Arrhythmias

Biomarker Signal Architecture in Cardiovascular Machine Learning: Stability, Redundancy, and Minimal High-Yield Panels After Myocardial Infarction

Background: Machine-learning models based on circulating biomarkers are increasingly used in cardiovascular research; however, model performance alone provides limited insight into how the predictive signal is distributed across features. We aimed to characterize the biomarker signal architecture of a machine-learning model distinguishing ST-elevation myocardial infarction (STEMI) from non-ST-elev...

CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation

Real-time cognitive load assessment is essential for adaptive human-computer interaction but remains challenging due to limited labeled data and poor cross-subject generalization. Recent ECG foundation models pre-trained on millions of clinical recordings offer rich representations, but cannot be directly applied to wearable devices due to sensor configuration mismatch and task differences. In thi...

Membrane proteomics of the Drosophila circadian neural network

Circadian behaviors are controlled by dedicated brain pacemaker neurons, whose activity oscillate during the day and the night. The Drosophila brain c...

Real-World Validation of Machine Learning Models for HIV Treatment Adherence Prediction and Care Gap Quantification: A Multi-Country Analysis of 192,732 Clinical Records

Delayed diagnosis and poor antiretroviral therapy (ART) adherence remain primary drivers of HIV-related morbidity in low-resource settings, yet real-w...

HexagonalWarriorMamba: Superior Threshold-Dependent Multi-label Classification of 12-Lead ECG Cardiac Abnormalities

The accurate automated diagnosis of cardiac abnormalities from 12-lead electrocardiograms (ECGs) is critical for managing cardiovascular disease. Howe...

Ensemble Post-hoc Explainable AI for Multilead ECG: Identifying Disease-Relevant Features in Single-Lead Interpretations

Despite the growing success of deep learning (DL) in multivariate time-series classification, such as 12-lead electrocardiography (ECG), widespread in...

How Do Electrocardiogram Models Scale?

While scaling laws have established a fundamental framework for foundation models in natural language processing, their applicability to electrocardio...

Von Economo neurons enable reliable social skill acquisition in recurrent spiking neural networks: a computational account with clinical predictions

Von Economo neurons (VENs) are selectively lost in behavioural-variant frontotemporal dementia (bvFTD) and reduced in autism spectrum conditions (ASC)...

Attention-Guided Fusion of 1D and 2D CNNs for Robust ECG-Based Biometric Recognition

Electrocardiogram (ECG)-based biometric recognition has emerged as a promising solution for secure authentication and liveness detection. However, mos...

Risk of apnoea-related cardiorespiratory instability in preterm infants is modulated by clinical, demographic and dynamic indicators

Background: Apnoea of prematurity is common and may cause desaturation and/or bradycardia. There is marked variability in infants cardiorespiratory re...

Towards a General Approach for Bat Echolocation Detection and Classification

Acoustic monitoring is a scalable approach for assessing bat populations, yet automating the detection and classification of bat echolocation calls re...

Predicting the When: Multimodal AI for Time-to-Recurrence Analysis After Atrial Fibrillation Ablation

Background: Catheter ablation is the most effective rhythm control strategy for atrial fibrillation (AF); however, recurrence remains common. Current ...

Rheumatic Heart Disease Detection in Asymptomatic Schoolchildren using ECG and PCG

Rheumatic heart disease (RHD) remains a major public health concern across low- and middle-income countries in the Global South. Early detection throu...

Estimation of Physiological Metrics from Resting ECGs Using Deep Learning in the UK Biobank, Including submaximal exercise derived VO2max, Body Fat Percentage, and Grip Strength

Maximal oxygen consumption VO2max is the gold standard for cardiorespiratory fitness but requires resource-intensive physical testing. Recent reports ...

Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study

Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the siz...

Cadence: A Benchmark Evaluation of the Narrative Velocity Framework for Next Clinical Event Prediction in MIMIC-IV

Objective: How structured clinical features and cluster-semantic embeddings interact under self-distillation in EHR prediction models is unknown. Exis...

BioMADE: Predicting Torsades de Pointes from molecular structures through biologically informed representations

Drug-induced arrhythmias, particularly Torsades de Pointes (TdP), pose a significant risk to patient safety and can sometimes have life-threatening ou...

Attractor-Vascular Coupling Theory: Formal Grounding and Empirical Validation for AAMI-Standard Cuffless Blood Pressure Estimation from Smartphone Photoplethysmography

This work proposes Attractor-Vascular Coupling Theory (AVCT), a mathematical framework showing that cardiac attractor geometry encodes blood pressure ...

An electrocardiogram-based machine learning model for distinguishing complete Kawasaki disease.

Kawasaki disease (KD) is a systemic vasculitis in young children, and early diagnosis remains challenging when clinical features are incomplete or ove...

Screening for Rheumatic Heart Disease in Asymptomatic Children using Machine Learning from Electrocardiograms

Early detection of Rheumatic Heart Disease (RHD) is essential in reducing its associated mortality and late complications. In resource-limited setting...