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

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.