KEYWORDS: Signal to noise ratio, Blood, Absorption, Signal detection, Spectroscopy, Near infrared, In vivo imaging, Mass attenuation coefficient, Absorbance, Tissues
The blood hemoglobin concentration’s (BHC) measurement using Photoplethysmography (PPG), which gets blood absorption to near infrared light from the instantaneous pulse of transmitted light intensity, has not been applied to the clinical use due to the non-enough precision. The main challenge might be caused of the non-enough stable pulse signal when it’s very weak and it often varies in different human bodies or in the same body with different physiological states. We evaluated the detection limit of BHC using PPG as the measurement precision level, which can be considered as a best precision result because we got the relative stable subject’s pulse signals recorded by using a spectrometer with high signal-to-noise ratio (SNR) level, which is about 30000:1 in short term. Moreover, we optimized the used pathlength using the theory based on optimum pathlength to get a better sensitivity to the absorption variation in blood. The best detection limit was evaluated as about 1 g/L for BHC, and the best SNR of pulse for in vivo measurement was about 2000:1 at 1130 and 1250 nm. Meanwhile, we conclude that the SNR of pulse signal should be better than 400:1 when the required detection limit is set to 5 g/L. Our result would be a good reference to the BHC measurement to get a desired BHC measurement precision of real application.
In the non-invasive blood glucose concentration (BGC) sensing, the measurement based on near infrared spectroscopy has been a promising technology since it had acquired dozens of satisfactory results in short-term glucose monitoring tests. However, it’s still necessary to improve the measurement precision because it has challenges of the reduced precision in a long-term test when a lot of variables in the test would exist. Considering the requirement of multivariable analysis, the signals of diffuse reflectance spectra should include enough absorption information from glucose. However, the sensitivity of diffuse light intensity to the absorption variation at different source detector separations (SDSs) could be different. We present an analysis method using Monte-Carlo (MC) simulation and the diffuse equation for reasonably selecting proper SDS to get a satisfactory glucose measurement precision when there are multivariable disturbances. In the case of measuring glucose in a tissue phantom using the waveband of 1000-1340 nm, we show the SDS optimization result by using this analysis method. The experiment was designed to measure the diffuse reflectance spectra at 0.1-3.0 mm with the step of 0.1 mm, and the phantom solutions with different glucose concentrations and hemoglobin concentrations are tested. The glucose prediction precision was evaluated using the root mean squared error of prediction (RMSEP) for the all SDSs of 0.1-3.0 mm, and the SDSs with the lower RMSEP were selected for use. Moreover, the selected SDSs in the experiment shows a similar conclusion from the MC simulation. This work could be referenced to the in vivo BGC measurement.
KEYWORDS: Blood, Glucose, Near infrared spectroscopy, Tissues, Signal to noise ratio, Near infrared, Interference (communication), Signal detection, Absorbance, Absorption
In the non-invasive blood components measurement using near infrared spectroscopy, the useful signals caused by the concentration variation in the interested components, such as glucose, hemoglobin, albumin etc., are relative weak. Then the signals may be greatly disturbed by a lot of noises in various ways. We improved the signals by using the optimum path-length for the used wavelength to get a maximum variation of transmitted light intensity when the concentration of a component varies. And after the path-length optimization for every wavelength in 1000-2500 nm, we present the detection limits for the components, including glucose, hemoglobin and albumin, when measuring them in a tissue phantom. The evaluated detection limits could be the best reachable precision level since it assumed the measurement uses a high signal-to-noise ratio (SNR) signal and the optimum path-length. From the results, available wavelengths in 1000-2500 nm for the three component measurements can be screened by comparing their detection limit values with their measurement limit requirements. For other blood components measurement, the evaluation their detection limits could also be designed using the method proposed in this paper. Moreover, we use an equation to estimate the absorbance at the optimum path-length for every wavelength in 1000-2500 nm caused by the three components. It could be an easy way to realize the evaluation because adjusting the sample cell’s size to the precise path-length value for every wavelength is not necessary. This equation could also be referred to other blood components measurement using the optimum path-length for every used wavelength.
The measurement accuracy of non-invasive blood glucose concentration (BGC) sensing with near-infrared spectroscopy is easily affected by the temperature variation in tissue because it would induce an unacceptable spectrum variation and the consequent prediction deviation. We use a multivariable correction method based on external parameter orthogonalization (EPO) to calibrate the spectral data recorded at different temperature values to reduce the spectral variation. The tested medium is a kind of tissue phantom, the Intralipid aqueous solution. The calibration uses a projection matrix to get the orthogonal spectral space to the variable of external parameter, i.e. temperature, and then the useful spectral information relative to glucose concentration has been reserved. Even more, training the projection matrix can be separated to building the calibration matrix for the prediction of glucose concentration as it only uses the representative samples’ data with temperature variation. The method presents a lower complexity than modeling a robust prediction matrix, which can be built from comprehensive spectral data involved the all variables both of BGC and temperature. In our test, the calibrated spectra with the same glucose concentration but different temperature values show a significantly improved repeatability. And then the glucose concentration prediction results show a lower root mean squared error of prediction (RMSEP) than that using the robust calibration model, which has considered the two variables. We also discuss the rationality of the representative samples chosen by EPO. This research may be referenced to the temperature calibration for in vivo BGC sensing.
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