J Med Syst
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This paper presents the framework for forecasting the surgery time by taking into account the surgical environment in an ophthalmology department (experience of surgeon in years, experience of anesthetist in years, staff experience in years, type of anesthesia etc.). The estimation of surgery times is done using three techniques, such as the Adaptive Neuro Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN) and Multiple Linear Regression Analysis (MLRA) and the results of estimation accuracy were compared. ⋯ It is hypothesized that by accurately knowing the surgery times, one can schedule the operations optimally resulting in the efficient utilization of the operating rooms. This increase in the efficiency is demonstrated through computer simulations of the operating theater.
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Comparative Study
The advantages of wearable green reflected photoplethysmography.
This report evaluates the efficacy of reflected-type green light photoplethysmography (green light PPG). Transmitted infrared light was used for PPG and the arterial pulse was monitored transcutaneously. The reflected PPG signal contains AC components based on the heartbeat-related signal from the arterial blood flow and DC components, which include reflectance and scattering from tissue. ⋯ Also, the DC components for green light PPG were similar during heat stress, but showed less signal output for infrared light PPG during hot exposure. The main reason for the reduced DC components was speculated to be the increased blood flow at the vascular bed. Therefore, reflected green light PPG can be useful for pulse rate monitoring because it is less influenced by the tissue and vein region.
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Blast-induced neurotrauma (BINT) is a common injury associated with the present military conflicts. Exposure to the shock-wave produced from exploding ordnances leads to significant neurological deficits throughout the brain and spinal cord. Prevention and treatment of this injury requires an appropriate understanding of the mechanisms governing the neurological response. ⋯ Our findings suggest an inverse relationship between the magnitude of the shock-wave overpressure and the degree of functional deficits using a double sucrose gap recording chamber. Similar correlations are drawn between overpressure and degree of anatomical damage of neuronal processes using a dye-exclusion assay. The following approach is expected to significantly contribute to the detection, mitigation and eventual treatment of BINT.
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Comparative Study
Detecting sleep apnea by heart rate variability analysis: assessing the validity of databases and algorithms.
Obstructive sleep apnea (OSA) is a serious disorder caused by intermittent airway obstruction which may have dangerous impact on daily living activities. Heart rate variability (HRV) analysis could be used for diagnosing OSA, since this disease affects HRV during sleep. In order to validate different algorithms developed for detecting OSA employing HRV analysis, several public or proprietary data collections have been employed for different research groups. ⋯ In this paper, different algorithms employing HRV analysis were applied over diverse public and proprietary databases for detecting OSA, and the outcomes were validated in terms of a statistical analysis. Results indicate that the use of a specific database may strongly affect the performance of the algorithms, due to differences in methodologies of processing. Our results suggest that researchers must strongly take into consideration the database used when quoting their results, since selected cases are highly database dependent and would bias conclusions.
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Arterial blood gas (ABG) has an important role in the clinical assessment of patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Because of ABG complications, an alternative method is beneficial. We have trained and tested five artificial neural networks (ANNs) with venous blood gas (VBG) values (pH, PCO(2), HCO(3), PO(2), and O(2) saturation) as inputs, to predict ABG values in patients with AECOPD. ⋯ Subsequently, data from the remainder 26 patients was used for testing the networks. The ability of ANNs to predict ABG values and to detect significant hypercarbia was assessed and the results were compared with a linear regression model. Our results indicate that the ANNs provide an accurate method for predicting ABG values from VBG values and detecting hypercarbia in AECOPD.