Inferencing with motion artifacts - bed based BCG and MRI case studies

Abstract

A signal is a physical quantity which differs with respect to time, space, or other independent variables and contains information about a physical phenomenon. In biomedical applications, analysis and evaluations of signals acquired with bio-measurement tools help with health monitoring and treatment. Biomedical signals may be easily affected by noise arising due to imprecision in measuring instruments, uncertainty within the systems, patient movements, or interference from external sources. Therefore, in many cases, it is necessary to process the signal in order to reduce or eliminate unwanted signals and improve reliability of inferencing. Motivated by these factors, the present dissertation addresses some key challenges associated with inferencing with motion artifacts in two biomedical sensing modalities as 1D and 2D signals: (1) ballistocardiogram (BCG), and (2) magnetic resonance imaging (MRI).

In the first case study, we determine the effect of motion artifacts on a bed-based BCG signal as a 1D biomedical signal. Specifically, we develop a motion detection algorithm followed by a motion artifact removal system using an unobtrusive contact-less electromechanical film based ballistocardiogram sensor integrated into a smart bed system. Furthermore, we leverage the results from the motion detection algorithm to define a restlessness metric that enables us to quantify sleep quality. We demonstrate the use of this algorithm using data from individuals with autism spectrum disorder (ASD) with a collaboration with Heartspring, Wichita, Kansas. Additionally, we illustrate the value of using the restlessness metric along with other indicators of sleep to predict daytime behaviors in this speci fic population.

In the second case study, we focus on identifying temperature changes due to device or organ movement in proton resonance frequency shift (PRFS) MR-thermometry as 2D biomedical sensing modality. Firstly, we develop an enhanced hybrid method suitable for MRI thermometry in the presence of motion during microwave thermal therapy for heating using diffuse sources such as needle- and catheter-based microwave applicators. The presented model-based method uses the sparsity of wavelet coefficients of the phase shift based on the fact that heat-induced phase shifts exhibit a correlation structure due to smoothness. Secondly, we propose a new robust motion correction algorithm based on robust principal component analysis (RPCA) to recover low-rank matrix from sparse errors without the need of modeling. We illustrate the efficiency of both works by comparing the results to the previously presented model-based method and fi ber-optic temperature sensors during microwave heating of a tissue-mimicking phantom with a 2.45 GHz directional microwave antenna integrated with 14.1 T high- field MRI.