Abstract
In the continuing goal to merge the fields of computational neuroscience with medical based neurodiagnostic clinical research this paper presents advancements on machine learning Big Electroencephalogram (EEG) Data. The authors’ clinical decision-support systems (CDSS) presented in previous work was able to distinguish, within minutes, pathological oscillations hidden in terabytes of complex signal analysis. This paper presents training and learning elements that compliment and advance this previous work. This paper shows how perceptrons, that predate modern-day neural network constructs, remain relevant in many modern classification applications where a clear linear separation is present in the data. Furthermore, the perceptrons also compliment the domain adaptation covariant shifts later used when the system is used in the neuroICU (Intensive Care Unit). Accordingly, we present supervised learning for the neuroICU using single-layer perceptron classifiers.
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Mello, C.A., Lewis, R., Brooks-Kayal, A., Carlsen, J., Grabenstatter, H., White, A.M. (2014). Supervised Learning for the Neurosurgery Intensive Care Unit Using Single-Layer Perceptron Classifiers. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_22
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DOI: https://doi.org/10.1007/978-3-319-09891-3_22
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