Quantify Physiologic Interactions Using Network Analysis

Pham, TT; Dutkiewicz, E

Quantify Physiologic Interactions Using Network Analysis

Pham, TT Dutkiewicz, E

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Computational Science and Its Applications – ICCSA 2019, 2019, 11619 LNCS, pp. 142-151
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Pham, TT
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286
dc.date	2019-07-01
dc.date.accessioned	2020-06-10T01:53:51Z
dc.date.available	2020-06-10T01:53:51Z
dc.date.issued	2019-01-01
dc.identifier.citation	Computational Science and Its Applications – ICCSA 2019, 2019, 11619 LNCS, pp. 142-151
dc.identifier.isbn	9783030242886
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/141222
dc.description.abstract	© 2019, Springer Nature Switzerland AG. To better understand the neural interactions amongst human organ systems, this work provides a framework of data analysis to quantify forms of neural signalling. We explore network interactions among the human brain and motor controlling. The main objective of this work is to provoke unique challenges in the emerging Network Physiology field. The proposed method applies network analysis techniques including power coherence for connectivity discovering and correlation measurement for profiling relationships. We used a well-designed dataset of 50 subjects over 14 different scenarios for each individual. We found network models for these interactions and observed informative network behaviours. The information can be used to study impaired communications that can lead to dysfunction of organs or the entire system such as sepsis.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Computational Science and Its Applications – ICCSA 2019
dc.relation.ispartof	International Conference on Computational Science and Its Applications
dc.relation.isbasedon	10.1007/978-3-030-24289-3_12
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Quantify Physiologic Interactions Using Network Analysis
dc.type	Conference Proceeding
utslib.citation.volume	11619 LNCS
utslib.location.activity	Saint Petersburg, Russia
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-10T01:53:47Z
pubs.finish-date	2019-07-04
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2019-07-01
pubs.volume	11619 LNCS
dc.location	Switzerland

Abstract:

© 2019, Springer Nature Switzerland AG. To better understand the neural interactions amongst human organ systems, this work provides a framework of data analysis to quantify forms of neural signalling. We explore network interactions among the human brain and motor controlling. The main objective of this work is to provoke unique challenges in the emerging Network Physiology field. The proposed method applies network analysis techniques including power coherence for connectivity discovering and correlation measurement for profiling relationships. We used a well-designed dataset of 50 subjects over 14 different scenarios for each individual. We found network models for these interactions and observed informative network behaviours. The information can be used to study impaired communications that can lead to dysfunction of organs or the entire system such as sepsis.

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

http://hdl.handle.net/10453/141222