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Development and Testing of a Near-Infrared Spectroscopy Opioid Overdose Detection DeviceMichael 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.
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Analýza souvislostí mezi simultánně měřenými EEG a fMRI daty / Analysis of connections between simultaneous EEG and fMRI dataLabounek, René January 2012 (has links)
Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created during master thesis. It preprocesses EEG signals into EEG regressors which are loaded with program SPM8 where joint EEG–fMRI analysis is estimated in general linear model. EEG regressors consist of vectors of temporal changes in absolute or relative power values of EEG signal in the specified frequency bands from selected electrodes due to periods of fMRI acquisition of individual images. The software is tested on the simultaneous EEG-fMRI data of a visual oddball experiment. EEG regressors are calculated for temporal changes in absolute and relative EEG power values in three frequency bands of interest ( 8-12Hz, 12-20Hz a 20-30Hz) from the occipital electrodes (O1, O2 and Oz). Three types of test analyzes is performed. Data from three individuals is examined in the first. Accuracy of results is evaluated due to the possibilities of setting of calculation method of regressor. Group analysis of data from twenty-two healthy patients is performed in the second. Group EEG regressors analysis is realized in the third through the correlation matrix due to the specified type of power and frequency band outside of the general linear model.
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