Introduction to Pulse Oximetry
Pulse oximetry is a non-invasive method allowing the monitoring of the oxygenation of a patient's hemoglobin. A sensor is placed on a thin part of the patient's anatomy, usually a fingertip or earlobe, or in the case of a neonate, across a foot, and a light containing both red and infrared wavelengths is passed from one side to the other. Changing absorbance of each of the two wavelengths is measured, allowing determination of the absorbances due to the pulsing arteria blood alone, excluding venous blood, skin, bone, muscle, fat, and (in most cases) fingernail polish. Based upon the ratio of changing absorbance of the red and infrared light caused by the difference in color between oxygen-bound (bright red) and oxygen unbound (dark red or blue, in severe cases) blood hemoglobin, a measure of oxygenation (the per cent of hemoglobin molecules bound with oxygen molecules) can be made.
Pulse oximeters intended for long-term monitoring of people living an everyday life with a chronic condition offer several challenges: 1) Design and construction of small, discrete, and low-power pulse oximeter devices. 2) Digital signal processing of photoplethysmograms (PPG) which are affected by the patient's motions, a less optimal monitoring site for the purpose of discreteness and long-term compatibility, and battery powered Light Emitting Diodes (LEDs). 3) Integration of these new technologies which typically features telehealth solutions into the established health care system.
An Electronic Patch with integrated reflective pulse oximetry based on a
novel ring-shaped optical sensor has been designed and developed.
Estimating the SpO2 from PPG data which are heavily distorted by
noise and motion artefacts can be done by advanced digital signal
processing. The commercial state-of-the-art, Discrete Saturation
Transform (DST) by Masimo Corporation, is often used in clinical pulse oximeters. To benefit
most from the above mentioned category of low-power pulse oximeters
algorithms which are better at noise and motion filtering are needed.
Signal processing of PPG data using independent component analysis (ICA) has been reported by several authors as a promising technique for motion artefact reduction. Most authors focus on the FastICA algorithm but there are several other ICA algorithms such as Maximum Likelihood ICA (ICAML), Molgedey and Schouster ICA (ICAMS) and Mean Field ICA (ICAMF) all available from the ICA:DTU Toolbox. The various ICA algorithms are based on different assumptions regarding the statistical properties of the source signals and works by optimizing different parameters such as autocorrelation and probability distributions in the attempt to separate sources and noise signals.