How random measurement errors propagate to the chromophore concentration calculation in broadband near-infrared spectroscopy

We describe innovative methodology for monitoring alterations in bladder oxygenation and haemodynamics in humans using near-infrared spectroscopy (NIRS). Concentrations of the chromophores oxygenated (O2Hb) and deoxygenated (HHb) haemoglobin and their sum (total haemoglobin) differ during bladder contraction in health and disease. A wireless device that incorporates three paired light emitting diodes (wavelengths 760 and 850 nanometers) and silicon photodiode detector collects data transcutaneously (10 Hz) with the emitter/detector over the bladder during spontaneous bladder emptying. Data analysis indicates comparable patterns of change in chromophore concentration in healthy children and adults (positive trend during voiding, predominantly due to elevated O2Hb), but different changes in symptomatic subjects with characteristic chromophore patterns identified for voiding dysfunction due to specific pathophysiologies: bladder outlet obstruction (males), overactive bladder (females), and nonneurogenic dysfunction (children). Comparison with NIRS muscle data suggests altered bladder haemodynamics and/or oxygenation may underlie voiding dysfunction offering new insight into the causal physiology.

Download Full-text

Effect of Sample Complexity on Quantification of Analytes in Aqueous Samples by Near-Infrared Spectroscopy

Applied Spectroscopy ◽

10.1366/0003702001949186 ◽

2000 ◽

Vol 54 (2) ◽

pp. 255-261 ◽

Cited By ~ 5

Author(s):

Mark R. Riley ◽

Mark A. Arnold ◽

David W. Murhammer

Keyword(s):

Infrared Spectroscopy ◽

Small Molecules ◽

Near Infrared Spectroscopy ◽

Standard Error ◽

Measurement Errors ◽

Near Infrared ◽

Small Degree ◽

Sample Complexity ◽

Aqueous Samples ◽

The Impact

This study was undertaken to quantitate the impact of increasing sample complexity on near-infrared spectroscopic (NIRS) measurements of small molecules in aqueous solutions with varying numbers of components. Samples with 2, 6, or 10 varying components were investigated. Within the 10-component samples, three analytes were quantified with errors below 6% and seven of the analytes were quantified with errors below 10%. An increase in the number of varying components can substantially increase the error associated with measurement. A comparison of measurement errors across sample sets, as gauged by the standard error of prediction (SEP), reveals that an increase in the number of varying components from 2 to 6 increases the SEP by approximately 50%. An increase from 2 to 10 varying components increases the SEP by approximately 340%. While there appear to be no substantial correlations between the presence of a specific analyte and the errors associated with quantification of another analyte, several analytes do display a small degree of sensitivity to varying concentrations of certain background components. The analysis also demonstrates that calibrations containing an overestimation of the numbers of varying components can substantially increase measurement errors and so calibrations must be constructed with an accurate understanding of the number of varying components that are likely to be encountered.

Download Full-text

Algorithm construction methodology for diagnostic classification of near-infrared spectroscopy data

Spectroscopy An International Journal ◽

10.1155/2011/752101 ◽

2011 ◽

Vol 25 (1) ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Ramón Guevara ◽

Lynn Stothers ◽

Andrew Macnab

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Pelvic Floor Muscle ◽

Classification And Regression Tree ◽

Data Sets ◽

Linear Discriminant ◽

Cart Analysis ◽

Chromophore Concentration ◽

Nirs Data

Background: Near-infrared spectroscopy (NIRS) has recognized potential but limited application for non-invasive diagnostic evaluation. Data analysis methodology that reproducibly distinguishes between the presence or absence of physiologic abnormality could broaden clinical application of this optical technique.Methods: Sample data sets from simultaneous NIRS bladder monitoring and invasive urodynamic pressure-flow studies (UDS) are used to illustrate how a diagnostic algorithm is constructed using classification and regression tree (CART) analysis. Misclassification errors of CART and linear discriminant analysis (LDA) are computed and examples of other urological NIRS data likely amenable to CART analysis presented.Results: CART generated a clinically relevant classification algorithm (error 4%) using 46 data sets of changes in chromophore concentration composed of the whole time series without specifying features. LDA did not (error 16%). Using CART NIRS data provided comparable discriminant ability to the UDS diagnostic nomogram for the presence or absence of obstructive pathology (88% specificity, 84% precision). Pilot data examples from children with and without voiding dysfunction and women with mild or severe pelvic floor muscle dysfunction also show potentially diagnostic differences in chromophore concentration.Conclusions: CART analysis can likely be applied in other NIRS monitoring applications intended to classify patients into those with and without pathology.

Download Full-text