6 papers
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,…
Unveiling Sleep Dysregulation in Chronic Fatigue Syndrome with and without Fibromyalgia Through Bayesian Networks
Michal Bechny, Marco Scutari, Julia van der Meer +4
Chronic Fatigue Syndrome (CFS) and Fibromyalgia (FM) often co-occur as medically unexplained conditions linked to disrupted physiological regulation, including altered sleep. Build…
Comparison analysis between standard polysomnographic data and in-ear-EEG signals: A preliminary study
Gianpaolo Palo, Luigi Fiorillo, Giuliana Monachino +8
Study Objectives: Polysomnography (PSG) currently serves as the benchmark for evaluating sleep disorders. Its discomfort makes long-term monitoring unfeasible, leading to bias in s…