Development of algorithms and signal enhancement techniques for seismocardiogram-based hemodynamic parameter estimation

Continuous monitoring plays a critical role in bridging the information gap that exists between periodic hospital visits. Recent advancements in sensor technology and biomedical signal processing have paved the way to the development of sensor systems capable of continuous monitoring of physiological signals and diagnosing various pathologies. Among various types of physiological signals, the seismocardiogram (SCG) is particularly popular in wearable system design. The SCG corresponds to the chest micro-vibrations caused by blood ejection and heart contraction during each cardiac cycle. This thesis focuses on developing algorithms to derive various hemodynamic parameters and enhancement methods for SCG signal analysis. These methods aim to improve signal quality, enable accurate feature extraction, and enhance the reliability of SCG-based physiological assessment. Chapter 2 explores the temporal and spectral relationships between SCG signals and hemodynamic parameters like pre-ejection period (PEP) and left ventricular ejection time (LVET). It presents regression models to estimate these parameters and investigates the relationship between thorax impedance and SCG-derived features. Chapter 3 introduces a two-step hierarchical framework for apnea detection and respiration pace assessment using SCG signals. This model includes binary classification for detecting breath-holding episodes and multi-class classification for categorizing breathing patterns (normal, slow, fast). Chapter 4 presents a comparative evaluation of various denoising algorithms aimed at improving SCG signal quality during exercise. It assesses the performance of different heart rate estimation methods and compares the impact of seven different denoising algorithms on SCG-based heart rate estimation. Overall, these findings collectively support the versatility and potential of SCG in advancing personalized healthcare, particularly in wearable health monitoring and continuous physiological assessment.