Towards Non-Invasive Monitoring of Hypovolemia in Intensive Care Patients

Hypovolemia caused by internal hemorrhage is a major cause of death in critical care patients. However, hypovolemia is difficult to diagnose in a timely fashion, as obvious symptoms do not manifest until patients are already nearing a critical state of shock. Novel non-invasive methods for detecting hypovolemia in the literature utilize the photoplethysmogram (PPG) waveform generated by the pulse-oximeter attached to a finger or ear. Until now, PPG-based alarms have been evaluated only on healthy patients under ideal testing scenarios (e.g., motionless patients); however, the PPG is sensitive to patient health and significant artifacts manifest when patients move. Since patient health varies within the intensive care unit (ICU) and ICU patients typically do not remain motionless, this work introduces a PPG-based monitor designed to be robust to waveform artifacts and health variability in the underlying patient population. To demonstrate the promise of our approach, we evaluate the proposed monitor on a small sample of intensive care patients from the Physionet database. The monitor detects hypovolemia within a twelve hour window of nurse documentation of hypovolemia when it is present, and achieves a low false alarm rate over patients without documented hypovolemia

Parameter-Invariant Design of Medical Alarms

The recent explosion of low-power low-cost communication, sensing, and actuation technologies has ignited the automation of medical diagnostics and care in the form of medical cyber physical systems (MCPS). MCPS are poised to revolutionize patient care by providing smarter alarm systems, clinical decision support, advanced diagnostics, minimally invasive surgical care, improved patient drug delivery, and safety and performance guarantees. With the MCPS revolution emerges a new era in medical alarm systems, where measurements gathered via multiple devices are fused to provide early detection of critical conditions. The alarms generated by these next generation monitors can be exploited by MCPS to further improve performance, reliability, and safety. Currently, there exist several approaches to designing medical monitors ranging from simple sensor thresholding techniques to more complex machine learning approaches. While all the current design approaches have different strengths and weaknesses, their performance degrades when underlying models contain unknown parameters and training data is scarce. Under this scenario, an alternative approach that performs well is the parameter-invariant detector, which utilizes sufficient statistics that are invariant to unknown parameters to achieve a constant false alarm rate across different systems. Parameter-invariant detectors have been successfully applied in other cyber physical systems (CPS) applications with structured dynamics and unknown parameters such as networked systems, smart buildings, and smart grids; most recently, the parameter-invariant approach has been recently extended to medical alarms in the form of a critical shunt detector for infants undergoing a lung lobectomy. The clinical success of this case study application of the parameter-invariant approach is paving the way for a range of other medical monitors. In this tutorial, we present a design methodology for medical parameter-invariant monitors. We begin by providing a motivational review of currently employed medical alarm techniques, followed by the introduction of the parameter-invariant design approach. Finally, we present a case study example to demonstrate the design of a parameter-invariant alarm for critical shunt detection in infants during surgical procedures

Robust Monitoring of Hypovolemia in Intensive Care Patients Using Photoplethysmogram Signals

The paper presents a fingertip photoplethysmography based technique to assess patient fluid status that is robust to waveform artifacts and health variability in the underlying patient population. The technique is intended for use in intensive care units, where patients are at risk for hypovolemia, and signal artifacts and inter-patient variations in health are common. Input signals are preprocessed to remove artifact, then a parameter-invariant statistic is calculated to remove effects of patient-specific physiology. Patient data from the Physionet MIMICII database was used to evaluate the performance of this technique. The proposed method was able to detect hypovolemia within 24 hours of onset in all hypovolemic patients tested, while producing minimal false alarms over non-hypovolemic patients

Parameter-Invariant Monitor Design for Cyber Physical Systems

The tight interaction between information technology and the physical world inherent in Cyber-Physical Systems (CPS) can challenge traditional approaches for monitoring safety and security. Data collected for robust CPS monitoring is often sparse and may lack rich training data describing critical events/attacks. Moreover, CPS often operate in diverse environments that can have significant inter/intra-system variability. Furthermore, CPS monitors that are not robust to data sparsity and inter/intra-system variability may result in inconsistent performance and may not be trusted for monitoring safety and security. Towards overcoming these challenges, this paper presents recent work on the design of parameter-invariant (PAIN) monitors for CPS. PAIN monitors are designed such that unknown events and system variability minimally affect the monitor performance. This work describes how PAIN designs can achieve a constant false alarm rate (CFAR) in the presence of data sparsity and intra/inter system variance in real-world CPS. To demonstrate the design of PAIN monitors for safety monitoring in CPS with different types of dynamics, we consider systems with networked dynamics, linear-time invariant dynamics, and hybrid dynamics that are discussed through case studies for building actuator fault detection, meal detection in type I diabetes, and detecting hypoxia caused by pulmonary shunts in infants. In all applications, the PAIN monitor is shown to have (significantly) less variance in monitoring performance and (often) outperforms other competing approaches in the literature. Finally, an initial application of PAIN monitoring for CPS security is presented along with challenges and research directions for future security monitoring deployments

The R-Process Alliance: Chemical Abundances for a Trio of R-Process-Enhanced Stars -- One Strong, One Moderate, One Mild

We present detailed chemical abundances of three new bright (V ~ 11), extremely metal-poor ([Fe/H] ~ -3.0), r-process-enhanced halo red giants based on high-resolution, high-S/N Magellan/MIKE spectra. We measured abundances for 20-25 neutron-capture elements in each of our stars. J1432-4125 is among the most r-process rich r-II stars, with [Eu/Fe]= +1.44+-0.11. J2005-3057 is an r-I star with [Eu/Fe] = +0.94+-0.07. J0858-0809 has [Eu/Fe] = +0.23+-0.05 and exhibits a carbon abundance corrected for evolutionary status of [C/Fe]_corr = +0.76, thus adding to the small number of known carbon-enhanced r-process stars. All three stars show remarkable agreement with the scaled solar r-process pattern for elements above Ba, consistent with enrichment of the birth gas cloud by a neutron star merger. The abundances for Sr, Y, and Zr, however, deviate from the scaled solar pattern. This indicates that more than one distinct r-process site might be responsible for the observed neutron-capture element abundance pattern. Thorium was detected in J1432-4125 and J2005-3057. Age estimates for J1432-4125 and J2005-3057 were adopted from one of two sets of initial production ratios each by assuming the stars are old. This yielded individual ages of 12+-6 Gyr and 10+-6 Gyr, respectively.Comment: 30 pages, includes a long table, 5 figure

Clinician-in-the-Loop Annotation of ICU Bedside Alarm Data

In this work, we describe the state of clinical monitoring in the intensive care unit and operating room, where patients are at their most fragile and thus monitoring is most heightened. We describe how large amounts of data generated by monitoring patients’ physiologic signals, along with the ubiquitous aspecific threshold alarms in use today, cause dangerous alarm fatigue for medical caregivers. In order to build more specific, more useful alarms, we gathered a novel data set that would allow us to assess the number, types, and utility of alarms currently in use in the intensive care unit. To do this, we developed a system to collect physiologic monitor data, alarms, and annotations of those alarms provided electronically by clinicians. We describe the collection process for this novel data set and provide a preliminary description of the data

Challenges and Research Directions in Medical Cyber-Physical Systems

Medical cyber-physical systems (MCPS) are lifecritical, context-aware, networked systems of medical devices. These systems are increasingly used in hospitals to provide highquality continuous care for patients. The need to design complex MCPS that are both safe and effective has presented numerous challenges, including achieving high assurance in system software, intoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability. In this paper, we discuss these challenges in developing MCPS, some of our work in addressing them, and several open research issue

Uranium Abundances and Ages of RR-process Enhanced Stars with Novel U II Lines

The ages of the oldest stars shed light on the birth, chemical enrichment, and chemical evolution of the Universe. Nucleocosmochronometry provides an avenue to determining the ages of these stars independent from stellar evolution models. The uranium abundance, which can be determined for metal-poor $r$-process enhanced (RPE) stars, has been known to constitute one of the most robust chronometers known. So far, U abundance determination has used a $single$ U II line at $\lambda3859$ \r{A}. Consequently, U abundance has been reliably determined for only five RPE stars. Here, we present the first homogeneous U abundance analysis of four RPE stars using two novel U II lines at $\lambda4050$ \r{A} and $\lambda4090$ \r{A}, in addition to the canonical $\lambda3859$ \r{A} line. We find that the U II lines at $\lambda4050$ \r{A} and $\lambda4090$ \r{A} are reliable and render U abundances in agreement with the $\lambda3859$ U abundance, for all the stars. We, thus, determine revised U abundances for RPE stars, 2MASS J09544277+5246414, RAVE J203843.2-002333, HE 1523-0901, and CS 31082-001, using multiple U II lines. We also provide nucleocosmochronometric ages of these stars based on the newly derived U, Th, and Eu abundances. The results of this study open up a new avenue to reliably and homogeneously determine U abundance for a significantly larger number of RPE stars. This will, in turn, enable robust constraints on the nucleocosmochronometric ages of RPE stars, which can be applied to understand the chemical enrichment and evolution in the early Universe, especially of $r$-process elements.Comment: Resubmitted to Ap

Lorentz angle measurements in irradiated silicon detectors between 77 K and 300 K

Future experiments are using silicon detectors in a high radiation environment and in high magnetic fields. The radiation tolerance of silicon improves by cooling it to temperatures below 180 K. At low temperatures the mobility increases, which leads to larger de of the charge carriers by the Lorentz force. A good knowledge of the Lorentz angle is needed for design and operation of silicon detectors. We present measurements of the Lorentz angle between 77 K and 300 K before and after irradiation with a primary beam of 21 MeV protons