Comparison of methods for adaptive removal of motion artifact

Hamilton, P.S.; Curley, M.G.; Aimi, Roberto; Sae-Hau, C.

doi:10.1109/cic.2000.898537

Cited by 68 publications

(44 citation statements)

References 2 publications

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“…Approaches previously investigated to reduce motion artifacts include skin abrasion at the electrode site (Tam andWebster 1977, Burbank and, and adaptive filtering techniques that utilize as a reference signal either electrode motion measured using single or dual axis accelerometers (Raya and Sison 2002, Tong et al 2002) or a two-axis magnetoresistive sensor (Tong et al 2002), skin stretch related signals measured using a strain gauge (Hamilton and Curley 1997, Hamilton et al 2000a, 2000b, 1999, or electrode/skin impedance measurements (Hamilton et al 2000b, Devlin et al 1984 Tam and Webster's (1977) research indicated that the change in skin potential is the major source of motion artifact and proposed the method of skin abrasion to minimize this artifact. Odman and Oberg (1982) studied movement-induced potentials in various electrode configurations and concluded that potentials generated by skin deformation beneath the electrode dominate the disturbance pattern in ECG recording.…”

Section: Previous Studiesmentioning

confidence: 99%

“…Hamilton et al (Hamilton and Curley 1997, Hamilton et al 2000a, 2000b, 1999) mounted a strain gauge and an optical bend sensor on the exposed surface of an electrode respectively to measure electrode deformation as the noise reference.…”

Section: Previous Studiesmentioning

confidence: 99%

“…However, the torso impedance will introduce the possibility of impedance artifact generated by breathing or heart pumping. Hamilton et al (2000b) further investigated using skin / electrode impedance to adaptively filter motion artifact from ECG signals. Low frequency impedance was measured in Hamilton's (2000b) study, different from the 20 kHz impedance used by Devlin et al (1984), since low frequency impedance should be more sensitive to changes in skin impedance and less sensitive to changes in torso impedance.…”

Section: Previous Studiesmentioning

confidence: 99%

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Reduction of Skin Stretch Induced Motion Artifacts in Electrocardiogram Monitoring Using Adaptive Filtering

Liu

Pecht

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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Cardiovascular disease (CVD) is the leading cause of death in many regionsworldwide, accounting for nearly one third of global deaths in 2001. Wearable electrocardiographic cardiovascular monitoring devices have contributed to reduce CVD mortality and cost by enabling the diagnosis of conditions with infrequent symptoms, the timely detection of critical signs that can be precursor to sudden cardiac death, and the long-term assessment/monitoring of symptoms, risk factors, and the effects of therapy. However, the effectiveness of ambulatory electrocardiography to improve the treatment of CVD can be significantly impaired by motion artifacts which can cause misdiagnoses, inappropriate treatment decisions, and trigger false alarms. Skin stretch associated with patient motion is a main source of motion artifact in current ECG monitors. A promising approach to reduce motion artifact is the use of adaptive filtering that utilizes a measured reference input correlated with the motion artifact to extract noise from the ECG signal. Previous attempts to apply adaptive filtering to electrocardiography have employed either electrode deformation or acceleration, body acceleration, or skin/electrode impedance as a reference input, and were not successful at reducing motion artifacts in a consistent and reproducible manner. This has been essentially attributed to the lack of correlation between the reference input selected and the induced noise.In this study, motion artifacts are adaptively filtered by using skin strain as the reference signal. Skin strain is measured non-invasively using a light emitting diode (LED) and an optical sensor incorporated in an ECG electrode. The optical strain sensor is calibrated on animal skin samples and finally in-vivo, in terms of sensitivity and measurement range. Skin stretch induced artifacts are extracted in-vivo using adaptive filters. The system and method are tested for different individuals and under various types of ambulatory conditions with the noise reduction performance quantified.

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Section: Previous Studiesmentioning

confidence: 99%

Section: Previous Studiesmentioning

confidence: 99%

Section: Previous Studiesmentioning

confidence: 99%

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Reduction of Skin Stretch Induced Motion Artifacts in Electrocardiogram Monitoring Using Adaptive Filtering

Liu

Pecht

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Variable step size least mean square AF (VSS-LMS-AF) [6] was used to remove MA in the AECG signal. Two cascade AFs [7] were applied to prevent the signal saturation and track large sudden MA quickly.…”

Section: Introductionmentioning

confidence: 99%

Motion Artifact Suppression in Ambulatory ECG with Feed Forward Combine Adaptive Filter

Zhang

Jin

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…When removing motion artifacts, the most common artifact references selected are accelerometers [10], however other sources such as optical bend sensors and impedance sensors [15] have also been assessed. Accelerometers are predominantly selected due to their high accuracy level combined with an ease of implementation which is critical for the assisted living domain.…”

Section: Adaptive Filteringmentioning

confidence: 99%

A review of the state of the art in artifact removal technologies as used in an assisted living domain

Sweeney

Kelly

Ward

et al. 2011

IET Seminar on Assisted Living 2011

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There has been significant growth in the area of ubiquitous, pervasive, distributed healthcare technologies due to the increasing burden on the healthcare system and the impending demographic shift towards an aging population. The move from a hospital-centric healthcare system towards in-home health assessment is aimed to alleviate the burden on healthcare professionals, the health care system and caregivers. Advances in signal acquisition, data storage and communication channels provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and reduce the utility of the affected part of the signal. The degrading effect of the artifacts significantly increases when data collection is moved from the clinic into the home. Advances in signal processing have brought about significant improvement in artifact removal over the last number of years. This paper reviews the most common physiological and location-indicative signals recorded in the home and documents the artifacts which occur most often. A discussion of some of the most common artifact removal techniques is then provided. An evaluation of the advantages and disadvantages of each is given with reference to the assisted living environment.

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Comparison of methods for adaptive removal of motion artifact

Cited by 68 publications

References 2 publications

Reduction of Skin Stretch Induced Motion Artifacts in Electrocardiogram Monitoring Using Adaptive Filtering

Reduction of Skin Stretch Induced Motion Artifacts in Electrocardiogram Monitoring Using Adaptive Filtering

Motion Artifact Suppression in Ambulatory ECG with Feed Forward Combine Adaptive Filter

A review of the state of the art in artifact removal technologies as used in an assisted living domain

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