This paper evaluates the effectiveness of a wearable device, developed by the author, to detect different types of epileptic seizures and monitor epileptic patients. The device uses GSR, Pulse, EMG, body temperature and 3-axis accelerometer sensors to detect epilepsy. The device first learns the signal patterns of the epileptic patient in ideal condition. The signal pattern generated during the epileptic seizure, which are distinct from other signal patterns, are detected and analyzed by the algorithms developed by the author. Based on an analysis, the device successfully detected different types of epileptic seizures. The author conducted an experiment on himself to determine the effectiveness of the device and the algorithms. Based on the simulation results, the algorithms are 100 percent accurate in detecting different types of epileptic seizures. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Bibliography: leaves 410-424. / by Paul Scott Schluter. / Ph.D.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by David Howard Turkel. / B.S.

The increasing adoption of electronic health records (EHRs) has led to an unprecedented amount of patient health information stored in an electronic format. The ability to comb through this information is imperative, both for patient care and computational modeling. Creating a system to minimize unnecessary EHR data, automatically distill longitudinal patient information, and highlight salient parts of a patient’s record is currently an unmet need. However, summarization of EHR data is not a trivial task, as there exist many challenges with reasoning over this data. EHR data elements are most often obtained at irregular intervals as patients are more likely to receive medical care when they are ill, than when they are healthy. The presence of narrative documentation adds another layer of complexity as the notes are riddled with over-sampled text, often caused by the frequent copy-and-pasting during the documentation process. This dissertation synthesizes a set of challenges for automated EHR summarization identified in the literature and presents an array of methods for dealing with some of these challenges. We used hybrid data-driven and knowledge-based approaches to examine abundant redundancy in clinical narrative text, a data-driven approach to identify and mitigate biases in laboratory testing patterns with implications for using clinical data for research, and a probabilistic modeling approach to automatically summarize patient records and learn computational models of disease with heterogeneous data types. The dissertation also demonstrates two applications of the developed methods to important clinical questions: the questions of laboratory test overutilization and cohort selection from EHR data.

Thesis. 1976. B.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Microfiche copy available in Archives and Engineering. / Includes bibliographical references. / by Lindsay Asbury Weaver, Jr. / B.S.

The goal of this dissertation is to demonstrate the utility of portable, small-scale diffuse optical spectroscopic (DOS) systems for the diagnosis and treatment monitoring of various diseases. These systems employ near-infrared light (wavelength range of 650nm to 950nm) to probe human tissue and are sensitive to changes in scattering and absorption properties of tissues. The absorption is mainly influenced by the components of blood, namely oxy- and deoxy-hemoglobin (HbO2 and Hb) and parameters that can be derived from them (e.g. total hemoglobin concentration [THb] and oxygen saturation, StO2). Therefore, I focused on diseases in which these parameters change, which includes vascular diseases such as Peripheral Atrial Disease (PAD) and Infantile Hemangiomas (IH) as well as musculoskeletal autoimmune diseases such as Rheumatoid Arthritis (RA). In each of these specific diseases, current monitoring techniques are limited by their sensitivity to disease progression or simply do not exist as a quantitative metric. As part of this project, I first designed and built a wireless handheld DOS device (WHDD) that can perform DOS measurements at various tissue depths. This device was used in a 15-patient pilot study for infantile hemangiomas (IH) to differentiate diseased skin from normal skin and monitor the vascular changes during intervention. In another study, I compare the ultra-small form- factor WHDD’s ability to monitor synovitis and disease progression during a patient’s treatment of RA against the capabilities of a proven frequency domain optical tomographic (FDOT) system that has shown to differentiate patients with and without RA. Learning from clinical utility of the WHDD from these two studies, I adapted the WHDD technology to develop a compact multi- channel DOS measurement system to monitor perfusion changes in the lower extremities before and after surgical intervention for patients with peripheral artery disease (PAD). Using this multi- channel system, which we called the vascular optical spectroscopic measurement (VOSM) system, our group conducted a 20-subject pilot study to quantify its ability to monitor blood perfusion before and after revascularization of stenotic arteries in the lower extremities. This proof-of- concept study demonstrated how DOS may help vascular surgeons perform revascularization procedures in the operating room and assists in post-operative treatment monitoring of vascular diseases.

Chen, Xijun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 72-77). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Design Challenges --- p.2 / Chapter 1.2 --- Wireless Sensor Network Applications --- p.6 / Chapter 1.2.1 --- Military Applications --- p.7 / Chapter 1.2.2 --- Environmental Applications --- p.9 / Chapter 1.2.3 --- Health Applications --- p.11 / Chapter 1.3 --- Wireless Biomedical Sensor Networks (WBSN) --- p.12 / Chapter 1.4 --- Text Organization --- p.13 / Chapter Chapter 2 --- Design a Wearable Platform for Wireless Biomedical Sensor Networks --- p.15 / Chapter 2.1 --- Objective --- p.17 / Chapter 2.2 --- Requirements for Wireless Medical Sensors --- p.19 / Chapter 2.3 --- Hardware design --- p.21 / Chapter 2.3.1 --- Materials and Methods --- p.21 / Chapter 2.3.2 --- Results --- p.24 / Chapter 2.3.3 --- Conclusion --- p.27 / Chapter 2.4 --- Software design --- p.28 / Chapter 2.4.1 --- TinyOS --- p.28 / Chapter 2.4.2 --- Software Organization --- p.28 / Chapter Chapter 3 --- Wireless Medical Sensors --- p.32 / Chapter 3.1 --- Sensing Physiological Information --- p.32 / Chapter 3.1.1 --- Pulse Oximetry --- p.32 / Chapter 3.1.2 --- Electrocardiograph --- p.36 / Chapter 3.1.3 --- Galvanic Skin Response --- p.41 / Chapter 3.2 --- Location Tracking --- p.43 / Chapter 3.2.1 --- Outdoor Location Tracking --- p.43 / Chapter 3.2.2 --- Indoor Location Tracking --- p.44 / Chapter 3.3 --- Motion Tracking --- p.49 / Chapter 3.3.1 --- Technology --- p.50 / Chapter 3.3.2 --- Motion Analysis Sensor Board --- p.51 / Chapter 3.4 --- Discussions --- p.52 / Chapter Chapter 4 --- Applications in Medical Care --- p.54 / Chapter 4.1 --- Introduction --- p.54 / Chapter 4.2 --- Wearable Wireless Body Area Network --- p.56 / Chapter 4.2.1 --- Architecture --- p.58 / Chapter 4.2.2 --- Deployment Scenarios --- p.62 / Chapter 4.3 --- Application in Ambulatory Setting --- p.63 / Chapter 4.3.1 --- Method --- p.64 / Chapter 4.3.2 --- The Software Architecture --- p.66 / Chapter Chapter 5 --- Conclusions and Future Work --- p.69 / References --- p.72 / Appendix --- p.78

Medical literature identifies a number of technology-driven improvements in disease management such as implantable medical devices (IMDs) that are a standard treatment for candidates with specific diseases. Among patients using implantable cardiac defibrillators (ICD), for example, problems and issues are being discovered faster compared to patients without monitoring, improving safety. What is not known is why patients report not feeling safer, creating a safety paradox, and why patients identify privacy concerns in ICD monitoring. There is a major gap in the literature regarding the factors that contribute to perceived safety and privacy in remote patient monitoring (RPM). To address this gap, the research goal of this study was to provide an interpretive account of the experience of RPM patients. This study investigated two research questions: 1) How did RPM recipients perceive safety concerns?, and 2) How did RPM recipients perceive privacy concerns? To address the research questions, in-depth, semi-structured interviews were conducted with six participants to explore individual perceptions in rich detail using interpretative phenomenological analysis (IPA). Four themes were identified and described based on the analysis of the interviews that include — comfort with perceived risk, control over information, education, and security — emerged from the iterative review and data analysis. Participants expressed comfort with perceived risk, however being scared and anxious were recurrent subordinate themes. The majority of participants expressed negative feelings as a result of an initial traumatic event related to their devices and lived in fear of being shocked in inopportune moments. Most of these concerns stem from lack of information and inadequate education. Uncertainties concerning treatment tends to be common, due to lack of feedback from ICD RPM status. Those who knew others with ICD RPM became worrisome after hearing about incidences of sudden cardiac death (SCD) when the device either failed or did not work adequately to save their friend’s life. Participants also expressed cybersecurity concerns that their ICD might be hacked, maladjusted, manipulated with magnets, or turned off. They believed ICD RPM security was in place but inadequate as well as reported feeling a lack of control over information. Participants expressed wanting the right to be left alone and in most cases wanted to limit others’ access to their information, which in turn, created conflict within families and loved ones. Geolocation was a contentious node in this study, with most of participants reporting they did not want to be tracked under any circumstances. This research was needed because few researchers have explored how people live and interact with these newer and more advanced devices. These findings have implications for practice relating to RPM safety and privacy such as identifying a gap between device companies, practitioners, and participants and provided directions for future research to discover better ways to live with ICD RPM and ICD shock.

Laboratory tests are a primary resource for diagnosing patient diseases. However, physicians often make decisions based on a single laboratory result and have a limited perspective of the role of commonly-measured parameters in enhancing the diagnostic process. By providing a dynamic patient profile, the diagnosis could be more accurate and timely, allowing physicians to anticipate changes in the recovery trajectory and intervene more effectively. The assessment and monitoring of the circulatory system is essential for patients in intensive care units (ICU). One component of this system is the platelet count, which is used in assessing blood clotting. However, platelet counts represent a dynamic equilibrium of many simultaneous processes, including altered capillary permeability, inflammatory cascades (sepsis), and the coagulation process. To characterize the value of dynamic changes in platelet count, analytical methods are applied to datasets of critically-ill patients in (1) a homogeneous population of ICU cardiac surgery patients and (2) a heterogeneous group of ICU patients with different conditions and several hospital admissions. The objective of this study was to develop a methodology to anticipate adverse events using metrics that capture dynamic changes of platelet counts in a homogeneous population, then redefine the methodology for a more heterogeneous and complex dataset. The methodology was extended to analyze other important physiological parameters of the circulatory system (i.e., calcium, albumin, anion gap, and total carbon dioxide). Finally, the methodology was applied to simultaneously analyze some parameters enhancing the predictive power of various models. This methodology assesses dynamic changes of clinical parameters for a heterogeneous population of ICU patients, defining rates of change determined by multiple point regression and by the simpler fixed time parameter value ratios at specific time intervals. Both metrics provide prognostic information, differentiating survivors from non-survivors and have demonstrated being more predictive than complex metrics and risk assessment scores with greater dimensionality. The goal was to determine a minimal set of biomarkers that would better assist care providers in assessing the risk of complications, allowing them alterations in the management of patients. These metrics should be simple and their implementation would be feasible in any environment and under uncertain conditions of the specific diagnosis and the onset of an acute event that causes a patient's admission to the ICU. The results provide evidence of the different behaviors of physiologic parameters during the recovery processes for survivors and non-survivors. These differences were observed during the first 8 to 10 days after a patient's admission to the ICU. The application of the presented methodology could enhance physicians' ability to diagnose more accurately, anticipate changes in recovery trajectories, and prescribe effective treatment, leading to more personalized care and reduced mortality rates.

This thesis addresses the hypothesis that a single, waist-mounted triaxial accelerometer (TA) can be used to monitor human movement patterns in unsupervised, freeliving subjects over extended periods, and that it can be used to quantitatively measure parameters that can provide clinical insight into the health status of the subject. A rigorous theoretical and experimental understanding of the signals obtained from a TA is developed. The effect of the placement of the TA device on the waist is explored and a model relating device position to TA signal is developed for a range of postures and activities. A classification framework for movement identification using the signals from a waist-mounted TA is presented. This framework is based on a hierarchical binary processing tree and is designed for real time use. An implementation of this framework for monitoring housebound patients is presented. Algorithms for detecting falls, distinguishing between activity and rest, classifiying transitions between different postural orientations, and for identifying periods of standing, sitting, lying and walking are developed. In evaluation studies performed in controlled laboratory conditions, every algorithm performed with better than 90% accuracy. Once movements are identified, movement-specific parameters sensitive to changes in functional status are extracted from the signal. A two stage methodology for employing the accelerometry system in monitoring free-living subjects is introduced. The first stage involved monitoring specific movements through a directed routine. The second stage involved monitoring of free movement. Signals obtained from the directed routine are used to extract clinically relevant, movement-specific parameters. Signals obtained from the period of free movement are monitored for falls and other abnormal events. General parameters of movement, including energy expenditure, are also measured. The system was evaluated in a series of field studies in laboratory and home environments, in supervised and unsupervised settings, using cohorts of healthy subjects. A pilot trial was conducted in which six healthy elderly subjects wore the TA device for a period of up to three months. The technical performance and useability of the system were evaluated. Clinically significant parameters were measured and the effects of age and health status on the measured parameters were evaluated.