| KEYWORD |
Prediction of cardiovascular complications using multi-modal data
keywords CARDIOVASCULAR DISEASES, MACHINE LEARNING, SENSOR FUSION, SIGNAL PROCESSING, WEARABLE DEVICES
Reference persons LUIGI BORZI', GABRIELLA OLMO
Research Groups SYSTEM BIOLOGY GROUP - SYSBIO
Thesis type EXPERIMENTAL APPLIED
Description 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.
Required skills - Matlab or Python programming
- Signal processing
- Basic knowledge of machine learning models
Deadline 06/10/2024
PROPONI LA TUA CANDIDATURA