Wavelength Selection Method with Standard Deviation: Application to Pulse Oximetry

• Published in: Annals of biomedical engineering Vol. 39; no. 7; pp. 1994 - 2009
• Main Authors: Vazquez-Jaccaud, Camille, Paez, Gonzalo, Strojnik, Marija
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.07.2011; Springer Nature B.V
• Subjects: Algorithms; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classical Mechanics; Data Interpretation, Statistical; Diagnosis, Computer-Assisted - methods; Humans; Infrared spectroscopy; Monitoring; Monitoring methods; Noise; Oximetry; Oximetry - methods; Oxygen - blood; Reproducibility of Results; Sensitivity and Specificity; Spectra; Spectroscopy; Standard deviation; Temporal logic; Wavelengths; Spectroscopy; Standard deviation; Pulse oximetry; Medical optics; Near-IR; Human functions as temporal noise
• ISSN: 0090-6964; 1573-9686; 1573-9686
• DOI: 10.1007/s10439-011-0304-7

Near-infrared spectroscopy provides useful biological information after the radiation has penetrated through the tissue, within the therapeutic window. One of the significant shortcomings of the current applications of spectroscopic techniques to a live subject is that the subject may be uncooperative and the sample undergoes significant temporal variations, due to his health status that, from radiometric point of view, introduce measurement noise. We describe a novel wavelength selection method for monitoring, based on a standard deviation map, that allows low-noise sensitivity. It may be used with spectral transillumination, transmission, or reflection signals, including those corrupted by noise and unavoidable temporal effects. We apply it to the selection of two wavelengths for the case of pulse oximetry. Using spectroscopic data, we generate a map of standard deviation that we propose as a figure-of-merit in the presence of the noise introduced by the living subject. Even in the presence of diverse sources of noise, we identify four wavelength domains with standard deviation, minimally sensitive to temporal noise, and two wavelengths domains with low sensitivity to temporal noise.