Machine Learning and Epilepsy: Classifying Epileptic Brain States Using Sugihara Causality Networks
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Authors
Kamalaldin, Kamal
Issue Date
2017
Type
Thesis
Language
en_US
Keywords
Alternative Title
Abstract
Epilepsy is a brain disease that profoundly affects the personal lives of its patients as well as the
health care system needed to address it. Machine learning is a field of computer science experiencing
a renaissance period at a time where computers can do more work in less time than ever before. While
industry has moved rapidly towards implementation of machine learning in profitable and futuristic
endeavors like autonomous driving and image recognition and creation, some basic and essential societal
problems remain relatively unexamined through the lens of machine learning research, an example of
which is epilepsy. This work attempts to start to fill this gap and shed light on the ways machine
learning can address complex and intricate problems like epilepsy. Through it, several machine learning
algorithms will be surveyed, and some simple implementations and tutorials be provided through an
external companion. Furthermore, a novel definition of brain communication is introduced in the form
of causality, which will also be discussed more broadly. This definition is then used to construct a
network of brain communication patterns in the context of which the author attempts to apply machine
learning. Finally, discoveries made in this work will highlight a lack of a general understanding and
framework of brain communication patterns and the magnitude and scope of future work available in
the intersection of neuroscience and machine learning.
Description
57 p.
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License
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