Estimating the Quality of Electroconvulsive Therapy Induced Seizures Using Decision Tree and Fuzzy Inference System Classifiers

Al-Kaysi, AM; Al-Ani, A; Galvez, V; Colleen Loo, K; Ling, S; Tjeerd Boonstra, W

Estimating the Quality of Electroconvulsive Therapy Induced Seizures Using Decision Tree and Fuzzy Inference System Classifiers

Al-Kaysi, AM Al-Ani, A

Galvez, V Colleen Loo, K Ling, S

Tjeerd Boonstra, W

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 3677 - 3680
Issue Date:: 2018-10-26

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Field	Value	Language
dc.contributor.author	Al-Kaysi, AM	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Galvez, V	en_US
dc.contributor.author	Colleen Loo, K	en_US
dc.contributor.author	Ling, S https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Tjeerd Boonstra, W	en_US
dc.date.available	2020-07-01T19:06:06Z
dc.date.issued	2018-10-26	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 3677 - 3680	en_US
dc.identifier.isbn	9781538636466	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128603
dc.description.abstract	© 2018 IEEE. Electroconvulsive therapy (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. This study aims to identify seizure quality rating by utilizing a set of seizure parameters. We used 750 ECT EEG recordings in this experiment. Four seizure related parameters, (time of slowing, regularity, stereotypy and post-ictal suppression) are used as inputs to two classifiers, decision tree and fuzzy inference system (FIS), to predict seizure quality ratings. The two classifiers produced encouraging results with error rate of 0.31 and 0.25 for FIS and decision tree, respectively. The classification results show that the four seizure parameters provide relevant information about the rating of seizure quality. Automatic scoring of seizure quality may be beneficial to clinicians working in this field.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2018.8513334	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Seizures	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Depressive Disorder, Major	en_US
dc.subject.mesh	Electroconvulsive Therapy	en_US
dc.subject.mesh	Decision Trees	en_US
dc.title	Estimating the Quality of Electroconvulsive Therapy Induced Seizures Using Decision Tree and Fuzzy Inference System Classifiers	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. Electroconvulsive therapy (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. This study aims to identify seizure quality rating by utilizing a set of seizure parameters. We used 750 ECT EEG recordings in this experiment. Four seizure related parameters, (time of slowing, regularity, stereotypy and post-ictal suppression) are used as inputs to two classifiers, decision tree and fuzzy inference system (FIS), to predict seizure quality ratings. The two classifiers produced encouraging results with error rate of 0.31 and 0.25 for FIS and decision tree, respectively. The classification results show that the four seizure parameters provide relevant information about the rating of seizure quality. Automatic scoring of seizure quality may be beneficial to clinicians working in this field.

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http://hdl.handle.net/10453/128603