DocumentCode :
2941436
Title :
Intelligent artifact classification for ambulatory physiological signals
Author :
Sweeney, Kevin T. ; Leamy, Darren J. ; Ward, Tomás E. ; McLoone, Seán
Author_Institution :
Dept. of Electron. Eng., Nat. Univ. of Ireland Maynooth, Maynooth, Ireland
fYear :
2010
fDate :
Aug. 31 2010-Sept. 4 2010
Firstpage :
6349
Lastpage :
6352
Abstract :
Connected health represents an increasingly important model for health-care delivery. The concept is heavily reliant on technology and in particular remote physiological monitoring. One of the principal challenges is the maintenance of high quality data streams which must be collected with minimally intrusive, inexpensive sensor systems operating in difficult conditions. Ambulatory monitoring represents one of the most challenging signal acquisition challenges of all in that data is collected as the patient engages in normal activities of everyday living. Data thus collected suffers from considerable corruption as a result of artifact, much of it induced by motion and this has a bearing on its utility for diagnostic purposes. We propose a model for ambulatory signal recording in which the data collected is accompanied by labeling indicating the quality of the collected signal. As motion is such an important source of artifact we demonstrate the concept in this case with a quality of signal measure derived from motion sensing technology viz. accelerometers. We further demonstrate how different types of artifact might be tagged to inform artifact reduction signal processing elements during subsequent signal analysis. This is demonstrated through the use of multiple accelerometers which allow the algorithm to distinguish between disturbance of the sensor relative to the underlying tissue and movement of this tissue. A brain monitoring experiment utilizing EEG and fNIRS is used to illustrate the concept.
Keywords :
accelerometers; bio-optics; biological tissues; biomechanics; electroencephalography; infrared spectroscopy; medical signal processing; patient monitoring; signal classification; EEG; accelerometers; ambulatory physiological signals; artifact reduction signal processing; brain monitoring; fNIRS; health care delivery; intelligent artifact classification; motion sensing technology; remote physiological monitoring; tissue; Accelerometers; Biomedical monitoring; Electrodes; Electroencephalography; Magnetic heads; Monitoring; Noise; Artifacts; Electrocardiography; Humans; Monitoring, Ambulatory; Signal Processing, Computer-Assisted; Spectroscopy, Near-Infrared;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4123-5
Type :
conf
DOI :
10.1109/IEMBS.2010.5627285
Filename :
5627285
Link To Document :
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