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.
Highlights
By shining light with specific wavelengths in the near-infrared range into tissue and measuring the diffusely back-reflected light, near-infrared spectroscopy (NIRS) is able to determine concentration changes of oxy- and deoxyhemoglobin ([O2Hb], [HHb]), which are related to changes in tissue hemodynamics and oxygenation [1,2,3,4]
acceleration-based movement artifact reduction algorithm” (AMARA) had a higher sensitivity when only the movement detection was taken into account
We developed a new automated artifact removal algorithm (AMARA) based on MARA and integrated the beneficial features of ABAMAR
Summary
By shining light with specific wavelengths in the near-infrared range (approx. 650–950 nm) into tissue and measuring the diffusely back-reflected light, near-infrared spectroscopy (NIRS) is able to determine concentration changes of oxy- and deoxyhemoglobin ([O2Hb], [HHb]), which are related to changes in tissue hemodynamics and oxygenation [1,2,3,4]. By shining light with specific wavelengths in the near-infrared range 650–950 nm) into tissue and measuring the diffusely back-reflected light, near-infrared spectroscopy (NIRS) is able to determine concentration changes of oxy- and deoxyhemoglobin ([O2Hb], [HHb]), which are related to changes in tissue hemodynamics and oxygenation [1,2,3,4]. NIRS recordings in particular, for example during sleep [19,20,21,22,23,24,25], artifacts in the NIRS data due to movements of the subjects are a common problem. Several NIRS signal-processing methods have been developed so far to detect and remove movement artifacts (MAs). These methods can be classified into (i) univariate methods,. (ii) multivariate methods of type 1, and (iii) multivariate methods of type 2 [2]
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