Author: Tyler Grummett
Grummett, Tyler, 2018 Analytical Complexity Required for Diagnosing Bipolar Disorder, Schizophrenia, and Dementia, Flinders University, College of Science and Engineering
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Diagnostic tools, currently used in the clinical setting to aid in the diagnosis of various
neurological diseases, are often limited by subjective interpretation and the absence
of continuing neurological symptoms (e.g. seizures) required to make a diagnosis.
Consequently, these limitations may reduce the sensitivity of the investigation, the
specificity, or both. Electroencephalography researchers have attempted to reduce
this subjectivity and the reliance on neurological symptoms by utilising computer-assisted
classification algorithms that use scalp electroencephalogram (EEG) data
to make a diagnostic decision that is not reliant on neurological symptoms being
present. Measures of EEG characteristics are then obtained and compared. Most
commonly, EEG power in specific frequency bands is calculated (spectral analysis)
and diagnosis attempted. In recent years, adjacency matrices based on some form of
connectivity between EEG sensors or sources are constructed which comprise a number
of nodes (regions of the brain) and edges (connection strengths between these
nodes). Adjacency matrices may also be used to calculate graph theory measures,
such as the clustering coefficient and the shortest path length, which de ne how
all the nodes interact with each other and are able to determine whether there are
clusters of brain regions that are highly interconnected. Several recent studies have
utilised the adjacency matrices and graph theory measures in classifying neurological
diseases such as epilepsy and Alzheimer's disease and have shown them to be successful
(Magnin et al., 2009; Zhang et al., 2012; Challis et al., 2015; Khazaee et al.,
2015; Wang et al., 2015b; Hassan et al., 2015). However, there are few studies that
test whether these measures are superior to less complex analytical techniques such as spectral analysis. This distinction is important as diagnosing a disease may not
require complex analytical techniques even though such techniques might be essential
in understanding the disease. Moreover, it is beneficial to the clinical setting to
determine whether there are any cheaper alternatives to the current EEG systems,
or systems that are mobile, as a number of patients may be house-bound. In the
present study, I will be testing whether the simplest analytical methods for EEG are
on-par with some of the more complex analytical methods that have been utilised
in previous studies, and to determine whether there is a simple method that can
be implemented in the clinical setting. I will then determine if the signal quality
from cheaper/mobile EEG systems are on-par with the current top-of-the-line EEG
systems to increase diagnostic accessibility and to reduce costs in the clinical setting.
High classification accuracy was consistently found with bipolar disorder, schizophrenia,
and dementia, but not with the other diseases studied. It was also found
that, in most cases, the simpler analytical techniques yielded a higher proportion
of informative channels. This finding strengthens the possibility of utilising simpler
methods in diagnostic studies with a large number of participants, that is achieving
the goal of eventually building a diagnostic tool to be used widely in the clinical
setting. It was also found that cheaper/mobile EEG systems recorded data that was
on-par with highly sophisticated EEG systems, in terms of data quality. Together,
these findings support the prospect of having disease classification programs using
relatively simple diagnostic features utilised in mobile EEG systems that can be
brought to a location more convenient for patients.
Keywords: EEG, electroencephalography, SVM, support vector machine, connectivity, coherence, transfer entropy, graph theory, classify, classification, diagnose, bipolar disorder, schizophrenia, dementia, Alzheimer's disease
Subject: Engineering thesis
Thesis type: Doctor of Philosophy
Completed: 2018
School: College of Science and Engineering
Supervisor: Kenneth Pope