Global ETD Search

31	Measurement and fusion of non-invasive vital signs for routine triage of acute paediatric illness Fleming, Susannah January 2010 (has links) Serious illness in childhood is a rare occurrence, but accounts for 20% of childhood deaths. Early recognition and treatment of serious illness is vital if the child is to recover without long-term disability. It is known that vital signs such as heart rate, respiratory rate, temperature, and oxygen saturation can be used to identify children who are at high risk of serious illness. This thesis presents research into the development of a vital signs monitor, designed for use in the initial assessment of unwell children at their first point of contact with a medical practitioner. Child-friendly monitoring techniques are used to obtain vital signs, which can then be combined using data fusion techniques to assist clinicians in identifying children with serious illness. Existing normal ranges for heart rate and respiratory rate in childhood vary considerably, and do not appear to be based on clinical evidence. This thesis presents a systematic meta-analysis of heart rate and respiratory rate from birth to 18 years of age, providing evidence-based curves which can be used to assess the degree of abnormality in these important vital signs. Respiratory rate is particularly difficult to measure in children, but is known to be predictive of serious illness. Current methods of automated measurement can be distressing, or are time-consuming to apply. This thesis therefore presents a novel method for estimating the respiratory rate from an optical finger sensor, the pulse oximeter, which is routinely used in clinical practice. Information from multiple vital signs can be used to identify children at risk of serious illness. A number of data fusion techniques were tested on data collected from children attending primary and emergency care, and shown to outperform equivalent existing scoring systems when used to identify those with more serious illness. 618.92
32	Development and Testing of a Near-Infrared Spectroscopy Opioid Overdose Detection Device Michael D Maclean (8795939) 12 October 2021 (has links) Opioid overdose is a growing epidemic plaguing the United States. Overdose related death has risen from 16,849 in 1999 to 69,029 in 2018. Almost 7 out of 10 of these deaths were due to opioids with 47% being caused by fentanyl or other synthetic opioids. There is a strong need to reduce the amount of overdose-related deaths. Indirect methods should be a first priority, and include counseling and care. For some individuals, this treatment option is unavailable because the drug user may not have the desire or economic means to pursue it. In this case, a more direct preventative approach is needed. This paper presents a novel method of detecting poor peripheral oxygenation, a biomarker linked to opioid overdose. A wristwatch near-infrared spectroscopy device (NIRS) was developed. SPICE simulations were conducted to confirm proper operation of electrical systems. The device was fabricated on a printed circuit board and mounted to a 3D printed enclosure. Absorbance of green, red and infrared (IR) light were measured. Additionally, peripheral capillary oxygen saturation (SpO2) modulation index and changes in concentration of oxyhemoglobin and deoxyhemoglobin were calculated from raw data. A brachial occlusion test was performed to mimic the effects of opioid overdose on peripheral oxygenation. A statistically significant difference (p < 0.05) was observed between pre-occlusion and during-occlusion groups in two subjects for measurement of peak-to-peak values of green raw data, red raw data, IR raw data, oxyhemoglobin concentration change, and deoxyhemoglobin concentration change. Peak-to-peak was observed as a consistent indicator of poor peripheral oxygenation and could serve as a useful metric in the detection of opioid overdose. Computer Engineering Biomedical Instrumentation Medical Devices Engineering Instrumentation opioid overdose near-infrared spectroscopy drug delivery drug detection pulse oximeter pulse oximetry medical device electrical medical device wearable wristwatch optical detection biosensor naloxone oxyhemoglobin deoxyhemoglobin peripheral capillary oxygen saturation closed loop automatic detection
33	Pulse Oximetry : Signal Processing in real time on Raspberry Pi / Pulsoximetri : Signalbehandling i realtid på Raspberry Pi Thunholm, Malin January 2017 (has links) This thesis introduces the reader into RespiHeart, which is a product under development. RespiHeart is an complement/alternative to the regular Pulse Oximeter and is intended to be used within the health care sector for combined measurements and communication on open inexpensive platforms. This thesis evaluates interaction between RespiHeart and a Raspberry Pi 3 Model B to evaluate if the computer is capable of handling the data from RespiHeart and make signal processing. Python is used throughout the whole project and is a suitable language for interaction and signal processing in real time. / Detta examensarbete introducerar läsaren till RespiHeart, en ny trådlös produkt som är under utveckling. RespiHeart är ett komplement/alternativ till den nuvarande Pulsoximetern och är tänkt att användas inom sjukvården för analys, kommuniakation och kombinerade mätningar på öppna billiga plattformar. Detta project utvärderar interaktionen mellan RespiHeart och en Raspberry Pi 3 Model B för att undersöka om datorn är kapabel till att hantera datan från RespiHeart samt göra signal processing i real tid. Programmeringsspråket Python användes under hela projektet och är ett lämpligt språk att använda för interation och signal processing i real tid. Pulse Oximetry Raspberry Pi Signal processing Filtering RespiHeart Healthcare Python FIR IIR Filter IMSE RToS Matlab Electronic Health Record Real Time Pulsoximetri Raspberry Pi Signalbehandling Filtrering RespiHeart Sjukvård Python FIR IIR Filter IMSE RToS Matlab Elektronisk Patient Journal Realtid Medical Engineering Medicinteknik

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