| KEYWORD |
Prediction of cardiovascular complications using multi-modal data
Parole chiave CARDIOVASCULAR DISEASES, MACHINE LEARNING, SENSOR FUSION, SIGNAL PROCESSING, WEARABLE DEVICES
Riferimenti LUIGI BORZI', GABRIELLA OLMO
Gruppi di ricerca SYSTEM BIOLOGY GROUP - SYSBIO
Tipo tesi EXPERIMENTAL APPLIED
Descrizione The thesis will focus on the development of robust and efficient processing algorithms for cardiovascular disease prediction and/or monitoring. Data recorded by wearable sensors, which include physical (motion) and physiological (heart activity, blood pressure) signals, will be analyzed. Machine learning models will be trained and extensively validated, with the ultimate goal of long-term, continuous, home monitoring in daily life.
Conoscenze richieste - Matlab or Python programming
- Signal processing
- Basic knowledge of machine learning models
Scadenza validita proposta 06/10/2024
PROPONI LA TUA CANDIDATURA