Gas Exchange Ventilatory Threshold
For the analysis of gas-exchange data and the determination of the gas-exchange ventilatory threshold, we have developed WinBreak 3.7, a professional tool that provides far greater flexibility than the proprietary automated algorithms that are built into commercial metabolic analysis software packages.
Some of the features of WinBreak 3.7 include:
- Three graphical modules: V-slope (VCO2 by VO2), ventilatory equivalents, and excess CO2. Module for inter-method comparisons.
- Choice of five validated algorithms for the V-slope: (a) Jones & Molitoris (1984), Orr et al. (1982), Beaver et al. (1986), Cheng et al. (1992), Sue et al. (1988). The standard Jones & Molitoris (1984) algorithm is used for the ventilatory equivalents, and excess CO2 modules. Complete computational reports for each algorithm.
- Module for determining the respiratory compensation point (RC) based on VE by VCO2 (following Beaver et al., 1986).
- Plots of residuals of the single-regression and two-regression solutions.
- Powerful data processing: averaging, interpolation, outlier removal, five smoothing methods (running average, Savitzky-Golay, low-pass FFT, cubic spline, polynomial regressions from 2nd to 10th order).
- Fully customizable graphs, can be saved as metafiles or bitmaps.
- Extensive context-sensitive help system.
- Detailed 78-page user guide.
- Can be customized to read ASCII data from any metabolic analysis software package. Saves data in ASCII and ExcelTM formats.
Near-Infrared Spectroscopy (NIRS)
For the analysis of NIRS data acquired with our ISS OxiplexTS frequency domain, multi-distance tissue spectrometer, we have developed a NIRS data processor that automatically applies a multistage pipeline and saves data in ExcelTM files. Specifically, the software proceeds through the following steps:
- First, it applies the movement artifact removal algorithm from the NIRS Analysis Package (NAP), to remove spikes (i.e., near-instantaneous signal inflections much larger in amplitude than the typical amplitude of the hemodynamic signal) and correct discontinuities (i.e., baseline shifts). This algorithm uses piecewise low-order polynomial interpolation to reconstruct data segments affected by movement artifacts.
- Second, it removes the very-low and high parts of the frequency spectrum by applying a third-order Butterworth filter, with bandpass settings of 0.008 and 0.5 Hz. This step is intended to remove oscillations due to heart pulsations (i.e., 2 Hz or higher during exercise) and respiration (i.e., 0.5 Hz or higher during exercise).
- Third, it applies the denoising algorithm of Feuerstein et al. The goal of this algorithm is to separate the noise from the signal given their differences in amplitude (assuming that the noise has larger amplitude than the underlying hemodynamic signal). The algorithm first calculates the difference between the original signal and a smoothed signal resulting from a quadratic Savitzky-Golay filter and then uses a histogram of this signal difference to iteratively seek the filtering threshold that minimizes the variance overlap between the presumed signal and the presumed noise.
- Fourth, for each timeseries, it fits a linear regression through the O2Hb and HHb data segments representing the "baseline" period and then expresses all O2Hb and HHb data points as changes from this baseline.
- Finally, it divides each timeseries representing exercise periods into segments and calculates the median value of O2Hb and HHb for each segment. These median values are then used to calculate the Tissue Oxygenation Index and the [O2Hb] – [HHb] difference (ΔHbDiff) that is used in statistical analyses.
Infrared Reflectance Oculography
For the analysis of infrared reflectance oculography data acquired with our San Diego Instruments SR-HLAB photoelectric cell (PEC) system, we developed software that automatically extracts information related to acoustic startle eyeblinks, including latency and peak amplitude.