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Modern Automatic Blood Pressure Measurement Techniques are based on measuring the cuff pressure and on sensing the pulsatile amplitude variations. These measurements are very sensitive to motion of the patient or the surroundings where the patient is. The slightest unexpected movements could offset the readings of the automatic Blood Pressure meter by a large amount or render the readings totally meaningless. Every effort must be taken to avoid subjecting the body of the patient or the patient's surroundings to motion for obtaining a reliable reading. But there are situations in which we need Blood Pressure Measurements with the patient or his surroundings in motion; for instance in an ambulance while a patient is being transported to a hospital. In this thesis, we present a technique to reduce the effect of motion artifact from Blood Pressure measurements. We digitize the blood pressure waveform and use Digital Signal Processing Techniques to process the corrupted waveform. We use the differences in frequency spectra of the Blood Pressure signal and motion artifact noise to remove the motion artifact noise. The motion artifact noise spectrum is not very well defined, since it may consist of many different frequency components depending on the kind of motion. The Blood Pressure signal is more or less a periodic signal. That translates to periodicity in the frequency domain. Hence, we designed a digital filter that could take advantage of the periodic nature of the Blood Pressure Signal waveform. The filter is shaped like a comb with periodic peaks around the signal frequency components. Further processing of the filtered signal: baseline restoration and level shifting help us to further reduce the noise corruption.

Blood pressure (BP) monitoring is an important tool for management of hypertension, which is a significant risk for cardiovascular disease and premature death. Since cuff-based BP measurement can be uncomfortable and does not provide continuous readings, several cuffless methods that are typically based on within-beat information or on the pulse transit time (PTT) have recently been investigated. This work proposes a novel cuffless BP estimation approach that mainly uses the information from cardiovascular dynamics of photoplethysmogram (PPG) waveforms. This work is divided into three parts. The first part proposes a calibration-free approach that uses dynamic changes in the pulse waveform. Results from 200 patients showed that the method achieved grade B, in terms of accuracy, for diastolic blood pressure (DBP) based on the British Hypertension Society (BHS) standard and complied with the accuracy requirements of the Association for Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) standard. The second part presents a method based on calibrated cardiovascular dynamics, achieved through a mathematical model that relates reflective PTT (R-PTT) to BP. Results from 30 patients showed a mean error (ME) of 0.58 mmHg, standard deviation of the error (SDE) of 8.13 mmHg, and a mean absolute error (MAE) of 4.93 mmHg for DBP and an ME of 2.52 mmHg, SDE of 12.28 mmHg, and an MAE of 8.82 mmHg for systolic blood pressure (SBP). The third part proposes a calibration-free method that combines morphology features and dynamic changes of the pulse waveform over short intervals. In this method a neural network was trained on 200 patients and tested on never-seen data from 25 other patients and provided an ME of -0.31 mmHg, SDE of 4.89 mmHg, and MAE of 3.32 mmHg for DBP and an ME of -4.02 mmHg, SDE of 10.40 mmHg, and MAE of 7.41 mmHg for SBP. Overall, the results show that cardiovascular dynamics may contribute useful information for cuffless estimation of BP.

This research presents a pulse-by-pulse analysis of Oscillometric blood pressure waveform at systolic, diastolic and mean arterial pressure points. Using a mathematical optimization technique, pulses are characterized into component harmonic by minimizing the least square error. The results at the important pressure points are analyzed and compared for different subject using different waveform extraction techniques. Blood pressure is estimated using the harmonic parameters. The approach studies changes in the parameters as oscillometric blood pressure recording is done. 8 harmonic parameters are obtained from the pulse characterization and are used to estimate Systolic arterial Blood Pressure, Mean arterial Blood Pressure, and Diastolic arterial Blood Pressure. The estimates are compared with our reference value to determine which has the best agreement. The proposed method is further compared with Maximum Amplitude Algorithm and Pulse Morphology Algorithm. The effect of oscillometric waveform extraction methods on the proposed method is observed. The experiment established the fact that the extraction technique can alter the shape of oscillometric pulses. The methods were compared and it was observed that the used extraction methods did not make any significant difference on the accuracy, using this technique.

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.