Develop a multiple physiological system of ICU patients with symptom analysis and decision making

Lin, CT; Chen, SA; Wang, YK; Lu, SW

Develop a multiple physiological system of ICU patients with symptom analysis and decision making

Lin, CT

Chen, SA Wang, YK

Lu, SW

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Publication Type:: Conference Proceeding
Citation:: Digest of Technical Papers - IEEE International Conference on Consumer Electronics, 2014, pp. 163 - 164
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Chen, SA	en_US
dc.contributor.author	Wang, YK https://orcid.org/0000-0001-8390-2664	en_US
dc.contributor.author	Lu, SW	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Digest of Technical Papers - IEEE International Conference on Consumer Electronics, 2014, pp. 163 - 164	en_US
dc.identifier.isbn	9781479938308	en_US
dc.identifier.issn	0747-668X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121288
dc.description.abstract	© 2014 IEEE. This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients. The current version of the expert system can detect EEG and ECG to identify different types of abnormal cardiac rhythms in real-time and identify patients' acute stress. The proposed system also activates an emergency medical alarm system when problems occur.	en_US
dc.relation.ispartof	Digest of Technical Papers - IEEE International Conference on Consumer Electronics	en_US
dc.relation.isbasedon	10.1109/ICCE-TW.2014.6904038	en_US
dc.title	Develop a multiple physiological system of ICU patients with symptom analysis and decision making	en_US
dc.type	Conference Proceeding
utslib.for	0902 Automotive Engineering	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. This study presents a real-time, and auto-alarm intelligent system of healthcare for ICU patients. The current version of the expert system can detect EEG and ECG to identify different types of abnormal cardiac rhythms in real-time and identify patients' acute stress. The proposed system also activates an emergency medical alarm system when problems occur.

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http://hdl.handle.net/10453/121288