More than one million fetal deaths occur in the United States every year [1]. Monitoring the long-term heart rate variability provides a great amount of information about the fetal health condition which requires continuous monitoring of the fetal heart rate. All the existing technologies have either complex instrumentation or need a trained professional at all times or both. The existing technologies are proven to be impractical for continuous monitoring [2]. Hence, there is an increased interest towards noninvasive, continuous monitoring, and less expensive technologies like fetal phonocardiography. Fetal Phonocardiography (FPCG) signal is obtained by placing an acoustic transducer on the abdomen of the mother. FPCG is rich in physiological bio-signals and can continuously monitor the fetal heart rate non-invasively. Despite its high diagnostic potential, it is still not being used as the secondary point of care. There are two challenges as to why it is still being considered as the secondary point of care; in the data acquisition system and the signal processing methodologies. The challenges pertaining to data acquisition systems are but not limited to sensor placement, maternal obesity and multiple heart rates. While, the challenges in the signal processing methodologies are dynamic nature of FPCG signal, multiple known and unknown signal components and SNR of the signal. Hence, to improve the FPCG based care, challenges in FPCG signal processing methodologies have been addressed in this study. A comparative evaluation was presented on various advanced signal processing techniques to extract the bio-signals with fidelity. Advanced signal processing approaches, namely empirical mode decomposition, spectral subtraction, wavelet decomposition and adaptive filtering were used to extract the vital bio-signals. However, extracting these bio-signals with fidelity is a challenging task in the context of FPCG as all the bio signals and the unwanted artifacts overlap in both time and frequency. Additionally, the signal is corrupted by noise induced from the fetal and maternal movements as well the background and the sensor. Empirical mode decomposition algorithm was efficient to denoise and extract the maternal and fetal heart sounds in a single step. Whereas, spectral subtraction was used to denoise the signal which was later subjected to wavelet decomposition to extract the signal of interest. On the other hand, adaptive filtering was used to estimate the fetal heart sound from a noisy FPCG where maternal heart sound was the reference input. The extracted signals were validated by obtaining the frequency ranges computed by the Short Time Fourier Transform (STFT). It was observed that the bandwidths of extracted fetal heart sounds and maternal heart sounds were consistent with the existing gold standards. Furthermore, as a means of additional validation, the heart rates were calculated. Finally, the results obtained from all these methods were compared and contrasted qualitatively and quantitatively.

Centers for Disease Control and Prevention (CDC) estimate that more than 11.2 million people require critical and emergency care in the United States per year. Optimizing and improving patient morbidity and mortality outcomes are the primary objectives of monitoring in critical and emergency care. Patients in need of critical or emergency care in general are at a risk of single or multiple organ failures occurring due to a traumatic injury, a surgical event, or an underlying pathology that results in severe patient hemodynamic instability. Hence, continuous monitoring of fundamental cardiovascular hemodynamic parameters, such as heart rate, respiratory rate, blood pressure, blood oxygenation and core temperature, is essential to accomplish diagnostics in critical and emergency care. Today’s standard of care measures these critical parameters using multiple monitoring technologies. Though it is possible to measure all the fundamental cardiovascular hemodynamic parameters using the blood flow dynamics, its use is currently only limited to measuring continuous blood pressure. No other comparable studies in the literature were successful in quantifying other critical parameters from the blood flow dynamics for a few reasons. First, the blood flow dynamics exhibit a complicated and sensitive dynamic pressure field. Existing blood flow based data acquisition systems are unable to detect these sensitive variations in the pressure field. Further, the pressure field is also influenced by the presence of background acoustic interference, resulting in a noisy pressure profile. Thus in order to extract critical parameters from this dynamic pressure field with fidelity, there is need for an integrated framework that is composed of a highly sensitive data acquisition system and advanced signal processing. In addition, existing state-of-the-art technologies require expensive instrumentation and complex infrastructure. The information sensed using these multiple monitoring technologies is integrated and visualized using a clinical information system. This process of integration and visualization creates the need for functional interoperability within the multiple monitoring technologies. Limited functional interoperability not only results in diagnostic errors but also their complexity makes it impossible to use such technologies to accomplish monitoring in low resource settings. These multiple monitoring technologies are neither portable nor scalable, in addition to inducing extreme patient discomfort. For these reasons, existing monitoring technologies do not efficiently meet the monitoring and diagnostic requirements of critical and emergency care. In order to address the challenges presented by existing blood flow based data acquisition systems and other monitoring systems, a point of care monitoring device was developed to provide multiple critical parameters by means of uniquely measuring a physiological process. To demonstrate the usability of this novel catheter multiscope, a feasibility study was performed using an animal model. The corresponding results are presented in this dissertation. The developed measurement system first acquires the dynamics of blood flow through a minimally invasive catheter. Then, a signal processing framework is developed to characterize the blood flow dynamics and to provide critical parameters such as heart rate, respiratory rate, and blood pressure. The framework used to extract the physiological data corresponding to the acoustic field of the blood flow consisted of a noise cancellation technique and a wavelet based source separation. The preliminary results of the acoustic field of the blood flow revealed the presence of acoustic heart and respiratory pulses. A unique and novel framework was also developed to extract continuous blood pressure from the pressure field of the blood flow. Finally, the computed heart and respiratory rates, systolic and diastolic pressures were benchmarked with actual values measured using conventional devices to validate the measurements of the catheter multiscope. In summary, the results of the feasibility study showed that the novel catheter multiscope can provide critical parameters such as heart rate, respiratory rate and blood pressure with clinical accuracy. In addition, this dissertation also highlights the diagnostic potential of the developed catheter multiscope by presenting preliminary results of proof of concept studies performed for application case studies such as sinus rhythm pattern recognition and fetal monitoring through phonocardiography.