We developed a method to distinguish bursts and suppressions for EEG burst suppression from the treatments of status epilepticus, employing the joint time-frequency domain. We obtained the feature used in the proposed method from the joint use of the time and frequency domains, and we estimated the decision as to whether the measured EEG was a burst segment or suppression segment by the maximum likelihood estimation. We evaluated the performance of the proposed method in terms of its accordance with the visual scores and estimation of the burst suppression ratio. The accuracy was higher than the sole use of the time or frequency domains, as well as conventional methods conducted in the time domain. In addition, probabilistic modeling provided a more simplified optimization than conventional methods. Burst suppression quantification necessitated precise burst suppression segmentation with an easy optimization; therefore, the excellent discrimination and the easy optimization of burst suppression by the proposed method appear to be beneficial.
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Background Although pneumonia is a leading cause of death in New York City (NYC), limited data exist about the settings in which pneumonia ...
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Summary We tested whether prophylactic droperidol and ondansetron, in combination with a moderate dose of dexamethasone, were equally effe...
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by Demin Li, Carol Bentley, Jenna Yates, Maryam Salimi, Jenny Greig, Sarah Wiblin, Tasneem Hassanali, Alison H. Banham Therapeutic monoclon...
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ACS Nano DOI: 10.1021/acsnano.6b08567 from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/2oNpdhD via...
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Abstract Dermoscopy has demonstrated clinical benefits in improving early melanoma diagnosis and reducing unnecessary biopsies. Despite th...
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