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A new approach for automatic removal of movement artifacts in near-infrared spectroscopy time series by means of acceleration data


Metz, Andreas; Wolf, Martin; Achermann, Peter; Scholkmann, Felix (2015). A new approach for automatic removal of movement artifacts in near-infrared spectroscopy time series by means of acceleration data. Algorithms, 8(4):1052-1075.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Neonatology
04 Faculty of Medicine > Institute of Pharmacology and Toxicology
04 Faculty of Medicine > Center for Integrative Human Physiology
Dewey Decimal Classification:610 Medicine & health
Uncontrolled 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)
Language:English
Date:2015
Deposited On:15 Dec 2015 09:41
Last Modified:05 Apr 2016 19:37
Publisher:MDPI Publishing
ISSN:1999-4893
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3390/a8041052
Official URL:http://www.mdpi.com/1999-4893/8/4/1052

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