Use of machine learning techniques for detection of early signs of cardiac deconditioning from ECG

Degree Course: 
Biomedical/Electronic/Computer science engineer (Information-engineering track)
Location: 
Department of Information Engineering - UNIVPM
Description: 

With ageing, immobilization condition due to mobility problems is becoming more and more frequent in older subjects. This situation affects the cardiovascular system in many ways, electrically and mechanically, thus inducing cardiac deconditioning similar to what experienced by astronauts in weightlessness. Data (12 leads Holter ECG) are already available, acquired during multiple head-down bed rest campaigns organized by the European Space Agency. The aim of this thesis is to use machine learning techniques to train an algorithm able to detect changes in ECG that could be related to progressive cardiac deconditioning.

Aim: The aim of this thesis is to use machine learning techniques to train an algorithm able to detect changes in ECG that could be related to progressive cardiac deconditioning.

Supervisors: Prof. Enrico Caiani (POLIMI), Prof. Emanuele Frontoni, Sara Moccia

Start: Sept, 2018

Expected graduation: Feb/July, 2019

Skills that will be acquired: Programming (Python, MATLAB)

Contacts: {e.frontoni, s.moccia}@univpm.it

Academic Tutor: 
Emanuele Frontoni