PMID- 24109853
OWN - NLM
STAT- MEDLINE
DCOM- 20140619
LR  - 20200928
IS  - 2694-0604 (Electronic)
IS  - 2375-7477 (Linking)
VI  - 2013
DP  - 2013
TI  - Employing ensemble empirical mode decomposition for artifact removal: extracting 
      accurate respiration rates from ECG data during ambulatory activity.
PG  - 977-80
LID - 10.1109/EMBC.2013.6609666 [doi]
AB  - 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.
FAU - Sweeney, Kevin T
AU  - Sweeney KT
FAU - Kearney, Damien
AU  - Kearney D
FAU - Ward, Tomas E
AU  - Ward TE
FAU - Coyle, Shirley
AU  - Coyle S
FAU - Diamond, Dermot
AU  - Diamond D
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
MH  - Adult
MH  - *Algorithms
MH  - *Artifacts
MH  - *Electrocardiography
MH  - Female
MH  - Humans
MH  - Male
MH  - Monitoring, Ambulatory/*instrumentation
MH  - Respiration
MH  - Respiratory Rate/*physiology
MH  - Signal Processing, Computer-Assisted
EDAT- 2013/10/11 06:00
MHDA- 2014/06/20 06:00
CRDT- 2013/10/11 06:00
PHST- 2013/10/11 06:00 [entrez]
PHST- 2013/10/11 06:00 [pubmed]
PHST- 2014/06/20 06:00 [medline]
AID - 10.1109/EMBC.2013.6609666 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:977-80. doi: 
      10.1109/EMBC.2013.6609666.