Year

2023

Season

Summer

Paper Type

Master's Thesis

College

College of Computing, Engineering & Construction

Degree Name

Master of Science in Electrical Engineering (MSEE)

Department

Engineering

NACO controlled Corporate Body

University of North Florida. School of Engineering

First Advisor

Dr. Mona Nasseri

Second Advisor

Dr. Patrick Kreidl

Third Advisor

Dr. AmirHossein MajidiRad

Fourth Advisor

Dr. Benjamin H. Brinkmann

Department Chair

Dr. Alan Harris

College Dean

Dr. William Klostermeyer

Abstract

Physiological signals are used in engineering and the sciences to determine the state of functionality of certain physiological systems such as the human endometrium. Many women use the basal body temperature method to track ovulation, or their fertile window. However, temperature tracking alone is subject to environmental factors. Non-invasive wearable devices can be tools in recording cycle-related physiological features relevant to women’s health research. This research uses the Empatica E4 wristband to track changes in the physiological features during sleep across the menstrual cycle of ovulating and non-ovulating females. Due to the cyclic nature of menstrual cycles, analytic solutions to estimate changes in ovulating and non-ovulating subjects is conducted using circular statistics.

The presented study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics. The results can be used empirically to determine the changes in the menstrual phases (follicular phase, ovulation, and the luteal phase). A biphasic pattern was observed in ovulating subjects, with a significant periodicity (p0.05). The Watson- Williams test shows a significant difference between ovulating and non-ovulating cycles (p<0.05) in temperature, IBI, and EDA but not in average HR. Incorporating additional physiological features may serve as a more accurate method for tracking and predicting ovulation.

In addition, this study compares physiological signals of the menstrual phases of healthy ovulating and non-ovulating subjects to subjects with epilepsy. As a preliminary exploration, a linear approach was used to determine seizure patterns during the analyzed menstrual cycles. Understanding the naturally occurring physiological signals during the menstrual cycle, which is the scope of this thesis, allows researchers to investigate whether seizure patterns have affects on the menstrual cycle, or vice versa.

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