Medical engineering & physics
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Sedation administration and agitation management are fundamental activities in any intensive care unit. A lack of objective measures of agitation and sedation, as well as poor understanding of the underlying dynamics, contribute to inefficient outcomes and expensive healthcare. Recent models of agitation-sedation pharmacodynamics have enhanced understanding of the underlying dynamics and enable development of advanced protocols for semi-automated sedation administration. ⋯ High median relative average normalised density (RAND) values of 0.77 and 0.78 support and minimum RAND values of 0.51 and 0.55 for models without and with EAR dynamics respectively show that both models are valid representations of the fundamental agitation-sedation dynamics present in a broad spectrum of intensive care unit (ICU) patients. While the addition of the EAR dynamic increases the ability of the model to capture the observed dynamics of the agitation-sedation system, the improvement is relatively small and the sensitivity of the model to the EAR dynamic is low. Although this may represent a limitation of the model, the inclusion of EAR is shown to be important for accurately capturing periods of low, or no, sedative infusion, such as during weaning prior to extubation.
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Comparative Study
Measuring contact area, force, and pressure for bioengineering applications: using Fuji Film and TekScan systems.
The goal of this study was to compare the TekScan I-Scan Pressure Measurement System with two methods of analysis involving the Fuji Film Prescale Pressure Measuring System in estimating area, force and pressure. Fuji Film and TekScan sensors were alternately placed between a cylindrical peg and a finely ground steel base plate, and compressed with known forces. All Fuji stains were digitally scanned and analyzed. ⋯ The TekScan system utilized special matrix-based sensors interfaced with a Windows compatible desktop computer that was equipped with specialized data acquisition hardware and analysis software. The data from this study did not support the hypothesis that all three methods would have accuracies within +/-5% of a known value, when estimating area, force and pressure. Specifically, the TekScan system was found to be more accurate than either of the Fuji Film methods when estimating area and pressure.
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Comparative Study
The comparison of different feed forward neural network architectures for ECG signal diagnosis.
The electrocardiograms (ECGs) record the electrical activity of the heart and are used to diagnose many heart disorders. This paper proposes a two-stage feed forward neural network for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool that can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. ⋯ The performance of the different modules as well as the efficiency of the whole system is presented. Among different architectures, a proposed multi-stage network named NET_BST possesses the highest recognition rate of around 93%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems.
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Controlled Clinical Trial
Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure.
In this study we have investigated the electroencephalogram (EEG) background activity in patients with Alzheimer's disease (AD) using non-linear analysis methods. We calculated the Lempel-Ziv (LZ) complexity - applying two different sequence conversion methods - and the central tendency measure (CTM) of the EEG in 11 AD patients and 11 age-matched control subjects. CTM quantifies the degree of variability, while LZ complexity reflects the arising rate of new patterns along with the EEG time series. ⋯ Our results show a decreased complexity of EEG patterns in AD patients. In addition, we obtained 90.9% sensitivity and 72.7% specificity at O1, and 72.7% sensitivity and 90.9% specificity at P3 and P4. These findings suggest that LZ complexity may contribute to increase the insight into brain dysfunction in AD in ways which are not possible with more classical and conventional statistical methods.
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In this paper, we propose a fast and automated navigation path generation algorithm to visualize inside of carotid artery using MR angiography images. The carotid artery is one of the body regions not accessible by real optical probe but can be visualized with virtual endoscopy. By applying two-phase adaptive region-growing algorithm, the carotid artery segmentation is started at the initial seed, which is located on the initially thresholded binary image. ⋯ Experiments have been conducted on both mathematical phantom and clinical data sets. This algorithm is more effective than key-framing and topological thinning method in terms of automated features and computing time. This algorithm is also applicable to generate the centerline of renal artery, coronary artery, and airway tree which has tree-like cylinder shape of organ structures in the medical imagery.