MS Thesis Defense: Yiqi Li

Tuesday, June 18, 2019
11:00 a.m.
Kim 2211
Emily Irwin
301 405 0680
eirwin@umd.edu

ANNOUNCEMENT: MS Thesis Defense
 
Name: Yiqi Li
 
Date/Time: Tuesday, June 18, 2019, from 11-1 pm
 
Location: 2211 Kim Building
 
Committee:
Professor Min Wu, Chair / Advisor
Professor K. J. Ray Liu
Professor Furong Huang
 
Title: Analysis and refinement of pulse rate variability
 
Abstract: 
 
Heart rate variability (HRV), calculated from the cardiac intervals of electrocardiogram (ECG), is a promising marker of the cardiovascular system status and fitness. However, ECG signal is not always available and photoplethysmogram (PPG) is easier to obtain, and more widely used in clinical is running HRV analysis on pulse-to-pulse intervals of PPG signal, which is usually referred to as pulse rate variability (PRV). Thus, whether PRV can be used as a substitution of HRV is of substantial interest to researchers.


In this thesis, two issues about PRV are discussed. The first issue is the selection of characteristic point, which determines the length and location of the pulse-to-pulse interval and will affect the agreement between PRV and HRV. Six characteristic points of PPG pulse are extracted and the agreement between HRV and corresponding PRV is calculated and compared in two situations, subjects with cardiovascular diseases (CVD) and subjects without cardiovascular diseases (non-CVD). The result indicates that pulse peak is most suitable for CVD subjects, and 50% max amplitude point and 75% max amplitude point on pulse slope are most suitable for non-CVD subjects.

The second issue studied in this thesis is the PRV refinement using arterial blood pressure (ABP) information. The relationship between systolic blood pressure extracted from ABP signal and pulse transit time (PTT) is modeled using linear kernel support vector regression (SVR) and RBF kernel SVR separately. Estimated PTT is used to adjust the location of PPG pulse-to-pulse intervals. PRV after adjustment is calculated, and its agreement to HRV is compared with the original PRV. For CVD subjects, the improvement to the agreement is limited, and only the agreement for variables representing long-term variability is improved. For non-CVD subjects, there is a relatively large improvement for approximately all variables after refinement and linear kernel outperforms RBF kernel in this situation.

Audience: Faculty 

 

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