  Preterm infants are at high risk of developing brain injury in the first days
of life as a consequence of poor cerebral oxygen delivery. Near-infrared
spectroscopy (NIRS) is an established technology developed to monitor regional
tissue oxygenation. Detailed waveform analysis of the cerebral NIRS signal
could improve the clinical utility of this method in accurately predicting
brain injury. Frequent transient cerebral oxygen desaturations are commonly
observed in extremely preterm infants, yet their clinical significance remains
unclear. The aim of this study was to examine and compare the performance of
two distinct approaches in isolating and extracting transient deflections
within NIRS signals. We optimized three different simultaneous low-pass
filtering and total variation denoising (LPF_TVD) methods and compared their
performance with a recently proposed method that uses singular-spectrum
analysis and the discrete cosine transform (SSA_DCT). Parameters for the
LPF_TVD methods were optimized over a grid search using synthetic NIRS-like
signals. The SSA_DCT method was modified with a post-processing procedure to
increase sparsity in the extracted components. Our analysis, using a synthetic
NIRS-like dataset, showed that a LPF_TVD method outperformed the modified
SSA_DCT method: median mean-squared error of 0.97 (95% CI: 0.86 to 1.07) was
lower for the LPF_TVD method compared to the modified SSA_DCT method of 1.48
(95% CI: 1.33 to 1.63), P<0.001. The dual low-pass filter and total variation
denoising methods are considerably more computational efficient, by 3 to 4
orders of magnitude, than the SSA_DCT method. More research is needed to
examine the efficacy of these methods in extracting oxygen desaturation in real
NIRS signals.
