Artifact Removal in Physiological Signals-Practices and Possibilities

• Published in: IEEE transactions on information technology in biomedicine Vol. 16; no. 3; pp. 488 - 500
• Main Authors: Sweeney, K. T., Ward, T. E., McLoone, S. F.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2012
• Subjects: Adaptive Filter; artifact removal; Biomedical monitoring; blind source separation (BSS); canonical correlation analysis (CCA); Electroencephalography; Electromyography; Humans; independent component analysis (ICA); Kalman filter; Medical services; Monitoring; Monitoring, Physiologic; Optical variables measurement; personal healthcare; Signal Processing, Computer-Assisted; Statistics as Topic; Wiener filter
• ISSN: 1089-7771; 1558-0032
• DOI: 10.1109/TITB.2012.2188536

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare "time bomb" has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.