3 papers
cs.LG2026
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…
eess.SP2025
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…
eess.SP2024
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…