Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring

Abstract

Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ. SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the NIRS measurements, facilitating the use of NIRS as a surrogate measure for cerebral blood flow (CBF). The second case study used data from a 3-years old infant under Extra Corporeal Membrane Oxygenation (ECMO), here SIDE-ObSP decomposed cerebral/peripheral tissue oxygenation, as a sum of the partial contributions from different systemic variables, facilitating the comparison between the effects of each systemic variable on the cerebral/peripheral hemodynamics.

Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study

“…Data analyses were performed by 14 participating centres on 44 datasets from 22 volunteers with two measurements each. The following dCA analysis methods were used: TFA (Reinhard et al, 2003a, Liu et al, 2005, van Beek et al, 2010, Gommer et al, 2010, Panerai, 2014, Mitsis et al, 2002, Zhang et al, 1998, Meel-van den Abeelen et al, 2014a, Muller et al, 2003, Muller and Osterreich, 2014, Panerai et al, 1998a, Laguerre expansion of 1 st -order Volterra kernels or finite impulse response models (Marmarelis, 2004, Marmarelis et al, 2013, Marmarelis et al, 2014b, Mitsis et al, 2004, Mitsis et al, 2009, wavelet analysis (Peng et al, 2010, Torrence and Webster, 1999, Grinsted et al, 2004, parametric finite-impulse response filter based methods (Panerai et al, 2000, Simpson et al, 2001, ARI anaysis (Panerai et al, 1998b), autoregressive moving average (ARMA) based ARI methods and variant ARI methods (Panerai et al, 2003), autoregressive with exogenous input (ARX) methods (Liu and Allen, 2002, Liu et al, 2003, Panerai et al, 2003 and correlation coefficient-like indices (Heskamp et al, 2013, Caicedo et al, 2016. A summary of the methods and corresponding references are given in Table 1.…”

Cerebral autoregulation and activity after propofol for endotracheal intubation in preterm neonates

“…Therefore, rScO 2 was corrected for changes in SaO 2 using oblique subspace projections. 22 This mathematical model decouples the linked dynamics between different underlying subsystems (SaO 2 and CBF) in order to decompose the observed output (rScO 2 ) in terms of the partial contributions of each input variable. The contribution of SaO 2 in the rScO 2 signal is hereby eliminated, which makes the residual component a more correct surrogate for CBF, suitable for the assessment of CAR.…”

Measuring Near-Infrared Spectroscopy Derived Cerebral Autoregulation in Neonates: From Research Tool Toward Bedside Multimodal Monitoring

“…Although used by Riera et al, this correction was not applied since they excluded the SaO 2 signals in their analysis ( 34 ). Caicedo et al proposed the use of oblique sub-space projections (ObSP) ( 35 , 36 ). ObSP makes use of sub-space system identification that uses input-output observations of the system in order to produce a mathematical model that can explain the measured output.…”