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

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^

Primary production at time series station DYFAMED

Phytoplankton carbon assimilation has been measured near monthly using the 14C method at DYFAMED France JGOFS time-series station from 1993 to 1999. Data were obtained using the "LET GO" technique, which allowed in situ injection of bicarbonate and incubation in enclosures at 10 depths. Incubation duration was 4 h around noon, from which daily production was estimated. The seasonal variation of the depth-integrated carbon assimilation exhibits a marked cycle. Maximum values reach 1.8 g C/m**2/d in March or April; constant lower values were observed from August to January, in the range 100-300 mg C/m**2/d. The annual primary production vary in the range 86-232 g C/m**2/yr, in the upper range of older estimations. Primary production normalized to chlorophyll a shows maximum values in the period of oligotrophy. This increase of carbon assimilation rate per unit of chlorophyll a appears as linked to the period of phosphorus-limited ecosystem, and vertical distribution of taxonomic pigments suggests a possible role of cyanobacteria. Potential export production has been estimated from primary production data and Fp ratio based on pigments concentrations. These estimates (which imply biological steady state conditions) vary in a wide range, from 19 to 71 g C/m**2/yr. There is a decoupling between years with high potential export production and years with high measured particulate fluxes, which highlights the question of balance by resupply of the limiting nutrients and the role of dissolved organic carbon. A possible shift of primary production towards a more regeneration-dominated system is suggested for recent years.

Robust trends of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks, with links to GeoTIFFs

Arctic permafrost landscapes are among the most vulnerable and dynamic landscapes globally, but due to their extent and remoteness most of the landscape changes remain unnoticed. In order to detect disturbances in these areas we developed an automated processing chain for the calculation and analysis of robust trends of key land surface indicators based on the full record of available Landsat TM, ETM +, and OLI data. The methodology was applied to the ~ 29,000 km**2 Lena Delta in Northeast Siberia, where robust trend parameters (slope, confidence intervals of the slope, and intercept) were calculated for Tasseled Cap Greenness, Wetness and Brightness, NDVI, and NDWI, and NDMI based on 204 Landsat scenes for the observation period between 1999 and 2014. The resulting datasets revealed regional greening trends within the Lena Delta with several localized hot-spots of change, particularly in the vicinity of the main river channels. With a 30-m spatial resolution various permafrost-thaw related processes and disturbances, such as thermokarst lake expansion and drainage, fluvial erosion, and coastal changes were detected within the Lena Delta region, many of which have not been noticed or described before. Such hotspots of permafrost change exhibit significantly different trend parameters compared to non-disturbed areas. The processed dataset, which is made freely available through the data archive PANGAEA, will be a useful resource for further process specific analysis by researchers and land managers. With the high level of automation and the use of the freely available Landsat archive data, the workflow is scalable and transferrable to other regions, which should enable the comparison of land surface changes in different permafrost affected regions and help to understand and quantify permafrost landscape dynamics.

Atmospheric and seawater carbon dioxide at time series station DYFAMED

A time series of fCO2, SST, and fluorescence data was collected between 1995 and 1997 by a CARIOCA buoy moored at the DyFAMed station (Dynamique des Flux Atmospheriques en Mediterranée) located in the northwestern Mediterranean Sea. On seasonal timescales, the spring phytoplankton bloom decreases the surface water fCO2 to approximately 290 µatm, followed by summer heating and a strong increase in fCO2 to a maximum of approximately 510 µatm. While the DELTA fCO2 shows strong variations on seasonal timescales, the annual average air-sea disequilibrium is only 2 µatm. Temperature-normalized fCO2 shows a continued decrease in dissolved CO2 throughout the summer and fall at a rate of approximately 0.6 µatm/d. The calculated annual air-sea CO2 transfer rate is -0.10 to -0.15 moles CO2 m-2 y-1, with these low values reflecting the relatively weak wind speed regime and small annual air-sea fCO2 disequilibrium. Extrapolating this rate over the whole Mediterranean Sea would lead to a flux of approximately -3 * 10**12 to -4.5 * 10**12 grams C/y, in good agreement with other estimates. An analysis of the effects of sampling frequency on annual air-sea CO2 flux estimates showed that monthly sampling is adequate to resolve the annual CO2 flux to within approximately ±10 - 18% at this site. Annual flux estimates made using temperature-derived fCO2 based on the measured fCO2-SST correlations are in agreement with measurement-based calculations to within ± 7-10% (depending on the gas transfer parameterization used), and suggest that annual CO2 flux estimates may be reasonably well predicted in this region from satellite or model-derived SST and wind speed information.

Collection of data on soil carbon (particulate and dissolved) in the Jena Experiment (Main Experiment, time series since 2002)

This data set contains three time series of measurements of soil carbon (particular and dissolved) from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. 1. Particulate soil carbon: Stratified soil sampling was performed every two years since before sowing in April 2002 and was repeated in April 2004, 2006 and 2008 to a depth of 30 cm segmented to a depth resolution of 5 cm giving six depth subsamples per core. Total carbon concentration was analyzed on ball-milled subsamples by an elemental analyzer at 1150°C. Inorganic carbon concentration was measured by elemental analysis at 1150°C after removal of organic carbon for 16 h at 450°C in a muffle furnace. Organic carbon concentration was calculated as the difference between both measurements of total and inorganic carbon. 2. Particulate soil carbon (high intensity sampling): In one block of the Jena Experiment soil samples were taken to a depth of 1 m (segmented to a depth resolution of 5 cm giving 20 depth subsamples per core) with three replicates per block ever 5 years starting before sowing in April 2002. Samples were processed as for the more frequent sampling. 3. Dissolved organic carbon: Suction plates installed on the field site in 10, 20, 30 and 60 cm depth were used to sample soil pore water. Cumulative soil solution was sampled biweekly and analyzed for dissolved organic carbon concentration by a high TOC elemental analyzer. Annual mean values of DOC are provided.