Modelling, verification, and calibration of a photoacoustics based continuous non-invasive blood glucose monitoring system

Non-Destructive Sensor for Glucose Solution Concentration Detection Using Electromagnetic Technology

In this paper, a sensor using a complementary split ring resonator (CSRR) is proposed for non-destructive testing of blood glucose. By depicting the complementary split ring structure on the ground, the electromagnetic field strength between the split rings can be enhanced effectively. The structure size of the sensor by CSRR is determined by simulation, so that the insertion loss curve of the device has a resonance point at the frequency of 3.419 GHz. With a special holder created by three-dimensional (3D) printing technology, the test platform was established when the concentration of the solution varied from 0 mg/mL to 20 mg/mL. The experimental results indicate that there is an obvious linear relationship between the insertion loss S21 and the glucose concentration at the resonant frequency. Similarly, the measured real part and imaginary part of the S21 both vary with glucose concentration linearly. Based on the above experimental results, the feasibility of the sensor using a CSRR proposed in this paper for non-destructive detection of blood glucose is preliminarily verified.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks

A highly accurate classification of diabetes mellitus (DM) with the synthetic impacts of several variables is first studied via optoacoustic technology in this work. For this purpose, an optoacoustic measurement apparatus of blood glucose is built, and the optoacoustic signals and peak-peak values for 625 cases of in vitro rabbit blood are obtained. The results show that although the single impact of five variables are obtained, the precise classification of DM is limited because of the synthetic impacts. Based on clinical standards, different levels of blood glucose corresponding to hypoglycaemia, normal, slight diabetes, moderate diabetes and severe diabetes are employed. Then, a wavelet neural network (WNN) is utilized to establish a classification model of DM severity. The classification accuracy is 94.4 % for the testing blood samples. To enhance the classification accuracy, particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are successively utilized to optimize WNN, and accuracy is enhanced to 98.4 % and 100 %, respectively. It is demonstrated from comparison between several algorithms that optoacoustic technology united with the QPSO-optimized WNN algorithm can achieve precise classification of DM with synthetic impacts.

Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast

In the past decade, photoacoustic (PA) imaging has attracted a great deal of popularity as an emergent diagnostic technology owing to its successful demonstration in both preclinical and clinical arenas by various academic and industrial research groups. Such steady growth of PA imaging can mainly be attributed to its salient features, including being non-ionizing, cost-effective, easily deployable, and having sufficient axial, lateral, and temporal resolutions for resolving various tissue characteristics and assessing the therapeutic efficacy. In addition, PA imaging can easily be integrated with the ultrasound imaging systems, the combination of which confers the ability to co-register and cross-reference various features in the structural, functional, and molecular imaging regimes. PA imaging relies on either an endogenous source of contrast (e.g., hemoglobin) or those of an exogenous nature such as nano-sized tunable optical absorbers or dyes that may boost imaging contrast beyond that provided by the endogenous sources. In this review, we discuss the applications of PA imaging with endogenous contrast as they pertain to clinically relevant niches, including tissue characterization, cancer diagnostics/therapies (termed as theranostics), cardiovascular applications, and surgical applications. We believe that PA imaging's role as a facile indicator of several disease-relevant states will continue to expand and evolve as it is adopted by an increasing number of research laboratories and clinics worldwide.

The sound of blood: photoacoustic imaging in blood analysis

Blood analysis is a ubiquitous and critical aspect of modern medicine. Analyzing blood samples requires invasive techniques, various testing systems, and samples are limited to relatively small volumes. Photoacoustic imaging (PAI) is a novel imaging modality that utilizes non-ionizing energy that shows promise as an alternative to current methods. This paper seeks to review current applications of PAI in blood analysis for clinical use. Furthermore, we discuss obstacles to implementation and future directions to overcome these challenges. Firstly, we discuss three applications to cellular analysis of blood: sickle cell, bacteria, and circulating tumor cell detection. We then discuss applications to the analysis of blood plasma, including glucose detection and anticoagulation quantification. As such, we hope this article will serve as inspiration for PAI's potential application in blood analysis and prompt further studies to ultimately implement PAI into clinical practice.

Observing temporal variation in hemolysis through photoacoustics with a low cost LASER diode based system

Patients under hemolytic condition need continuous monitoring of lysis as depletion of Red Blood Cells (RBC) and the presence of antioxidant free hemoglobin (Hb) in excess amount due to hemolysis lead to severe deterioration of their health. Out of many modalities, Photoacoustics (PA) offers real time information noninvasively from deep lying blood vessels since Hb is the strongest chromophore in mammalian blood and the PA response of blood varies with the amount of Hb present. During hemolysis, total Hb content in blood however remains unchanged, thus, questions the use of PA in hemolysis detection. In this report, a hypothesis that the amplitude of the PA signal would not change with the amount of lysis is framed and tested by applying osmotic shock to the RBCs in hypotonic environment and the PA response is recorded over time using a low cost NIR based PA system. The experimental outcome indicates that PA amplitude falls off as lysis progresses in course of time consequently rejecting the hypothesis. The decaying PA response also carries the signature of RBC swelling during the early phase of lysis. The PA measurement can detect hemolysis as low as 1.7%. These findings further advocate transforming this NIR-PA system into a portable, noninvasive patient care device to monitor hemolysis in-vivo.

Glucose Determination by a Single 1535 nm Pulsed Photoacoustic Technique: A Multiple Calibration for the External Factors

Photoacoustic spectroscopy has been proved to be a potential method for noninvasive blood glucose detection. We used 1535 nm pulsed laser to excite photoacoustic signal in glucose solution and then explored the influence of different glucose concentration on photoacoustic signal to analyze the sensitivity of photoacoustic signal to glucose at this wavelength. We designed a simple photoacoustic cell structure, which used a focused ultrasonic transducer to receive signals, so as to reduce signal attenuation. In terms of the results, we have found that for high-concentration glucose solutions, the results have strong linearity and discrimination, and when the concentration is close to the human body level, the signal difference is not so obvious. Therefore, we explore the external factors affecting the photoacoustic signal in detail and propose a calibration method. Through calibration, the signal generated by the low-concentration glucose solution also has a good linearity.

Performance Enhancement of Opened Resonance Photoacoustic Cells Based on Three Dimensional Topology Optimization

Photoacoustic (PA) spectroscopy techniques enable the detection of trace substances. However, lower threshold detection requirements are increasingly common in practical applications. Thus, we propose a systematic geometry topology optimization approach on a PA cell to enhance the intensity of its detection signal. The model of topology optimization and pressure acoustics in the finite element method was exploited to construct a PA cell and then acquire the optimal structure. In the assessment, a thermo-acoustic model was constructed to properly simulate the frequency response over the range of 0–70 kHz and the temperature field distribution. The simulation results revealed that the acoustic gain of the optimized cell was 2.7 and 1.3 times higher than conventional cells near 25 and 52 kHz, respectively. Moreover, the optimized PA cell achieved a lower threshold detection over a wide frequency range. Ultimately, this study paves a new way for designing and optimizing the geometry of multifarious high-sensitivity PA sensors.

A sensor based intelligent system for classification and assistance of diabetes patients in telemedicine technology

Diabetes is one of the chronic metabolic disorder. Under diabetic condition, blood glucose level should be properly maintained in order to avoid various major diseases. The condition will be worse when it is not controlled at an earlier stage. Even massive heart attack cannot be identified when the patient has been affected by diabetes. Early diagnosis is required for preventing fatal diseases like cardiac problem, asthma, heart attack etc. In the proposed system measurement of glucose level and Prediction/ diagnosis of diabetes is based on the real time low complexity neural network implemented on a wearable device. A larger network is required for the diagnosis which needs to be present far-off in cloud and initiated for diagnosis and classification process of diabetes whenever it is essential. People can be able to manage and monitor the required basic parameters like heart rate, glucose level, lung condition, pressure of blood using the corresponding light weight biosensors in the wearable device designed through telemedicine technology. The quality of the disease diagnosis and Prediction is improved in this way. Using neural network feed forward prediction model in conjugation with back propagation algorithm and given training data, the system predicts whether the patient is prone to diabetes or not. The proposed work was evaluated using physic sensor data from physio net data base and also tested for real time functioning. The Proposed system found to be efficient in accuracy, sensitivity and fast operative.

Acquiring photoacoustic signature of hematocrit variation from plexus layer of in-silico human skin phantom

Optical penetration inside human skin is constrained by the wavelength dependent scattering and absorption losses by tissue microstructure and chromophores. This computational study investigates whether the signature of hematocrit variation from plexus i.e., the first skin layer having very small blood volume percentage distributed in capillary vessels, is retained by the detected photoacoustic response. Thein-silicoskin phantom is irradiated by a light source equivalent to a small footprint and low power (below 5 W) continuous wave LASER diode. As the low fluence can be compensated by exploiting strong absorption by targeted chromophores (hemoglobin molecules), an irradiation of wavelength 405 nm has been used to generate detectable pressure from capillary blood vessels of plexus. Optical energy deposition inside the tissue has been modelled using Monte Carlo technique and the pressure wave is computed using k-wave. It is found that with the increase in hematocrit from 10% to 50%, photoacoustic amplitude monotonically increases and gets almost doubled. The increment is about 30% in the range of hematocrit of physiological interest (from 30% to 50%).The variation follows a quadratic relationship for the entire hematocrit range. This photoacoustic signature of hematocrit variation has further been validated against minimum detectable pressure (800 Pa). This numerical model is expected to be an important basis to realize the idea of low cost small footprintin-vivophotoacoustic hematocrit measurement device.

Adaptive Boosting Based Personalized Glucose Monitoring System (PGMS) for Non-Invasive Blood Glucose Prediction with Improved Accuracy

In this paper, we present an architecture of a personalized glucose monitoring system (PGMS). PGMS consists of both invasive and non-invasive sensors on a single device. Initially, blood glucose is measured invasively and non-invasively, to train the machine learning models. Then, paired data and corresponding errors are divided scientifically into six different clusters based on blood glucose ranges as per the patient’s diabetic conditions. Each cluster is trained to build the unique error prediction model using an adaptive boosting (AdaBoost) algorithm. Later, these error prediction models undergo personalized calibration based on the patient’s characteristics. Once, the errors in predicted non-invasive values are within the acceptable error range, the device gets personalized for a patient to measure the blood glucose non-invasively. We verify PGMS on two different datasets. Performance analysis shows that the mean absolute relative difference (MARD) is reduced exceptionally to 7.3% and 7.1% for predicted values as compared to 25.4% and 18.4% for measured non-invasive glucose values. The Clarke error grid analysis (CEGA) plot for non-invasive predicted values shows 97% data in Zone A and 3% data in Zone B for dataset 1. Moreover, for dataset 2 results echoed with 98% and 2% in Zones A and B, respectively.

Listening to tissues with new light: recent technological advances in photoacoustic imaging

Photoacoustic tomography (PAT), or optoacoustic tomography, has achieved remarkable progress in the past decade, benefiting from the joint developments in optics, acoustics, chemistry, computing and mathematics. Unlike pure optical or ultrasound imaging, PAT can provide unique optical absorption contrast as well as widely scalable spatial resolution, penetration depth and imaging speed. Moreover, PAT has inherent sensitivity to tissue's functional, molecular, and metabolic state. With these merits, PAT has been applied in a wide range of life science disciplines, and has enabled biomedical research unattainable by other imaging methods. This Review article aims at introducing state-of-the-art PAT technologies and their representative applications. The focus is on recent technological breakthroughs in structural, functional, molecular PAT, including super-resolution imaging, real-time small-animal whole-body imaging, and high-sensitivity functional/molecular imaging. We also discuss the remaining challenges in PAT and envisioned opportunities.

In vivo Microscopic Photoacoustic Spectroscopy for Non-Invasive Glucose Monitoring Invulnerable to Skin Secretion Products

Photoacoustic spectroscopy has been shown to be a promising tool for non-invasive blood glucose monitoring. However, the repeatability of such a method is susceptible to changes in skin condition, which is dependent on hand washing and drying due to the high absorption of infrared excitation light to the skin secretion products or water. In this paper, we present a method to meet the challenges of mid-infrared photoacoustic spectroscopy for non-invasive glucose monitoring. By obtaining the microscopic spatial information of skin during the spectroscopy measurement, the skin region where the infrared spectra is insensitive to skin condition can be locally selected, which enables reliable prediction of the blood glucose level from the photoacoustic spectroscopy signals. Our raster-scan imaging showed that the skin condition for in vivo spectroscopic glucose monitoring had significant inhomogeneities and large variability in the probing area where the signal was acquired. However, the selective localization of the probing led to a reduction in the effects of variability due to the skin secretion product. Looking forward, this technology has broader applications not only in continuous glucose monitoring for diabetic patient care, but in forensic science, the diagnosis of malfunctioning sweat pores, and the discrimination of tumors extracted via biopsy.

Extended Near-Infrared Optoacoustic Spectrometry for Sensing Physiological Concentrations of Glucose

Glucose sensing is pursued extensively in biomedical research and clinical practice for assessment of the carbohydrate and fat metabolism as well as in the context of an array of disorders, including diabetes, morbid obesity, and cancer. Currently used methods for real-time glucose measurements are invasive and require access to body fluids, with novel tools and methods for non-invasive sensing of the glucose levels highly desired. In this study, we introduce a near-infrared (NIR) optoacoustic spectrometer for sensing physiological concentrations of glucose within aqueous media and describe the glucose spectra within 850-1,900 nm and various concentration ranges. We apply the ratiometric and dictionary learning methods with a training set of data and validate their utility for glucose concentration measurements with optoacoustics in the probe dataset. We demonstrate the superior signal-to-noise ratio (factor of ~3.9) achieved with dictionary learning over the ratiometric approach across the wide glucose concentration range. Our data show a linear relationship between the optoacoustic signal intensity and physiological glucose concentration, in line with the results of optical spectroscopy. Thus, the feasibility of detecting physiological glucose concentrations using NIR optoacoustic spectroscopy is demonstrated, enabling the sensing glucose with ±10 mg/dl precision.

Synergetic Resonance Matching of a Microphone and a Photoacoustic Cell

We propose an approach to match the resonant characteristics of a photoacoustic cell with that of a microphone in order to enhance the signal-to-noise ratio in the photoacoustic sensor system. The synergetic resonance matching of a photoacoustic cell and a microphone was achieved by observing that photoacoustic cell resonance is merged with microphone resonance, in addition to conducting numerical and analytical simulations. Using this approach, we show that the signal-to-noise ratio was increased 3.5-fold from the optimized to non-optimized cell in the photoacoustic spectroscopy system. The present work is expected to have a broad impact on a number of applications, from improving weak photoacoustic signals in photoacoustic spectroscopy to ameliorating various sensors that use acoustic resonant filters.

Noninvasive photoacoustic measurement of glucose by data fusion

A novel method of noninvasive photoacoustic glucose measurement utilizing the amplitude and phase difference information.

Research on the best measurement situation between optical probe and tissue surfaces in non-invasive detection

Near-infrared spectroscopy is often used for the non-invasive detection of composition in the human body, such as that of blood glucose and haemoglobin, due to its high penetration depth into tissues. Although it is feasible to position the optical probe precisely, contact situation between probe and human tissues is a difficult problem to determine because of physiological tremor and mechanical performance of bio-soft tissue. Here, we proposed a novel estimation method for the situation between the optical probe and tissue surfaces based on the dynamic auto-correlation matrix of two-dimensional correlation spectroscopy (2DCOS) and radar chart. The diffuse reflectance spectra from the left palm of 4 healthy volunteers were collected while the optical probe gradually approached and pressed bio-tissues with a custom-design controlling device. 2DCOS in the wavelength with lower absorption (1000-1400 nm) was calculated under the perturbation of relative-distance and contact pressure between the optical probe and tissue surface. The synchronous 2DCOS showed that the surface reflection and diffuse reflectance were greatly affected by the contact conditions in 1100 nm, 1220 nm, and 1300 nm. Then the dynamic auto-correlation matrix of 2DCOS was established for the adjacent spectra, and the significant difference wavelengths were used to build radar charts to determine the critical contact situation visually. Results showed that the maximum variations of dynamic auto-correlation matrix appeared at near 1300 nm, and the relative distance between the probe and tissue corresponding to the critical contact state can be easily observed with radar charts with 0.25 mm uncertainty, which was consistent with the self-feeling of each volunteer. So this method can be applied to exactly determine the optimal measurement status for the non-invasive body composition detection in vivo. It is important for the design of human-machine interface and the accuracy improvement of body composition measurements.