXTRACT: Evaluating the Accuracy of a Large Language Model Pipeline for Out-of-Hospital Cardiac Arrest Case Identification and Utstein Variable Extraction

Background: Manual identification and abstraction of out-of-hospital cardiac arrest (OHCA) cases and Utstein template variables from electronic health...

A curated reference dataset and deep learning model for multi-lead electrocardiographic interval measurements in UK Biobank

Electrocardiographic (ECG) interval measurements underpin clinical decision-making and large-scale cardiovascular research, yet existing automated met...

A Reproducible Clinical Decision-Support Suite on MIMIC-IV

Most published clinical-AI results are single models on a single dataset, difficult to reproduce, and rarely validated outside their training hospital...

Retrieval-Warmed Energy-Based Reasoning: A Five-Arm Ablation Methodology for Diffusion-as-Inference on Structured Reasoning Tasks

Warm-started diffusion samplers accelerate iterative inference, but it is rarely clear which part of the pipeline carries the gain. We study \textbf{r...

Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy

Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to b...

Agentic Autodiscovery of Diastolic Dysfunction Phenotypes from Surface Electrocardiogram

Background: Left ventricular diastolic dysfunction (LVDD) is a major determinant of heart failure (HF), yet its assessment relies on multiparametric e...

Image-based deep learning for emergency electrocardiogram classification

Automated electrocardiogram analysis has advanced largely through digital waveforms, yet many emergency-care workflows rely on ECGs available only as ...

Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study

Multimodal large language models (LLMs) are increasingly adopted to interpret 12-lead ECG images, though the interpretations often lack validation. Ho...

Deep learning-based detection of cessation of breathing in pre-term infants

Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monito...

SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models

Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichann...