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19 November 2013 Investigation into the efficacy of generating synthetic pathological oscillations for domain adaptation
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Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 89220E (2013) https://doi.org/10.1117/12.2035561
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico
Abstract
In the ongoing investigation of integrating Knowledge Discovery in Databases (KDD) into neuroscience, we present a paper that facilitates overcoming the two challenges preventing this integration. Pathological oscillations found in the human brain are difficult to evaluate because 1) there is often no time to learn and train off of the same distribution in the fatally sick, and 2) sinusoidal signals found in the human brain are complex and transient in nature requiring large data sets to work with which are costly and often very expensive or impossible to acquire. Overcoming these challenges in today's neuro-intensive-care unit (ICU) requires insurmountable resources. For these reasons, optimizing KDD for pathological oscillations so machine learning systems can predict neuropathological states would be of immense value. Domain adaptation, which allows a way of predicting on a separate set of data than the training data, can theoretically overcome the first challenge. However, the challenge of acquiring large data sets that show whether domain adaptation is a good candidate to test in a live neuro ICU remains a challenge. To solve this conundrum, we present a methodology for generating synthesized neuropathological oscillations for domain adaptation.
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Rory Lewis, James Ellenberger, Colton Williams, and Andrew M. White "Investigation into the efficacy of generating synthetic pathological oscillations for domain adaptation", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220E (19 November 2013); doi: 10.1117/12.2035561; https://doi.org/10.1117/12.2035561
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