Non-invasive optical glucose detection faces significant challenges due to the need to identify and extract glucose-induced signals amidst continuous human variations and probing disturbances. To ensure stable near-infrared optical signal acquisition in vivo, we enhanced the design of a wearable detector and introduced strategies to mitigate human-induced variations, aiming to minimize unnecessary fluctuations and interferences. Our custom-designed multi-ring InGaAs detector, combined with a differential method, achieved a high signal-to-noise ratio (SNR) during in vivo data acquisition. The proposed posture-aiming method enabled continuous, high-stability data collection for 1-2 hours in vivo, even with slight human motion. These enhancements enable the direct acquisition of near-infrared optical signals modulated by blood glucose levels in vivo. Results from Monte Carlo (MC) simulations and data collected from fasting subjects validated the detection approaches’ capability for stable spectroscopic detection. We conducted 30 oral glucose tolerance tests (OGTT) involving 28 volunteers. At 1550 nm, we successfully extracted optical signals that were continuously synchronized with blood glucose fluctuations, achieving an average coefficient of determination (R2) of 0.82 across the 30 OGTT tests.
Skin temperature fluctuations cannot be ignored in non-invasive human blood composition measurement based on near-infrared spectroscopy. To effectively address and correct for the interference caused by skin temperature, it is essential to understand the spectral characteristics induced by changes in skin temperature. In this study, we simulated the three-dimensional temperature distribution of a five-layered skin model. The goal was to examine how the skin temperature varies with changes in the environmental temperature and the body's core temperature. Additionally, Monte-Carlo simulation was used to obtain the diffuse reflectance spectra. The corresponding spectral characteristics were analyzed. This study can be useful for temperature calibration in non-invasive blood composition measurement in humans.
SignificanceNear-infrared (NIR) diffuse reflectance spectroscopy has been widely used for non-invasive glucose measurement in humans, as glucose can induce a significant and detectable optical signal change in tissue. However, the scattering-dominated glucose spectrum in the range of 1000 to 1700 nm is easily confused with many other scattering factors, such as particle density, particle size, and tissue refractive index.AimOur aim is to identify the subtle distinctions between glucose and these factors through theoretical analysis and experimental verification, in order to employ suitable methods for eliminating these interferences, thus increasing the accuracy of non-invasive glucose measurement.ApproachWe present a theoretical analysis of the spectra of 1000 to 1700 nm for glucose and some scattering factors, which is then verified by an experiment on a 3% Intralipid solution.ResultsWe found that both the theoretical and experimental results show that the effective attenuation coefficient of glucose has distinct spectral characteristics, which are distinct from the spectra caused by particle density and refractive index, particularly in the range of 1400 to 1700 nm.ConclusionsOur findings can offer a theoretical foundation for eliminating these interferences in non-invasive glucose measurement, aiding mathematical methods to model appropriately and enhance the accuracy of glucose prediction.
KEYWORDS: Absorption, Tissues, Scattering, Signal attenuation, Temperature metrology, Skin, In vivo imaging, Monte Carlo methods, Optical fibers, Tissue optics
We present a scattering-independent measurement to monitor the pure near-infrared light absorption variation for scattering media, especially for in vivo tissue. We found a scattering variation independent source-detector separation (SVI-SDS), where the diffuse light intensity only varies with tissue absorption change but does not vary with scattering change. We applied the SVI-SDS setup to monitor the tissue spectra with a temperature modulation. We also proposed a method to simplify the measurement by using two fixed SDSs for all wavelengths. It makes the detection device easy to design and fit to the required SVI-SDSs. Monte Carlo simulation and experiments on intralipid solutions and in vitro pig skin samples are performed to test the method. The temperature absorption spectra were acquired, and the temperature insensitive wavelengths of the tissue are discussed. We believe this new method will guide many potential applications for the absorption-based tissue spectroscopy.
KEYWORDS: Glucose, Absorption, Skin, Signal attenuation, Blood, Scattering, Monte Carlo methods, Light scattering, Near infrared spectroscopy, Diffusion
In the non-invasive blood glucose sensing based on near-infrared spectroscopy, the skin scattering variation during the long-term blood glucose monitoring would be a big challenge to get an accurate measurement result. We present a scattering and absorption separating method for the near-infrared diffuse reflectance spectra of scattering media. And we use the extracted absorption part, which is the medium’s effective attenuation coefficient (EAC) spectrum, to improve the accuracy for long-term glucose monitoring. We optimized the light source-detector separation (SDS) setup to realize the maximum sensitivity for the EAC spectra-based measurement. The measurement uses two SDSs to perform a differential on their diffuse reflectance spectra, as the differential could help to reduce the light drift during the long-term in vivo tissue monitoring. The SDS setup optimization for the two positions was tested by the Monte-Carlo (MC) simulation of tissue. The human oral glucose tolerance test (OGTT) with the optimized SDSs also shows a satisfactory blood glucose prediction result. In conclusion, this method shows a good application potential in the non-invasive blood glucose sensing.
In the non-invasive blood glucose measurement based on near-infrared spectroscopy, the glucose signal is very weak and easy to be disturbed. Tissue temperature fluctuation is a primary disturbance source, since it would greatly affect the accuracy of blood glucose concentration results. We present a method called differential diffuse reflection spectroscopy, which makes a differential processing on the data from multiple source-detector distances (SDDs), and it can directly estimate the change in effective attenuation coefficient (EAC) of tissue. Using EAC spectra, we investigated the influence of temperature on the tissue spectra and then used a multivariable analysis of external parameters orthogonalization (EPO) to calibrate the spectra. The spectra of 1000-1800 nm caused by temperature and glucose are compared. Theoretical computing, Monte Carlo simulations and experiments were used to test this method. In conclusion, this proposed method using EAC spectrum to monitor the tissue change shows a promising application potential in non-invasive blood glucose measurement.
We present a floating reference position (FRP)-based drift correction method for near-infrared (NIR) spectroscopy-based long-term blood glucose concentration (BGC) monitoring. Previously, we reported that it is difficult to quantify the systematic drift caused by the fluctuation of incident light intensity at different source–detector (SD) separations based on the absolute FRP change. We use the relative FRP change as a baseline reference to quantitatively characterize the signal drift at different SD separations. For the wavelengths that were used, a uniform equation was developed to describe the relationship between the drift and the relative FRP change. With the help of this equation, the correction can easily be performed by subtracting the systematic drift estimated by the equation. A theoretical analysis and an experimental phantom study demonstrated that our method could be used for systematic drift correction in NIR spectroscopy for long-term BGC monitoring. Moreover, the analysis method can also be referenced to reduce drifts from multiple sources.
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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