Compact Instrumentation for Accurate Detection and Measurement of Glucose Concentration Using Photoacoustic Spectroscopy

Shaikh, Faheem; Haworth, Noah; Wells, Riley; Bishop, Jodi; Chatterjee, Shre Kumar; Banerjee, Sankha; Laha, Soumyasanta

doi:10.1109/access.2022.3158945

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…The investigated in-vitro instrumentation techniques have a compact form factor, making them a good option for a non-invasive, in-vivo wearable blood glucose monitoring system. But, due to some drawbacks, it could not be possible to consider commercialization [83].…”

Section: Mid Infra Red (Mir)mentioning

confidence: 99%

Non-Invasive Glucose Measurement Technologies: Recent Advancements and Future Challenges

Jain,

Joshi,

Mohanty

et al. 2024

IEEE Access

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Diabetes is a long-term condition in which a person's body cannot break down blood sugar adequately due to a shortage of insulin. The most crucial element of health care is continuously monitoring blood glucose (BG) levels. The main concern of effective glucose monitoring equipment is based on the blood-pricking technique. However, this may not be suggested for frequent glucose measurement. The paper presents various glucose-measuring technologies. The research discusses various non-invasive glucose measurement techniques and their management using advanced medical technologies. The configuration of the precise measuring device is essential to meet the blood glucose monitoring requirements that are not invasive systems. Non-invasive glucose monitoring devices solve the issue of frequently pricking patients for blood samples for clinical tests. For the goal of continuous health monitoring, a Smart Healthcare framework would be built on the Internet-of-Medical-Things (IoMT) and a Healthcare Cyber-Physical System (H-CPS) to estimate blood glucose. The study also discusses a few consumer devices and cutting-edge methods for measuring glucose. The paper also outlines the several difficulties and open challenges with glucose prediction.

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Section: Mid Infra Red (Mir)mentioning

confidence: 99%

Non-Invasive Glucose Measurement Technologies: Recent Advancements and Future Challenges

Jain,

Joshi,

Mohanty

et al. 2024

IEEE Access

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“…Both of these works also use the laser diode for the optical excitation. Very recently, Faheem et al, has implemented a laser based photoacoustic glucose monitoring system using a phase sensitive amplifier in wearable form factor as reported in [ 74 ]. This work replaces the desktop based lock-in amplifier with an integrated modulator/demodulator microchip working as a lock-in-amplifier for practical wearable applications in diabetes management.…”

Section: Photoacoustic Spectroscopymentioning

confidence: 99%

“…All the instruments are portable and can be replicated for wearable in vivo glucose monitoring ( b ) The electronics circuit for the optical stimulation and the phase sensitive detection of the proposed compact instrumentation system using the micrchip AD630 ( c ) Linear regression calculations of the Amplitude of the signal. Reproduced with permission [ 74 ]. Copyright 2022 IEEE under CC BY.…”

Section: Figurementioning

confidence: 99%

A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management

et al. 2022

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The current standard of diabetes management depends upon the invasive blood pricking techniques. In recent times, the availability of minimally invasive continuous glucose monitoring devices have made some improvements in the life of diabetic patients however it has its own limitations which include painful insertion, excessive cost, discomfort and an active risk due to the presence of a foreign body under the skin. Due to all these factors, the non-invasive glucose monitoring has remain a subject of research for the last two decades and multiple techniques of non-invasive glucose monitoring have been proposed. These proposed techniques have the potential to be evolved into a wearable device for non-invasive diabetes management. This paper reviews research advances and major challenges of such techniques or methods in recent years and broadly classifies them into four types based on their detection principles. These four methods are: optical spectroscopy, photoacoustic spectroscopy, electromagnetic sensing and nanomaterial based sensing. The paper primarily focuses on the evolution of non-invasive technology from bench-top equipment to smart wearable devices for personalized non-invasive continuous glucose monitoring in these four methods. With the rapid evolve of wearable technology, all these four methods of non-invasive blood glucose monitoring independently or in combination of two or more have the potential to become a reality in the near future for efficient, affordable, accurate and pain-free diabetes management.

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“…Long [ 10 ] proposed a blood glucose photoacoustic detection algorithm based on Teager-Kaiser main energy to overcome noise and medium interference and achieved a high detection accuracy for a simulated human skin and blood. Shaikh [ 11 ] developed a novel compact and accurate glucose concentration measurement system using the photoacoustic near infrared spectroscopy, and a good correlation between the glucose concentration and the electrical signal was obtained. Chen [ 12 ] proposed a kind of bioprobe evaluation method to obtain a high florescence quantum yield based on measurements of PAS combined with fluorescence.…”

Section: Introductionmentioning

confidence: 99%

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

Xiong,

Ren,

Liu

2024

Biomed. Opt. Express

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In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.

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Compact Instrumentation for Accurate Detection and Measurement of Glucose Concentration Using Photoacoustic Spectroscopy

Cited by 7 publications

References 26 publications

Non-Invasive Glucose Measurement Technologies: Recent Advancements and Future Challenges

Non-Invasive Glucose Measurement Technologies: Recent Advancements and Future Challenges

A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

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