Ph.D. Dissertation Defense - Hazar Ashouri

TitleRobust Estimation of Systolic Time Intervals using Ballistocardiogram and Seismocardiogram

Committee:

Dr. Omer Inan, ECE, Chair , Advisor

Dr. Pamela Bhatti, ECE

Dr. David Anderson, ECE

Dr. Amit Shah, Emory

Dr. Mozziyar Etemadi, Northwestern

Abstract:

The objective of this dissertation is to bridge some of the existing gaps in research for non-invasive measurement of systolic time intervals, specifically pre-ejection period (PEP), using ballistocardiogram (BCG) and seismocardiogram (SCG) signals, to pave the way for out-of-clinic, non-invasive, unobtrusive, and reliable monitoring of patients with heart failure. New hardware to measure BCG was explored by using a high bandwidth force plate and further signal processing techniques which allowed for significant improvement in absolute measurements of PEP, and measurements of changes in stroke volume, over current state-of-the-art technology. Additionally, since SCG signals measure local vibrations, the relationship between sensor placement and the morphology of the signals was investigated. This was done by designing a robust algorithm that distinguishes between changes in morphology resulting from placement and changes resulting from physical activities, and consequently, detects misplacement of the SCG sensors allowing for robust PEP monitoring in unsupervised settings. Moreover, different placements and interfaces of SCG sensors, on the upper body, were explored to identify the ideal position/ combination of positions. This showed, for the first time, that better PEP estimates can be obtained by placements different than what is currently used in research. Finally, a universal ensemble regression model, that uses multiple features to estimate PEP from SCG signals, is presented in this work. This algorithm bypasses the lack of a well-defined standard to detect the aortic valve (AO) opening from SCG, resulting from the signals morphology being affected by age, sex and heart condition.

Event Details

Date/Time:

  • Friday, December 8, 2017
    1:00 pm - 3:00 pm
Location: Room 1177, ES&T