NDLI: Employing ensemble empirical mode decomposition for artifact removal: Extracting accurate respiration rates from ECG data during ambulatory activity

Content Provider	IEEE Xplore Digital Library
Author	Sweeney, K.T. Kearney, D. Ward, T.E. Coyle, S. Diamond, D.
Copyright Year	2013
Description	Author affiliation: CLARITY: Centre for Sensor Web Technol., Dublin City Univ., Dublin, Ireland (Sweeney, K.T.; Coyle, S.; Diamond, D.) \|\| Dept. of Electron. Eng., Nat. Univ. of Ireland, Maynooth, Ireland (Kearney, D.; Ward, T.E.)
Abstract	Observation of a patient's respiration signal can provide a clinician with the required information necessary to analyse a subject's wellbeing. Due to an increase in population number and the aging population demographic there is an increasing stress being placed on current healthcare systems. There is therefore a requirement for more of the rudimentary patient testing to be performed outside of the hospital environment. However due to the ambulatory nature of these recordings there is also a desire for a reduction in the number of sensors required to perform the required recording in order to be unobtrusive to the wearer, and also to use textile based systems for comfort. The extraction of a proxy for the respiration signal from a recorded electrocardiogram (ECG) signal has therefore received considerable interest from previous researchers. To allow for accurate measurements, currently employed methods rely on the availability of a clean artifact free ECG signal from which to extract the desired respiration signal. However, ambulatory recordings, made outside of the hospital-centric environment, are often corrupted with contaminating artifacts, the most degrading of which are due to subject motion. This paper presents the use of the ensemble empirical mode decomposition (EEMD) algorithm to aid in the extraction of the desired respiration signal. Two separate techniques are examined; 1) Extraction of the respiration signal directly from the noisy ECG 2) Removal of the artifact components relating to the subject movement allowing for the use of currently available respiration signal detection techniques. Results presented illustrate that the two proposed techniques provide significant improvements in the accuracy of the breaths per minute (BPM) metric when compared to the available true respiration signal. The error reduced from ± 5.9 BPM prior to the use of the two techniques to ± 2.9 and ± 3.3 BPM post processing using the EEMD algorithm techniques.
Starting Page	977
Ending Page	980
File Size	1621167
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609666
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrocardiography Protocols Biomedical monitoring Monitoring Accelerometers Sensor systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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