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Algorithms 2015, 8(4), 1052-1075; doi:10.3390/a8041052

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

1
Biomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
2
Institute for Biomedical Engineering, ETH Zurich, 8092 Zurich, Switzerland
3
Member of the PhD Program “Integrative Molecular Medicine”, University of Zurich, 8057 Zurich, Switzerland
4
Institute for Complementary Medicine, University of Bern, 3012 Bern, Switzerland
5
Zurich Center for Integrative Human Physiology Zurich, University Zurich, 8057 Zurich, Switzerland
6
Institute for Pharmacology and Toxicology, Chronobiology and Sleep Research, University of Zurich, 8057 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editor: Stephan Chalup
Received: 6 July 2015 / Revised: 14 September 2015 / Accepted: 28 October 2015 / Published: 19 November 2015
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Abstract

Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics and oxygenation in tissue. Changes in light-coupling due to movement of the subject can cause movement artifacts (MAs) in the recorded signals. Several methods have been developed so far that facilitate the detection and reduction of MAs in the data. However, due to fixed parameter values (e.g., global threshold) none of these methods are perfectly suitable for long-term (i.e., hours) recordings or were not time-effective when applied to large datasets. We aimed to overcome these limitations by automation, i.e., data adaptive thresholding specifically designed for long-term measurements, and by introducing a stable long-term signal reconstruction. Our new technique (“acceleration-based movement artifact reduction algorithm”, AMARA) is based on combining two methods: the “movement artifact reduction algorithm” (MARA, Scholkmann et al. Phys. Meas. 2010, 31, 649–662), and the “accelerometer-based motion artifact removal” (ABAMAR, Virtanen et al. J. Biomed. Opt. 2011, 16, 087005). We describe AMARA in detail and report about successful validation of the algorithm using empirical NIRS data, measured over the prefrontal cortex in adolescents during sleep. In addition, we compared the performance of AMARA to that of MARA and ABAMAR based on validation data. View Full-Text
Keywords: movement artifact reduction algorithm (MARA); acceleration-based motion artifact removal (ABAMAR); acceleration-based movement artifact reduction algorithm (AMARA); motion artifacts; movement artifacts; near-infrared spectroscopy (NIRS); functional-near infrared spectroscopy (fNIRS) movement artifact reduction algorithm (MARA); acceleration-based motion artifact removal (ABAMAR); acceleration-based movement artifact reduction algorithm (AMARA); motion artifacts; movement artifacts; near-infrared spectroscopy (NIRS); functional-near infrared spectroscopy (fNIRS)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Metz, A.J.; Wolf, M.; Achermann, P.; Scholkmann, F. A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data. Algorithms 2015, 8, 1052-1075.

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