Robust Algorithms for Unattended Monitoring of Cardiovascular Health

Cardiovascular disease is the leading cause of death in the United States. Tracking daily changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular disease, such as heart failure and hypertension. A toilet seat is the ideal device for monitoring parameters relating to a subject’s cardiac health in his or her home, because it is used consistently and requires no change in daily habit. The present work demonstrates the ability to accurately capture clinically relevant ECG metrics, pulse transit time based blood pressures, and other parameters across subjects and physiological states using a toilet seat-based cardiovascular monitoring system, enabled through advanced signal processing algorithms and techniques. The algorithms described herein have been designed for use with noisy physiologic signals measured at non-standard locations. A key component of these algorithms is the classification of signal quality, which allows automatic rejection of noisy segments before feature delineation and interval extractions. The present delineation algorithms have been designed to work on poor quality signals while maintaining the highest possible temporal resolution. When validated on standard databases, the custom QRS delineation algorithm has best-in-class sensitivity and precision, while the photoplethysmogram delineation algorithm has best-in-class temporal resolution. Human subject testing on normative and heart failure subjects is used to evaluate the efficacy of the proposed monitoring system and algorithms. Results show that the accuracy of the measured heart rate and blood pressure are well within the limits of AAMI standards. For the first time, a single device is capable of monitoring long-term trends in these parameters while facilitating daily measurements that are taken at rest, prior to the consumption of food and stimulants, and at consistent times each day. This system has the potential to revolutionize in-home cardiovascular monitoring

A ZigBee-based wireless biomedical sensor network as a precursor to an in-suit system for monitoring astronaut state of health

Master of ScienceDepartment of Electrical and Computer EngineeringSteven WarrenNetworks of low-power, in-suit, wired and wireless health sensors offer the potential to track and predict the health of astronauts engaged in extra-vehicular and in-station activities in zero- or reduced- gravity environments. Fundamental research questions exist regarding (a) types and form factors of biomedical sensors best suited for these applications, (b) optimal ways to render wired/wireless on-body networks with the objective to draw little-to-no power, and (c) means to address the wireless transmission challenges offered by a spacesuit constructed from layers of aluminized mylar. This thesis addresses elements of these research questions through the implementation of a collection of ZigBee-based wireless health monitoring devices that can potentially be integrated into a spacesuit, thereby providing continuous information regarding astronaut fatigue and state of health. Wearable biomedical devices investigated for this effort include electrocardiographs, electromyographs, pulse oximeters, inductive plethysmographs, and accelerometers/gyrometers. These ZigBee-enabled sensors will form the nodes of an in-suit ZigBee Pro network that will be used to (1) establish throughput requirements for a functional in-suit network and (2) serve as a performance baseline for future devices that employ ultra-low-power field-programmable gate arrays and micro-transceivers. Sensor devices will upload data to a ZigBee network coordinator that has the form of a pluggable USB connector. Data are currently visualized using MATLAB and LabVIEW

Survey on wireless body area sensor networks for healthcare applications: Signal processing, data analysis and feedback

Wireless sensor networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries.The wireless body area sensor networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements.The paper surveys the state-of-the-art on WBASNs discussing the major components of research in this area including physiological sensing, data preprocessing, detection and classification of human related phenomena. We provide comparative studies of the technologies and techniques used in such systems

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

Due to the high demands of tiny, compact, lightweight, and low-cost photoplethysmogram (PPG) monitoring devices, these devices are resource-constrained including limited battery power. Consequently, it highly demands frequent charge or battery replacement in the case of continuous PPG sensing and transmission. Further, PPG signals are often severely corrupted under ambulatory and exercise recording conditions, leading to frequent false alarms. In this paper, we propose a unified quality-aware compression and pulse-respiration rates estimation framework for reducing energy consumption and false alarms of wearable and edge PPG monitoring devices by exploring predictive coding techniques for jointly performing signal quality assessment (SQA), data compression and pulse rate (PR) and respiration rate (RR) estimation without the use of different domains of signal processing techniques that can be achieved by using the features extracted from the smoothed prediction error signal. By using the five standard PPG databases, the performance of the proposed unified framework is evaluated in terms of compression ratio (CR), mean absolute error (MAE), false alarm reduction rate (FARR), processing time (PT) and energy saving (ES). The compression, PR, RR estimation, and SQA results are compared with the existing methods and results of uncompressed PPG signals with sampling rates of 125 Hz and 25 Hz. The proposed unified qualityaware framework achieves an average CR of 4%, SQA (Se of 92.00%, FARR of 84.87%), PR (MAE: 0.46 ±1.20) and RR (MAE: 1.75 (0.65-4.45), PT (sec) of 15.34 ±0.01) and ES of 70.28% which outperforms the results of uncompressed PPG signal with a sampling rate of 125 Hz. Arduino Due computing platformbased implementation demonstrates the real-time feasibility of the proposed unified quality-aware PRRR estimation and data compression and transmission framework on the limited computational resources. Thus, it has great potential in improving energy-efficiency and trustworthiness of wearable and edge PPG monitoring devices.publishedVersio

Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

A Photoplethysmography System Optimised for Pervasive Cardiac Monitoring

Photoplethysmography is a non-invasive sensing technique which infers instantaneous cardiac function from an optical measurement of blood vessels. This thesis presents a photoplethysmography based sensor system that has been developed speci fically for the requirements of a pervasive healthcare monitoring system. Continuous monitoring of patients requires both the size and power consumption of the chosen sensor solution to be minimised to ensure the patients will be willing to use the device. Pervasive sensing also requires that the device be scalable for manufacturing in high volume at a build cost that healthcare providers are willing to accept. System level choice of both electronic circuits and signal processing techniques are based on their sensitivity to cardiac biosignals, robustness against noise inducing artefacts and simplicity of implementation. Numerical analysis is used to justify the implementation of a technique in hardware. Circuit prototyping and experimental data collection is used to validate a technique's application. The entire signal chain operates in the discrete-time domain which allows all of the signal processing to be implemented in firmware on an embedded processor which minimised the number of discrete components while optimising the trade-off between power and bandwidth in the analogue front-end. Synchronisation of the optical illumination and detection modules enables high dynamic range rejection of both AC and DC independent light sources without compromising the biosignal. Signal delineation is used to reduce the required communication bandwidth as it preserves both amplitude and temporal resolution of the non-stationary photoplethysmography signals allowing more complicated analytical techniques to be performed at the other end of communication channel. The complete sensing system is implemented on a single PCB using only commercial-off -the-shelf components and consumes less than 7.5mW of power. The sensor platform is validated by the successful capture of physiological data in a harsh optical sensing environment

A Photoplethysmography System Optimised for Pervasive Cardiac Monitoring

Photoplethysmography is a non-invasive sensing technique which infers instantaneous cardiac function from an optical measurement of blood vessels. This thesis presents a photoplethysmography based sensor system that has been developed speci fically for the requirements of a pervasive healthcare monitoring system. Continuous monitoring of patients requires both the size and power consumption of the chosen sensor solution to be minimised to ensure the patients will be willing to use the device. Pervasive sensing also requires that the device be scalable for manufacturing in high volume at a build cost that healthcare providers are willing to accept. System level choice of both electronic circuits and signal processing techniques are based on their sensitivity to cardiac biosignals, robustness against noise inducing artefacts and simplicity of implementation. Numerical analysis is used to justify the implementation of a technique in hardware. Circuit prototyping and experimental data collection is used to validate a technique's application. The entire signal chain operates in the discrete-time domain which allows all of the signal processing to be implemented in firmware on an embedded processor which minimised the number of discrete components while optimising the trade-off between power and bandwidth in the analogue front-end. Synchronisation of the optical illumination and detection modules enables high dynamic range rejection of both AC and DC independent light sources without compromising the biosignal. Signal delineation is used to reduce the required communication bandwidth as it preserves both amplitude and temporal resolution of the non-stationary photoplethysmography signals allowing more complicated analytical techniques to be performed at the other end of communication channel. The complete sensing system is implemented on a single PCB using only commercial-off -the-shelf components and consumes less than 7.5mW of power. The sensor platform is validated by the successful capture of physiological data in a harsh optical sensing environment

NASA Tech Briefs, April 2007

Topics include: Wearable Environmental and Physiological Sensing Unit; Broadband Phase Retrieval for Image-Based Wavefront Sensing; Filter Function for Wavefront Sensing Over a Field of View; Iterative-Transform Phase Retrieval Using Adaptive Diversity; Wavefront Sensing With Switched Lenses for Defocus Diversity; Smooth Phase Interpolated Keying; Maintaining Stability During a Conducted-Ripple EMC Test; Photodiode Preamplifier for Laser Ranging With Weak Signals; Advanced High-Definition Video Cameras; Circuit for Full Charging of Series Lithium-Ion Cells; Analog Nonvolatile Computer Memory Circuits; JavaGenes Molecular Evolution; World Wind 3D Earth Viewing; Lithium Dinitramide as an Additive in Lithium Power Cells; Accounting for Uncertainties in Strengths of SiC MEMS Parts; Ion-Conducting Organic/Inorganic Polymers; MoO3 Cathodes for High-Temperature Lithium Thin-Film Cells; Counterrotating-Shoulder Mechanism for Friction Stir Welding; Strain Gauges Indicate Differential-CTE-Induced Failures; Antibodies Against Three Forms of Urokinase; Understanding and Counteracting Fatigue in Flight Crews; Active Correction of Aberrations of Low-Quality Telescope Optics; Dual-Beam Atom Laser Driven by Spinor Dynamics; Rugged, Tunable Extended-Cavity Diode Laser; Balloon for Long-Duration, High-Altitude Flight at Venus; and Wide-Temperature-Range Integrated Operational Amplifier

Compression and Multi-Spectral Sensing for Video Based Physiological Monitoring

Remote physiological monitoring is an active area of research that extends monitoring capabilities traditionally found in a clinical setting towards the home, telehealth, and beyond. In particular, there is interest in leveraging consumer electronic devices for sensing physiological characteristics such as heart rate, heart rate variability, and blood oxygen saturation. This thesis focuses on enhancing the understanding and usage of the sensing component for these applications to improve the performance and quality of cardio-physiological monitoring. First, a close relationship between the color spaces used for video compression and the color projection planes commonly used for heart rate estimation is identified. % that results in higher compression of the physiological signal. The study demonstrates the impact of this observation on real and synthetic data to provide a foundation to guide future video coding to optimize its configurations to better preserve the heart rate signal for health related applications. Second, an investigation with a commercial-off-the-shelf (COTS) multi-spectral sensor is presented with key observations related to the sampling rate, exposure settings, and multi-channel processing. These observations will enable better usage of the sensor for future studies and data collections that leverage the more precise spectral measurements from the multi-spectral sensor compared to standard RGB cameras