46 citations · 80 across the 8 of their papers we have counts for
10 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,…
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…
Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring
Luigi Fiorillo, Davide Pedroncelli, Valentina Agostini +2
Study Objectives: Inter-scorer variability in scoring polysomnograms is a well-known problem. Most of the existing automated sleep scoring systems are trained using labels annotate…
DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates
Luigi Fiorillo, Paolo Favaro, Francesca Dalia Faraci
Deep learning is widely used in the most recent automatic sleep scoring algorithms. Its popularity stems from its excellent performance and from its ability to directly process raw…