46 citations · 53 across the 4 of their papers we have counts for
6 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…
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…
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…
Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review
Michal Bechny, Giuliana Monachino, Luigi Fiorillo +5
Purpose: This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in t…
U-Sleep's resilience to AASM guidelines
Luigi Fiorillo, Giuliana Monachino, Julia van der Meer +7
AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines…