Analysis of Data from Polymeric Pressure Sensor for Cardiovascular Risk Assessment

Cardiovascular disease (CVD) is a leading cause of death and disability, with 1 in 3 of us at risk of the disease.
Screening for relevant biomarkers regularly can help prevent and manage CVD. This project focuses on the
acquisition and analysis of CVD biomarkers obtained using a device consisting of polymeric pressure sensors and
advanced machine learning algorithms. The biomarkers will be obtained by extracting features from dynamic
pressure changes captured by the polymeric pressure sensor. Deep learning and neuromorphic models will be
developed to automatically classify pressure changes data based on their Signal Quality Index (SQI) scores and
to determine relevant CVD parameters in a computationally efficient manner on edge devices.

Faculty Supervisor:

Nilanjan Ray

Student:

Partner:

Synapsis Medical

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

University of Alberta

Program: