College of Computing, Engineering & Construction
Master of Science in Electrical Engineering (MSEE)
NACO controlled Corporate Body
University of North Florida. School of Engineering
Dr. Mona Nasseri
Dr. Patrick Kreidl
Dr. AmirHossein MajidiRad
Dr. Benjamin H. Brinkmann
Dr. Alan Harris
Dr. William Klostermeyer
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.
Sides, Krystal D., "Analyzing Physiological Signals During the Menstrual Cycle: Introducing an Application of Circular Statistics" (2023). UNF Graduate Theses and Dissertations. 1215.
Available for download on Thursday, August 07, 2025