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S Bermejo

Publications and source records attributed to S Bermejo.

4 recordsLinked to original sources
PubMed2002

[Cardiorespiratory arrest: a rare complication of subdural block].

We report a case of accidental subdural block after epidural anesthesia that manifested as cardiac arrest due to extensive spinal blockade 20 minutes after administration of 50 mg of 0.5% bupivacaine. The event resolved without sequelae. Subdural placement of the catheter was verified by computed axial tomography contrast medium. Clinical signs of subdural block are highly variable, extensive neural block being among the possible rare presentations, with latency ranging from a few minutes to as long as 30. Recent electron microscope observations with new methods for fixing and preparing tissues suggest that the dubdural space does not exist naturally, but rather forms artificially within a low-resistance cell plane composed of neurothelial cells, as a result of trauma or the injection of a local anesthetic. The characteristics of the space depend, therefore, on factors that come together at the site. These data explain the great variability in the clinical manifestations of a subdural block. The case of cardiopulmonary arrest we report is rare in the literature.

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PubMed2001

Oriented principal component analysis for large margin classifiers.

Large margin classifiers (such as MLPs) are designed to assign training samples with high confidence (or margin) to one of the classes. Recent theoretical results of these systems show why the use of regularisation terms and feature extractor techniques can enhance their generalisation properties. Since the optimal subset of features selected depends on the classification problem, but also on the particular classifier with which they are used, global learning algorithms for large margin classifiers that use feature extractor techniques are desired. A direct approach is to optimise a cost function based on the margin error, which also incorporates regularisation terms for controlling capacity. These terms must penalise a classifier with the largest margin for the problem at hand. Our work shows that the inclusion of a PCA term can be employed for this purpose. Since PCA only achieves an optimal discriminatory projection for some particular distribution of data, the margin of the classifier can then be effectively controlled. We also propose a simple constrained search for the global algorithm in which the feature extractor and the classifier are trained separately. This allows a degree of flexibility for including heuristics that can enhance the search and the performance of the computed solution. Experimental results demonstrate the potential of the proposed method.

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