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Content available remote Selecting the optimal conditions of Savitzky–Golay filter for fNIRS signal
EN
This paper proposes a method to find the best conditions for applying Savitzky–Golay (SG) filter to remove physiological noises from the functional near-infrared spectroscopy (fNIRS) signal. A narrative review on existing physiological noise reduction techniques from fNIRS signal demonstrates that the most common methods are window based finite impulse response (FIR) and SG filters. However, these filters did not clarify why and how it is able to remove noises from the fNIRS signal. This paper shows a systemic investigation of works performed with window based FIR filter and SG filter and found very convincing results to use SG filter with specific conditions. Three main frequency bands (0–0.1 Hz, 0–0.14 Hz, and 0.03–0.1 Hz) have been considered as standard for fNIRS signal filtering and filtered the signals by window-based FIR filter. With a number of conditions of SG filter, the raw fNIRS signals were filtered again and checked the correlation between filtered signals by FIR and SG. By check and trial basis, the best correlations were revealed. To validate the proposed results, several golden standard statistical investigations were analyzed. The experimental results propose a recommendation which indicates the best conditions of the SG filter to remove physiological noises from the fNIRS signals.
EN
A tunable diode laser absorption spectrometer was developed for trace acetylene gas analysis, the system performance was evaluated by combing an appropriate digital signal processing methods, i.e., Savitzky–Golay smoothing and differentiation algorithm. The calculation of the derivative spectra by this method is accompanied by simultaneous data smoothing. Based on the 2nd deriv- ative spectra detection method, the Allan variance technique indicates a C2H2 detection limit of 1.7ppm for 1-s averaging time, and a minimum noise level of 62ppb (parts per billion), at the optimum integration time of ~60s. The calculated 2nd derivative spectra with better resolution, lower detection limits, save signal processing time, and improve the ability to distinguish unresolved spectral signals.
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