J Med Syst
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Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. ⋯ Further, it was observed that the classification accuracy improved from 80.2% with outliers to 92.15% without outliers. The classifier performance metrics ensures the suitability of the proposed multi-features with optimized SVM classifier. It can be concluded that the proposed approach can be applied for recognition of focal EEG signals to localize epileptogenic zones.
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The objective of this study was to characterize workload during all hours of the day in the non-operating room anesthesia (NORA) environment and identify what type of patients and procedures were more likely to occur during after-hours. By investigating data from the National Anesthesia Clinical Outcomes Registry, we characterized the total number of ongoing NORA cases per hour of the day (0 - 23 h). Results were presented as the mean hour and standard error (SE). ⋯ Pairwise differences between means for each NORA specialty were all statistically significant (p < 0.0001). During after-hour shifts (4.3% of cases), patients with higher American Society of Anesthesiologists physical status classification scores had increased odds for undergoing a NORA procedure, while procedures that were more physiologically complex had decreased odds. With the increasing demand for NORA services, it is prudent that we fully understand the challenges of providing safe and efficient anesthetic services particularly in locations where fewer resources are available.
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Children undergoing general anesthesia require airway monitoring by an anesthesia provider. The airway may be supported with noninvasive devices such as face mask or invasive devices such as a laryngeal mask airway or an endotracheal tube. The physiologic data stored provides an opportunity to apply machine learning algorithms distinguish between these modes based on pattern recognition. ⋯ In contrast, the sensitivity, specificity, and accuracy of support vector machine are 89.1%, 92.3%, and 88.3% and with the boosted tree classifier they are 93.8%, 92.1%, and 91.4%. We describe a method to automatically distinguish between noninvasive and invasive airway device support in a pediatric surgical setting based on respiratory monitoring parameters. The results show that the neural network classifier algorithm can accurately classify noninvasive and invasive airway device support.
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While a number of studies have examined efficiency metrics in the operating rooms (ORs), there are few studies addressing non-operating room anesthesia (NORA) metrics. The standards established in the realm of OR studies may not apply to ongoing investigations of NORA efficiency. We hypothesize that there are significant differences in these commonly used metrics. ⋯ Case times for NORA settings tended to be overestimated (-4.07 min versus -2.12 min), but showed less variation (8.61 min vs. 17.92 min). In short, there are significant differences in common efficiency metrics between OR and NORA cases. Future studies should elucidate and validate appropriate efficiency benchmarks for the NORA setting.
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There has been little in the development or application of operating room (OR) management metrics to non-operating room anesthesia (NORA) sites. This is in contrast to the well-developed management framework for the OR management. We hypothesized that by adopting the concept of physician efficiency, we could determine the applicability of this clinical productivity benchmark for physicians providing services for NORA cases at a tertiary care center. ⋯ On days where scheduling efficiency was less than 1, that is, there are more sites than physicians, mean physician efficiency (95% CI, [0.326, 0.402]) was higher than mean site utilization (95% CI, [0.250, 0.296]). We demonstrate that scheduling efficiency vis-à-vis physician efficiency as an OR management metric diverge when anesthesiologists travel between NORA sites. When the opportunity to scale operational efficiencies is limited, increasing scheduling efficiency by incorporating different NORA sites into a "block" allocation on any given day may be the only suitable tactical alternative.