Online Removal of Baseline Shift with a Polynomial Function for Hemodynamic Monitoring Using Near-Infrared Spectroscopy

Abstract

Near-infrared spectroscopy (NIRS) has become widely accepted as a valuable tool for noninvasively monitoring hemodynamics for clinical and diagnostic purposes. Baseline shift has attracted great attention in the field, but there has been little quantitative study on baseline removal. Here, we aimed to study the baseline characteristics of an in-house-built portable medical NIRS device over a long time (>3.5 h). We found that the measured baselines all formed perfect polynomial functions on phantom tests mimicking human bodies, which were identified by recent NIRS studies. More importantly, our study shows that the fourth-order polynomial function acted to distinguish performance with stable and low-computation-burden fitting calibration (R-square >0.99 for all probes) among second- to sixth-order polynomials, evaluated by the parameters R-square, sum of squares due to error, and residual. This study provides a straightforward, efficient, and quantitatively evaluated solution for online baseline removal for hemodynamic monitoring using NIRS devices.

Longtime driving induced cerebral hemodynamic elevation and behavior degradation as assessed by functional near‐infrared spectroscopy and a voluntary attention test

“…The fNIRS data of each subject was previewed and the data sequence that was marked as “motion artifact” by the experiment performer during the surveillance on the behavior of subject was excluded. In addition, the baseline during the time period right before the attention test (Figure D) was extracted and the baseline shift of the whole measurement was calibrated with the baseline correction method in . For the motion artifacts, we marked timelines when a motion happened and ended to the subjects, and the data sequence of the motion was excluded.…”

Affordable and Easy Data Exploration of NIR Spectra Using Chemometric Techniques

“…The optical path length of the NIR beam penetrating the sample surface can reach up to several hundred micrometers. Therefore, the path length and the reflected light detected by the NIR spectrometer differ, leading to baseline shifts in the spectra, even of the same sample . In order to prevent this, cautious sample preparation is indispensable, while the preprocessing afterward is just a slight correction compared to the sample preparation.…”

Applications of Near Infrared Photoacoustic Spectroscopy for Analysis of Human Respiration: A Review

“…The LPAS present a high sensitivity and selectivity for trace gas detection and allows on-line measurements. Noise plays an important role in all PA measurements and is of particular importance in trace detection, because the noise level limits the sensitivity [25,67,77,78]. The sensitivity-limiting factors which are encountered in LPAS can be classified into three categories [25,67]: (c) Coherent photoacoustic background signal.…”