Thesis (M.S.)--Ohio University, November, 2004. / Includes bibliographical references (p. 115-117)

Thesis (M. Phil.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 90-101).

Thesis (M.S.)--California Polytechnic State University, 2010. / Title from PDF title page; viewed on May 3, 2010. Major professor: Robert S. Crockett, Ph.D. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Engineering, with a Specialization in Biomedical Engineering." "March 2010." Includes bibliographical references (p. 39-41).

Thesis (M.S.)--Ohio University, November, 2004. / Title from PDF t.p. Includes bibliographical references (p. 115-117)

Patients in hospital can be prone to sudden, life-threatening changes in their cardiovascular state. Haemodynamic parameters such as blood pressure, pulse transit time (PTT) and perfusion can be monitored in clinical situations to identify these changes as early as possible. Continuous blood pressure is usually monitored using a catheter placed into a major artery, but this is invasive and involves risk to the patient. In the last decade, the field of non-contact vital sign monitoring has emerged, with growing evidence that the remote photoplethysmogram (rPPG) signal can be used to estimate vital signs using video cameras. If the analysis of the rPPG signal can be expanded to include the estimation of haemodynamic parameters, it could result in methods for the continuous, non-contact monitoring of a subject's haemodynamic state. In a physiology study, a series of video recordings were made of 43 healthy volunteers. The subjects sat in a purpose-built chamber, and the composition of the air was carefully adjusted to cause the subjects to experience large, controlled changes in blood oxygen levels. To validate the video camera algorithms, reference data were also collected. Along with the volunteer study, a clinical study was performed to acquire data in a challenging clinical environment. Data were collected from patients on haemodialysis in the Renal Unit, a population likely to experience sudden changes in haemodynamics. The reference data from the Renal Unit study were analysed to determine the extent to which PTT and mean arterial pressure (MAP) are related. The correlation coefficients and linear fits were found on a global and a per-subject basis. In addition, the video recordings from the Physiology study were processed to derive rPPG signals, and these signals were analysed to obtain estimates for PTT. Local rPPG signals were also derived for different regions of interest, and the waveforms were analysed using a novel application of the technique of signal averaging to produce spatial maps of perfusion and blood flow. The correlation between conventionally measured PTT and MAP was found to be weaker in the haemodialysis population than has been shown elsewhere in the literature, except for a sub-set of patients. The results of the video analysis showed that PTT could be estimated robustly and consistently, although direct validation of these estimates was not possible because of the different method used to calculate the reference PTT. For most subjects, the spatial mapping methods produced robust maps that were consistent over time. These results suggest that it is possible to detect changes in haemodynamics using a video camera, and that this could have applications in healthcare, providing that challenges such as subject movement and clinical validation can be overcome.

The design of a microcomputer-based monitoring and display system for a cardiopulmonary bypass loop is discussed. Analog signals representing blood temperature and oxygen saturation of sample points entering and leaving the blood oxygenator, patient temperature, oxygenator heat exchanger water temperature and hemoglobin content are monitored and displayed. The hemoglobin content and oxygen saturation signals, input from a reflective-type hemoglobin meter and oximeter are corrected for blood temperature and operator input pH. The oxygen saturation is also corrected for hemoglobin content. Oxygen transfer to the patient is calculated and displayed to evaluate the effectiveness of the system in cardiopulmonary support. Alarms are issued for free gas in blood, no blood flow, and high oxygenator water temperature. The hardware and software design is described along with schematics and flowcharts. A complete software listing is included. The monitoring and display system is operational, but has not been tested with flowing blood. / M.S.

Today one of the fast growing social services is the ability for doctors to monitor patients in their residences. The proposed highly scalable database system is designed to support a Remote Patient Monitoring system (RPMS). In an RPMS, a wide range of applications are enabled by collecting health related measurement results from a number of medical devices in the patient’s home, parsing and formatting these results, and transmitting them from the patient’s home to specific data stores. Subsequently, another set of applications will communicate with these data stores to provide clinicians with the ability to observe, examine, and analyze these health related measurements in (near) real-time. Because of the rapid expansion in the number of patients utilizing RPMS, it is becoming a challenge to store, manage, and process the very large number of health related measurements that are being collected. The primary reason for this problem is that most RPMSs are built on top of traditional relational databases, which are inefficient when dealing with this very large amount of data (often called “big data”). This thesis project analyzes scalable data management to support RPMSs, introduces a new set of open-source technologies that efficiently store and manage any amount of data which might be used in conjunction with such a scalable RPMS based upon HBase, implements these technologies, and as a proof of concept, compares the prototype data management system with the performance of a traditional relational database (specifically MySQL). This comparison considers both a single node and a multi node cluster. The comparison evaluates several critical parameters, including performance, scalability, and load balancing (in the case of multiple nodes). The amount of data used for testing input/output (read/write) and data statistics performance is 1, 10, 50, 100, and 250 GB. The thesis presents several ways of dealing with large amounts of data and develops & evaluates a highly scalable database that could be used with a RPMS. Several software suites were used to compare both relational and non-relational systems and these results are used to evaluate the performance of the prototype of the proposed RPMS. The results of benchmarking show that MySQL is better than HBase in terms of read performance, while HBase is better in terms of write performance. Which of these types of databases should be used to implement a RPMS is a function of the expected ratio of reads and writes. Learning this ratio should be the subject of a future thesis project. / En av de snabbast växande sociala tjänsterna idag är möjligheten för läkare att övervaka patienter i sina bostäder. Det beskrivna, mycket skalbara databassystemet är utformat för att stödja ett sådant Remote Patient Monitoring-system (RPMS). I ett RPMS kan flertalet applikationer användas med hälsorelaterade mätresultat från medicintekniska produkter i patientens hem, för att analysera och formatera resultat, samt överföra dem från patientens hem till specifika datalager. Därefter kommer ytterligare en uppsättning program kommunicera med dessa datalager för att ge kliniker möjlighet att observera, undersöka och analysera dessa hälsorelaterade mått i (nära) realtid. På grund av den snabba expansionen av antalet patienter som använder RPMS, är det en utmaning att hantera och bearbeta den stora mängd hälsorelaterade mätningar som samlas in. Den främsta anledningen till detta problem är att de flesta RPMS är inbyggda i traditionella relationsdatabaser, som är ineffektiva när det handlar om väldigt stora mängder data (ofta kallat "big data"). Detta examensarbete analyserar skalbar datahantering för RPMS, och inför en ny uppsättning av teknologier baserade på öppen källkod som effektivt lagrar och hanterar godtyckligt stora datamängder. Dessa tekniker används i en prototypversion (proof of concept) av ett skalbart RPMS baserat på HBase. Implementationen av det designade systemet jämförs mot ett RPMS baserat på en traditionell relationsdatabas (i detta fall MySQL). Denna jämförelse ges för både en ensam nod och flera noder. Jämförelsen utvärderar flera kritiska parametrar, inklusive prestanda, skalbarhet, och lastbalansering (i fallet med flera noder). Datamängderna som används för att testa läsning/skrivning och statistisk prestanda är 1, 10, 50, 100 respektive 250 GB. Avhandlingen presenterar flera sätt att hantera stora mängder data och utvecklar samt utvärderar en mycket skalbar databas, som är lämplig för användning i RPMS. Flera mjukvaror för att jämföra relationella och icke-relationella system används för att utvärdera prototypen av de föreslagna RPMS och dess resultat. Resultaten av dessa jämförelser visar att MySQL presterar bättre än HBase när det gäller läsprestanda, medan HBase har bättre prestanda vid skrivning. Vilken typ av databas som bör väljas vid en RMPS-implementation beror därför på den förväntade kvoten mellan läsningar och skrivningar. Detta förhållande är ett lämpligt ämne för ett framtida examensarbete.

A patient monitor device for use during open heart surgery has been designed and constructed. The device uses a VIC 20 microcomputer along with some additional circuitry to monitor 3 separate functions. The first patient variable monitored is the blood flow rate through the extracorporeal blood circuit during surgery. The device also continuously monitors and displays 6 separate temperatures. Finally, 3 individual timers are monitored and displayed with the device. Both the hardware and the software used in the design are fully described. / Master of Science

Master of Science / Department of Computing and Information Sciences / Gurdip Singh / The Patient Monitoring System is a wireless sensor network application used for dynamically tracking a patient’s physical activity using 3D Accelerometer Sensors in the Sun Small Programmable Object Technology (SPOT) platform. The system is able to detect different postures of a person and recognize high-level actions performed by a patient by monitoring different pattern of postures. This activity can be monitored remotely from a nurse station or a handheld device. The monitoring system can be used for alerting the nurse station in a hospital, if a patient performs some abnormal action. In the proposed system, the Sun SPOTs are affixed on a person's chest, thigh, leg and arm. The application determines the posture of a person by sensing the acceleration and tilt values of the SPOT in the direction of X, Y and Z axis. Based on these values the application can determine the postures of a person such as Lying Down, Sitting, Standing, Walking, Bending, and Arm Moving. We provide user mechanisms to define high level actions such as “attempting to get up from Lying Down position”, in terms of patterns of lower-level posture sequences. The system detects these patterns from the posture sequences reported by the Sun SPOTs, and reports them at desired locations.

The Emergency Department (ED) provides the first line of care for anyone seeking treatment for an urgent problem caused by an accident or illness . Physiological observations in the ED are a required part of patient care, and are used to monitor a patient's condition. Manual observations are recorded regularly by nursing staff, using a Track and Trigger (T&T) system, in which higher scores indicate greater physiological abnormality. An observational study at the John Radcliffe Hospital, Oxford, was conducted to assess the effectiveness of T&T in the ED. Retrospective analysis showed that the effectivenessof T&T was limited by poor completion, and incorrect calculation of T&T scores. In response, we computed a retrospective, fully completed, scoring system which showedvery clear improvements in both sensitivity and specificity. In addition to nurse observations, higher acuity ED patients have their vital signs continuously monitored by bedside monitors. However, the alerts generated by the monitors are routinely ignored due to their high false alert rate. We investigated whether a baseline data fusion model and two alternative techniques, weighted Parzen windows and Support Vector Machines, could identify events relating to vital sign abnormality while keeping the number of false alerts to a minimum. The performance of each model was assessed by calculating its sensitivity and specificity. However, it was not possible to select an optimal model, due to the difficulty in assessing the relative importance of maximising true alertsand minimising false alerts. In the final part of this thesis, two limitations of the data fusion models are highlighted. Firstly, missing data is not handled coherently within the current models, and secondly the models do not make use of temporal information. One method of addressing both of these issues, Gaussian processes, was considered. Using this method, a novel framework was derived that allowed for alerts to be generated even when there is uncertainty in the vital sign values.