Arrhythmias

Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data.

Seizure forecasting and affective state analysis using EEG-ECG data play a pivotal role in advancing neurological and mental health monitoring. However, existing methods such as Fed-Transformer, Res-1D CNN, and Fed-ESD suffer from privacy risks, inefficient feature extraction, and high computational overhead, limiting their effectiveness in real-world applications. To overcome these challenges, th...

Extending Multiscale Characterization of Heart Rate Variability via Deep Learning for Mortality Risk Prediction.

OBJECTIVE: To improve mortality risk prediction from heart rate variability (HRV) signals by capturing nonlinear scaling patterns often overlooked by traditional linear analyses. METHODS: This study combines detrended moving average (DMA) analysis with convolutional neural networks (CNNs). DMA curves were computed from 2-hour overlapping windows of 24-hour Holter ECG recordings in 916 survivors an...

Stabilizing Extreme Few-Shot ECG Classification via Self-Supervised Contrastive Pretraining.

In clinical electrocardiogram (ECG) analysis, high-quality annotations are expensive and difficult to scale, leaving many tasks in an extreme few-shot...

Empowering atrial fibrillation detection with TabPFN and SHAP interpretation based on ECG-derived features: a dual-center temporal validation study.

Atrial fibrillation (AF) remains a leading driver of stroke and heart failure, yet timely diagnosis is frequently hindered by its asymptomatic nature ...

Assessment models for driving comfort and fatigue status based on heart rate variability.

OBJECTIVES: This study, from an interdisciplinary perspective of human factors engineering and biomedical engineering, aims to develop a real-time ass...

A teacher-student deep learning framework for enhanced clinical screening of heart failure from 12-lead electrocardiograms.

OBJECTIVE: Identifying heart failure (HF) from electrocardiograms (ECG) is challenging due to the lack of definitive features. This study aims to deve...

A dynamic light image enhancement algorithm using generative adversarial network for group activity recognition.

Video-based or image-based human activity recognition (HAR) via machine learning algorithms helps track, detect, and categorize users' daily activitie...

Exploratory association between multimodal AI-derived digital biomarkers and in-hospital mortality in adult patients with pneumonia: A proof-of-concept study.

Pneumonia remains a leading cause of in-hospital mortality worldwide. Current prognostic tools such as the IDSA/ATS severity score have meaningful lim...

The Rlign algorithm for enhanced electrocardiogram analysis through heart rate-corrected ECG alignment for explainable classification and clustering.

AIMS: Electrocardiogram (ECG) recordings are fundamental for diagnosing cardiac conditions. Recent advances in automatic ECG analysis have been domina...

AgroDualNet: a dual deep learning-based crop disease forecasting and fruit ripening detection.

Proper diagnosis of crop diseases and accurate measurement of fruit ripeness is essential in enhancing agricultural productivity, but conventional met...

Circulating miR-10b-5p as a Candidate Biomarker of Atrial Fibrillation Recurrence After Catheter Ablation: A Two-Phase Translational Study.

BACKGROUND: The identification of reliable biomarkers for atrial fibrillation (AF) recurrence post-catheter ablation remains a clinical challenge. Thi...

Artificial Intelligence-Enabled ECG for Elevated E/e' on Echocardiography: Hemodynamic Relevance and Prognostic Value.

BACKGROUND: Left ventricular filling pressure is associated with heart failure symptoms and a key prognostic marker and therapeutic target, but a scal...

External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.

An ECG-based artificial intelligence (AI) model was previously developed to generate ten digital biomarkers for emergency and cardiac assessment and i...

Integrating high-fidelity hiPSC-cardiomyocytes with AI-driven modeling for enhanced proarrhythmic risk assessment.

Cardiotoxicity remains the leading driver of drug attrition; however, its prediction remains suboptimal when conventional hERG assays and animal model...

Optimizing Cine Cardiac MRI: Technical Advances and Clinical Applications.

Cine cardiac magnetic resonance imaging (MRI) is the gold standard for cardiac function assessment, offering exceptional spatial and temporal resoluti...

Protocol to benchmark and evaluate the status of imbalance measure using correlation, data complexity, and ablation analyses.

Machine learning struggles with imbalanced data. Although several mitigation approaches exist, their application depends on the extent of imbalance. T...

HeartBERT: A self-supervised ECG embedding model for efficient and effective medical signal analysis.

The analysis of Electrocardiogram (ECG) signals is critical for clinical applications, but current machine learning methods often face limitations whe...

A self-supervised electrocardiogram foundation model for empowering cardiovascular disease prediction and genetic factor discovery.

Electrocardiogram (ECG) has been widely used in the diagnosis of cardiovascular disease (CVD). Current deep learning methods for CVD prediction using ...

Prediction of Atrial Fibrillation Using Artificial Intelligence-Enhanced Electrocardiography　- Does Left Atrial Size Matter?

BACKGROUND: Artificial intelligence-enhanced electrocardiography (AI-ECG) for detecting atrial fibrillation (AF) using sinus rhythm ECGs has shown pro...

Prediction of Pregnancy-Related Cardiovascular Outcomes Using Electrocardiogram-Based Deep Learning Estimation of Cardiorespiratory Fitness.

BACKGROUND: Peak oxygen consumption (peak VO2), the gold standard measure of cardiorespiratory fitness, may identify women at high risk for pregnancy-...