Domain Adaptation for Pathologic Oscillations

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8170)


This paper presents a platform to bridge datamining techniques and concepts in the field of neurosciences with state-of-the-art data mining, in particular domain adaptation. In non-clinical environs, once an exhaustive search for a particular item of knowledge seems to be impractical, there is the natural tendency to switch to heuristic methods to expedite the search. Conversely, when neuroscientists are in the same situation, they will trust exhaustive searches rather than heuristics such as clinical decision-support systems (CDSS). This is particularly when electroencephalography (EEG) sequences are used to search for pathologic oscillations in the brain. The purpose of this paper is to promising results illustrating how an intelligent agent can data mine explicit types of pathologic oscillations in the human brain.


Status Epilepticus Exhaustive Search Domain Adaptation Fuzzy Subset Covariate Shift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Departments of Pediatrics & NeurologyUniversity of Colorado DenverAuroraUSA
  2. 2.Department of Computer ScienceUniversity of Colorado at Colorado SpringsColorado SpringsUSA

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