5 papers
On Sleep-Wake Disorder Identification from Polysomnographic Representations
Alvise Dei Rossi, Luigi Fiorillo, Giuliana Monachino +6
Self-supervised polysomnography foundation models (PSG FMs) aim to capture clinically useful information beyond sleep staging, yet are rarely compared with sleep-staging encoders p…
Do ECG Foundation Models Transfer to Rare Cardiac Diseases? Evidence from Brugada Syndrome Detection
Beatrice Zanchi, Giuliana Monachino, Alvise Dei Rossi +4
Background: Foundation models (FMs) trained on large-scale unlabeled physiological data have emerged as a promising paradigm for medical artificial intelligence. Their ability to c…
NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals
Alvise Dei Rossi, Julia van der Meer, Markus H. Schmidt +4
Physiological signals are inherently heterogeneous: they are collected under diverse acquisition setups, differ in the number and type of modalities and channels, varying in qualit…
Self-DANA: A Resource-Efficient Channel-Adaptive Self-Supervised Approach for ECG Foundation Models
Giuliana Monachino, Nicolò La Porta, Beatrice Zanchi +4
Foundation Models (FMs) are large-scale machine learning models trained on extensive, diverse datasets that can be adapted to a wide range of downstream tasks with minimal fine-tun…
SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models
Alvise Dei Rossi, Matteo Metaldi, Michal Bechny +7
Despite advances in deep learning for automatic sleep staging, clinical adoption remains limited due to challenges in fair model evaluation, generalization across diverse datasets,…