Estimation of Blood Oxygen Content Using Context-Aware Filtering

In this paper we address the problem of estimating the blood oxygen concentration in children during surgery.Currently, the oxygen content can only be measured through invasive means such as drawing blood from the patient. In this work, we attempt to perform estimation by only using other non-invasive measurements (e.g., fraction of oxygen in inspired air, volume of inspired air) collected during surgery. Although models mapping these measurements to blood oxygen content contain multiple parameters that vary widely across patients, the non-invasive measurements can be used to provide binary information about whether the oxygen concentration is rising or dropping. This information can then be incorporated in a context-aware filter that is used to combine regular continuous measurements with discrete detection events in order to improve estimation. We evaluate the filter using real-patient data collected over the last decade at the Children’s Hospital of Philadelphia and show that it is a promising approach for the estimation of unobservable physiological variables

Context-Aware Sensor Fusion For Securing Cyber-Physical Systems

The goal of this dissertation is to provide detection and estimation techniques in order to ensure the safety and security of modern Cyber-Physical Systems (CPS) even in the presence of arbitrary sensors faults and attacks. We leverage the fact that modern CPS are equipped with various sensors that provide redundant information about the system\u27s state. In such a setting, the system can limit its dependence on any individual sensor, thereby providing guarantees about its safety even in the presence of arbitrary faults and attacks. In order to address the problem of safety detection, we develop sensor fusion techniques that make use of the sensor redundancy available in modern CPS. First of all, we develop a multidimensional sensor fusion algorithm that outputs a bounded fusion set which is guaranteed to contain the true state even in the presence of attacks and faults. Furthermore, we provide two approaches for strengthening sensor fusion\u27s worst-case guarantees: 1) incorporating historical measurements as well as 2) analyzing sensor transmission schedules (e.g., in a time-triggered system using a shared bus) in order to minimize the attacker\u27s available information and impact on the system. In addition, we modify the sensor fusion algorithm in order to provide guarantees even when sensors might experience transient faults in addition to attacks. Finally, we develop an attack detection technique (also in the presence of transient faults) in order to discard attacked sensors. In addition to standard plant sensors, we note that modern CPS also have access to multiple environment sensors that provide information about the system\u27s context (e.g., a camera recognizing a nearby building). Since these context measurements are related to the system\u27s state, they can be used for estimation and detection purposes, similar to standard measurements. In this dissertation, we first develop a nominal context-aware filter (i.e., with no faults or attacks) for binary context measurements (e.g., a building detection). Finally, we develop a technique for incorporating context measurements into sensor fusion, thus providing guarantees about system safety even in cases where more than half of standard sensors might be under attack

Context-Aware Detection in Medical Cyber-Physical Systems

This paper considers the problem of incorporating context in medical cyber-physical systems (MCPS) applications for the purpose of improving the performance of MCPS detectors. In particular, in many applications additional data could be used to conclude that actual measurements might be noisy or wrong (e.g., machine settings might indicate that the machine is improperly attached to the patient); we call such data context. The first contribution of this work is the formal definition of context, namely additional information whose presence is associated with a change in the measurement model (e.g., higher variance). Given this formulation, we developed the context-aware parameter-invariant (CA-PAIN) detector; the CA-PAIN detector improves upon the original PAIN detector by recognizing events with noisy measurements and not raising unnecessary false alarms. We evaluate the CA-PAIN detector both in simulation and on real-patient data; in both cases, the CA-PAIN detector achieves roughly a 20-percent reduction of false alarm rates over the PAIN detector, thus indicating that formalizing context and using it in a rigorous way is a promising direction for future work

Wireless body area sensor networks signal processing and communication framework: Survey on sensing, communication technologies, delivery and feedback

Problem statement: 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.This study surveys the state-of-the-art on Wireless Body Area Networks, discussing the major components of research in this area including physiological sensing and preprocessing, WBASNs communication techniques and data fusion for gathering data from sensors.In addition, data analysis and feedback will be presented including feature extraction, detection and classification of human related phenomena.Approach: Comparative studies of the technologies and techniques used in such systems will be provided in this study, using qualitative comparisons and use case analysis to give insight on potential uses for different techniques.Results and Conclusion: Wireless Sensor Networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries.Sensor supply chain and communication technologies used within the system and power consumption therein, depend largely on the use case and the characteristics of the application.Authors conclude that Life-saving applications and thorough studies and tests should be conducted before WBANs can be widely applied to humans, particularly to address the challenges related to robust techniques for detection and classification to increase the accuracy and hence the confidence of applying such techniques without physician intervention

Graph Laplacian for Image Anomaly Detection

Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD's limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer

Fast quantitative susceptibility mapping with L1-regularization and automatic parameter selection

Purpose To enable fast reconstruction of quantitative susceptibility maps with total variation penalty and automatic regularization parameter selection. Methods ℓ[subscript 1]-Regularized susceptibility mapping is accelerated by variable splitting, which allows closed-form evaluation of each iteration of the algorithm by soft thresholding and fast Fourier transforms. This fast algorithm also renders automatic regularization parameter estimation practical. A weighting mask derived from the magnitude signal can be incorporated to allow edge-aware regularization. Results Compared with the nonlinear conjugate gradient (CG) solver, the proposed method is 20 times faster. A complete pipeline including Laplacian phase unwrapping, background phase removal with SHARP filtering, and ℓ[subscript 1]-regularized dipole inversion at 0.6 mm isotropic resolution is completed in 1.2 min using MATLAB on a standard workstation compared with 22 min using the CG solver. This fast reconstruction allows estimation of regularization parameters with the L-curve method in 13 min, which would have taken 4 h with the CG algorithm. The proposed method also permits magnitude-weighted regularization, which prevents smoothing across edges identified on the magnitude signal. This more complicated optimization problem is solved 5 times faster than the nonlinear CG approach. Utility of the proposed method is also demonstrated in functional blood oxygen level–dependent susceptibility mapping, where processing of the massive time series dataset would otherwise be prohibitive with the CG solver. Conclusion Online reconstruction of regularized susceptibility maps may become feasible with the proposed dipole inversion