Development of predictive algorithms for electrical stimulation based major depressive disorder therapy

Al-Kaysi, Alaa Mohammed Dawood

Development of predictive algorithms for electrical stimulation based major depressive disorder therapy

Al-Kaysi, Alaa Mohammed Dawood

Permalink

Publication Type:: Thesis
Issue Date:: 2018

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (233.66 kB)

Adobe PDF

Download thesisAdobe PDF (6.93 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.advisor	Major depressive disorder.	en_AU
dc.contributor.author	Al-Kaysi, Alaa Mohammed Dawood
dc.date.accessioned	2018-10-03T04:34:14Z
dc.date.available	2018-10-03T04:34:14Z
dc.date.issued	2018
dc.identifier.uri	http://hdl.handle.net/10453/127928
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_AU
dc.description.abstract	Major depressive disorder (MDD) is a brain disorder that is characterised by negative thoughts, mood and behaviour. Transcranial direct current stimulation (tDCS) has emerged recently as a promising brain-stimulation treatment for MDD. A standard tDCS treatment involves numerous sessions that are run over a few weeks, however, not all participants respond to this type of treatment. This delay could have negative impact upon patients that do not respond, being an inefficient use of staff time and exposing patients to ineffective treatment. The early identification of patients who respond to this type of treatment is needed. Electroencephalography (EEG) signal is a significant tool that can be used to study the modulatory effects of tDCS treatment. The significant part of this research aims to predict the clinical outcomes of tDCS treatment by analysing the patients’ EEG signals. EEG signal is a complex signal that has high sensitivity to noise. EEG signal records both neural and non-neural activities from a large number of electrodes, therefore, the analysis and classification of EEG signals proved to be quite challenging. Machine learning has attracted the attention of many researches as a powerful approach for analysing various types of signals, including EEG. Algorithms for channel/feature selection, classification, detection, prediction and fusion have been developed. Recently, deep neural networks, particularly deep belief networks (DBNs), have emerged as a new hierarchical technique for modelling high level abstractions of data, and have been successfully applied to a number of classification problems. Similar to most classification algorithms, the existing DBNs have been mainly designed to handle single stream data, and there are hardly any attempts to generalize those to suit multi-channel signals. Accordingly, the first part of this research investigates the utilisation of DBN to differentiate between tDCS sessions based on classification EEG signals, particularly the implementation of multi-channel DBNs. One of the important attributes that needs to be carefully studied is considering the multi-channel nature of EEG in the design and training of deep networks. A second part of this research aims to predict which patients improve in mood and cognitive in response to tDCS treatment based on EEG data that were collected at the start of tDCS treatment. Classifying power spectral density (PSD) of resting-state EEG is achieved using support vector machine (SVM), linear discriminate analysis (LDA) and extreme learning machine (ELM). Participants were labelled as improved/not improved based on the change in mood and cognitive scores. The obtained classification results of all channel pair combinations are used to identify the most relevant brain regions. The frontal area is found to be particularly informative for the prediction of the clinical outcome of the tDCS treatment. Subject independent results reveal that our proposed method enables the correct identification of the treatment outcome for seven of the ten participants for mood improvement and nine of ten participants for cognitive improvement. This represents an encouraging sign that EEG-based classification may help to tailor the selection of patients for treatment with tDCS brain stimulation. The second line treatment of depressive disorder is electroconvulsive therapy (ECT). ECT is an effective and widely used treatment for major depressive disorder, in which a brief electric current is passed through the brain to trigger a brief seizure. The second main aim in this research is to identify seizure quality rating by utilising a set of seizure parameters. Four seizure related parameters, (time to onset of slowing, regularity, stereotypy and post-ictal suppression) are used as inputs to decision tree and fuzzy rule-based classifiers to predict seizure quality ratings. The classification results show that the four seizure parameters provide relevant information about the rating of seizure quality. Automatic scoring of seizure quality could be beneficial to clinicians working in electroconvulsive therapy.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/127928/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.subject	Predictive algorithms.	en_AU
dc.subject	MDD.	en_AU
dc.subject	Electroencephalography.	en_AU
dc.subject	EEG signals.	en_AU
dc.subject	Deep belief networks.	en_AU
dc.subject	DBNs.	en_AU
dc.title	Development of predictive algorithms for electrical stimulation based major depressive disorder therapy	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

Major depressive disorder (MDD) is a brain disorder that is characterised by negative thoughts, mood and behaviour. Transcranial direct current stimulation (tDCS) has emerged recently as a promising brain-stimulation treatment for MDD. A standard tDCS treatment involves numerous sessions that are run over a few weeks, however, not all participants respond to this type of treatment. This delay could have negative impact upon patients that do not respond, being an inefficient use of staff time and exposing patients to ineffective treatment. The early identification of patients who respond to this type of treatment is needed. Electroencephalography (EEG) signal is a significant tool that can be used to study the modulatory effects of tDCS treatment. The significant part of this research aims to predict the clinical outcomes of tDCS treatment by analysing the patients’ EEG signals. EEG signal is a complex signal that has high sensitivity to noise. EEG signal records both neural and non-neural activities from a large number of electrodes, therefore, the analysis and classification of EEG signals proved to be quite challenging. Machine learning has attracted the attention of many researches as a powerful approach for analysing various types of signals, including EEG. Algorithms for channel/feature selection, classification, detection, prediction and fusion have been developed. Recently, deep neural networks, particularly deep belief networks (DBNs), have emerged as a new hierarchical technique for modelling high level abstractions of data, and have been successfully applied to a number of classification problems. Similar to most classification algorithms, the existing DBNs have been mainly designed to handle single stream data, and there are hardly any attempts to generalize those to suit multi-channel signals. Accordingly, the first part of this research investigates the utilisation of DBN to differentiate between tDCS sessions based on classification EEG signals, particularly the implementation of multi-channel DBNs. One of the important attributes that needs to be carefully studied is considering the multi-channel nature of EEG in the design and training of deep networks. A second part of this research aims to predict which patients improve in mood and cognitive in response to tDCS treatment based on EEG data that were collected at the start of tDCS treatment. Classifying power spectral density (PSD) of resting-state EEG is achieved using support vector machine (SVM), linear discriminate analysis (LDA) and extreme learning machine (ELM). Participants were labelled as improved/not improved based on the change in mood and cognitive scores. The obtained classification results of all channel pair combinations are used to identify the most relevant brain regions. The frontal area is found to be particularly informative for the prediction of the clinical outcome of the tDCS treatment. Subject independent results reveal that our proposed method enables the correct identification of the treatment outcome for seven of the ten participants for mood improvement and nine of ten participants for cognitive improvement. This represents an encouraging sign that EEG-based classification may help to tailor the selection of patients for treatment with tDCS brain stimulation. The second line treatment of depressive disorder is electroconvulsive therapy (ECT). ECT is an effective and widely used treatment for major depressive disorder, in which a brief electric current is passed through the brain to trigger a brief seizure. The second main aim in this research is to identify seizure quality rating by utilising a set of seizure parameters. Four seizure related parameters, (time to onset of slowing, regularity, stereotypy and post-ictal suppression) are used as inputs to decision tree and fuzzy rule-based classifiers to predict seizure quality ratings. The classification results show that the four seizure parameters provide relevant information about the rating of seizure quality. Automatic scoring of seizure quality could be beneficial to clinicians working in electroconvulsive therapy.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/127928