Title: Optimizing Noninvasive Uterine Electrophysiological Imaging: Advancements in Electrode Reduction, Signal Enhancement, and Robust Data Processing

Abstract: In recent years, noninvasive electrophysiological systems have significantly advanced the visualization and analysis of electrical activity in human organs without the need for invasive procedures. Among these innovative systems, Electromyometrial Imaging (EMMI) and Uterine Peristalsis Imaging (UPI) have emerged as powerful tools for studying uterine electrical behavior — EMMI during pregnancy and UPI throughout the menstrual cycle. By enabling the reconstruction of detailed four-dimensional electrical activation patterns, these technologies offer unprecedented insights into uterine physiology and function. 

However, current procedures for noninvasive electrophysiological imaging systems face several challenges, including the need for a large number of electrodes, low-quality electrical measurements due to noise and motion artifacts, and a lack of robust algorithms for signal processing. This dissertation aims to address these issues by optimizing the critical components of the workflow: electrode reduction, signal enhancement, and data processing.

First, we propose a method to reduce the number of electrodes while maintaining imaging quality, thereby improving clinical feasibility. Second, we enhance signal quality by leveraging the relationship between slow and fast wave components in the uterine electrical signals, improving the signal-to-noise ratio to enable better detection of uterine contractions. Finally, we introduce improved post-processing techniques tailored to the complex, multi-channel, and geometry-dependent nature of EMMI data, allowing for more accurate and robust identification of uterine contractions.