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PublicationRobust Monitoring of Hypovolemia in Intensive Care Patients Using Photoplethysmogram Signals(2015-08-01) Roederer, Alexander; Weimer, James; Dimartino, Joseph; Gutsche, Jacob; Lee, Insup; Roederer, Alexander; Weimer, James; Dimartino, Joseph; Gutsche, Jacob; Lee, InsupThe 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. PublicationTowards Non-Invasive Monitoring of Hypovolemia in Intensive Care Patients(2015-04-13) Roederer, Alexander; Weimer, James; Dimartino, Joseph; Gutsche, Jacob; Lee, Insup; Roederer, Alexander; Weimer, James; Dimartino, Joseph; Gutsche, Jacob; Lee, InsupHypovolemia 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. PublicationClinician-in-the-Loop Annotation of ICU Bedside Alarm Data(2016-06-01) Roederer, Alexander; Dimartino, Joseph; Gutsche, Jacob; Roederer, Alexander; Hanson III, C. William; Dimartino, Joseph; Lee, Insup; Gutsche, Jacob; Mullen-Fortino, Margaret; Shah, Sachin; Hanson III, C. William; Lee, InsupIn 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.