The use of EIT in the detection of regional lung dysfunction in prematurely born neonates

A. Zifan, P. Liatsis, R. Bayford

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This research describes the progress of work in developing a system for automated detection of regional lung dysfunction in prematurely born neonates. EIT boundary measurements, observed at each lung region, are treated as a time series. The SPIRIT algorithm is used to extract local (regional) and global patterns from the datasets of healthy and ill neonates. The SAX technique is used to derive a symbolic representation of the global pattern signal. Current results are promising and demonstrate the possibility of characterise EIT boundary signals by 'words'. Such a representation can then be used to train a discrete Hidden Markov Model (HMM) to automatically detect and characterise regional lung function.

Original languageBritish English
Title of host publicationWorld Congress on Medical Physics and Biomedical Engineering
Subtitle of host publicationImage Processing, Biosignal Processing, Modelling and Simulation, Biomechanics
PublisherSpringer Verlag
Pages1310-1313
Number of pages4
Edition4
ISBN (Print)9783642038815
DOIs
StatePublished - 2009
EventWorld Congress on Medical Physics and Biomedical Engineering: Image Processing, Biosignal Processing, Modelling and Simulation, Biomechanics - Munich, Germany
Duration: 7 Sep 200912 Sep 2009

Publication series

NameIFMBE Proceedings
Number4
Volume25
ISSN (Print)1680-0737

Conference

ConferenceWorld Congress on Medical Physics and Biomedical Engineering: Image Processing, Biosignal Processing, Modelling and Simulation, Biomechanics
Country/TerritoryGermany
CityMunich
Period7/09/0912/09/09

Fingerprint

Dive into the research topics of 'The use of EIT in the detection of regional lung dysfunction in prematurely born neonates'. Together they form a unique fingerprint.

Cite this