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Oñate W, Ramos-Zurita E, Pallo JP, Manzano S, Ayala P, Garcia MV. NIR-Based Electronic Platform for Glucose Monitoring for the Prevention and Control of Diabetes Mellitus. SENSORS (BASEL, SWITZERLAND) 2024; 24:4190. [PMID: 39000969 PMCID: PMC11243983 DOI: 10.3390/s24134190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/19/2024] [Accepted: 06/19/2024] [Indexed: 07/16/2024]
Abstract
The glucose level in the blood is measured through invasive methods, causing discomfort in the patient, loss of sensitivity in the area where the sample is obtained, and healing problems. This article deals with the design, implementation, and evaluation of a device with an ESP-WROOM-32D microcontroller with the application of near-infrared photospectroscopy technology that uses a diode array that transmits between 830 nm and 940 nm to measure glucose levels in the blood. In addition, the system provides a webpage for the monitoring and control of diabetes mellitus for each patient; the webpage is hosted on a local Linux server with a MySQL database. The tests are conducted on 120 people with an age range of 35 to 85 years; each person undergoes two sample collections with the traditional method and two with the non-invasive method. The developed device complies with the ranges established by the American Diabetes Association: presenting a measurement error margin of close to 3% in relation to traditional blood glucose measurement devices. The purpose of the study is to design and evaluate a device that uses non-invasive technology to measure blood glucose levels. This involves constructing a non-invasive glucometer prototype that is then evaluated in a group of participants with diabetes.
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Affiliation(s)
- William Oñate
- Carrera de Electrónica y Automatización, Universidad Politecnica Salesiana (UPS), Quito 170146, Ecuador
| | - Edwin Ramos-Zurita
- Faculty of Systems, Electronics and Industrial Engineering, Universidad Tecnica de Ambato (UTA), Ambato 180206, Ecuador
| | - Juan-Pablo Pallo
- Faculty of Systems, Electronics and Industrial Engineering, Universidad Tecnica de Ambato (UTA), Ambato 180206, Ecuador
| | - Santiago Manzano
- Faculty of Systems, Electronics and Industrial Engineering, Universidad Tecnica de Ambato (UTA), Ambato 180206, Ecuador
| | - Paulina Ayala
- Faculty of Systems, Electronics and Industrial Engineering, Universidad Tecnica de Ambato (UTA), Ambato 180206, Ecuador
| | - Marcelo V Garcia
- Faculty of Systems, Electronics and Industrial Engineering, Universidad Tecnica de Ambato (UTA), Ambato 180206, Ecuador
- Departamento de Ingeniería de Sistemas y Automática, University of the Basque Country, Euskal Herriko Unibertsitatea/Universidad del País Vasco, 48013 Bilbao, Spain
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2
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Mohan N, Awwad F, Albastaki N, Atef M. A Low-Power High-Sensitivity Photocurrent Sensory Circuit with Capacitive Feedback Transimpedance for Photoplethysmography Sensing. SENSORS (BASEL, SWITZERLAND) 2024; 24:4097. [PMID: 39000876 PMCID: PMC11243996 DOI: 10.3390/s24134097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/12/2024] [Accepted: 06/20/2024] [Indexed: 07/16/2024]
Abstract
This study presents an integrated analog front-end (AFE) tailored for photoplethysmography (PPG) sensing. The AFE module introduces a novel transimpedance amplifier (TIA) that incorporates capacitive feedback techniques alongside common drain feedback (CDF) TIA. The unique TIA topology achieves both high gain and high sensitivity while maintaining low power consumption. The resultant PPG sensor module demonstrates impressive specifications, including an input noise current of 4.81 pA/sqrt Hz, a transimpedance gain of 18.43 MΩ, and a power consumption of 68 µW. Furthermore, the sensory system integrates an LED driver featuring automatic light control (ALC), which dynamically adjusts the LED power based on the strength of the received signal. Employing 0.35 µm CMOS technology, the AFE implementation occupies a compact footprint of 1.98 mm × 2.475 mm.
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Affiliation(s)
- Neethu Mohan
- Electrical and Communication Engineering Department, United Arab Emirates University, Al Ain 15551, United Arab Emirates
| | - Falah Awwad
- Electrical and Communication Engineering Department, United Arab Emirates University, Al Ain 15551, United Arab Emirates
| | - Nabil Albastaki
- Electrical and Communication Engineering Department, United Arab Emirates University, Al Ain 15551, United Arab Emirates
| | - Mohamed Atef
- Electrical and Communication Engineering Department, United Arab Emirates University, Al Ain 15551, United Arab Emirates
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Khadem H, Nemat H, Elliott J, Benaissa M. In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics. Heliyon 2024; 10:e30981. [PMID: 38778952 PMCID: PMC11108977 DOI: 10.1016/j.heliyon.2024.e30981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay. Our objective was to evaluate the effectiveness of these techniques in accurately predicting glucose levels and to determine which approach was most optimal. Our investigation involved the acquisition of spectral data from samples of glucose solutions using the three aforementioned spectroscopy techniques. The data was subjected to several preprocessing filtering methods, including convolutional moving average, Savitzky-Golay, multiplicative scatter correction, and normalisation. We then applied representative machine learning algorithms from three categories: linear modelling, traditional nonlinear modelling, and artificial neural networks. The evaluation results revealed that linear models exhibited higher predictive accuracy than nonlinear models, whereas artificial neural network models demonstrated comparable performance. Additionally, the comparative analysis of various filtering methods demonstrated that the convolutional moving average and Savitzky-Golay filters yielded the most precise outcomes overall. In conclusion, our study provides valuable insights into the efficacy of different machine learning techniques for glucose measurement and highlights the importance of applying appropriate filtering methods in enhancing predictive accuracy. These findings have important implications for the development of new and improved glucose quantification technologies.
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Affiliation(s)
- Heydar Khadem
- Department of Electronic and Electrical Engineering, University of Sheffield, UK
- Department of Computer Science, University of Manchester, Manchester, UK
- Artificial Intelligence & Machine Learning Team, KultraLab, London, UK
| | - Hoda Nemat
- Department of Electronic and Electrical Engineering, University of Sheffield, UK
| | - Jackie Elliott
- Department of Oncology and Metabolism, University of Sheffield, UK
- Sheffield Teaching Hospitals, Diabetes and Endocrine Centre, Northern General Hospital, Sheffield, UK
| | - Mohammed Benaissa
- Department of Electronic and Electrical Engineering, University of Sheffield, UK
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Alam I, Dunde A, Balapala KR, Gangopadhyay M, Dewanjee S, Mukherjee M. Design and Development of a Non-invasive Opto-Electronic Sensor for Blood Glucose Monitoring Using a Visible Light Source. Cureus 2024; 16:e60745. [PMID: 38903374 PMCID: PMC11188021 DOI: 10.7759/cureus.60745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 05/20/2024] [Indexed: 06/22/2024] Open
Abstract
Background The management of diabetes is critically dependent on the continuous monitoring of blood glucose levels. Contemporary approaches primarily utilize invasive methods, which often prove to be uncomfortable and can deter patient adherence. There is a pressing need for the development of novel strategies that improve patient compliance and simplify the process of glucose monitoring. Aim and objectives The primary objective of this research is to develop a non-invasive blood glucose monitoring system (NIBGMS) that offers a convenient alternative to conventional invasive methods. This study aims to demonstrate the feasibility and accuracy of using visible laser light at a wavelength of 650 nm for glucose monitoring and to address physiological and technical challenges associated with in vivo measurements. Methods Our approach involved the design of a device that exploits the quantitative relationship between glucose concentration and the refraction phenomena of laser light. The system was initially calibrated and tested using glucose solutions across a range of concentrations (25-500 mg/dL). To get around the problems that come up when people's skin and bodies are different, we combined an infrared (IR) transmitter (800 nm) and receiver that checks for changes in voltage, which are indicative of glucose levels. Results The prototype device was compared with a commercially available blood glucose monitor (Accu-Chek active machine; Roche Diabetes Care, Inc., Mumbai, India). The results demonstrated an average linearity of 95.7% relative to the Accu-Chek machine, indicating a high level of accuracy in the non-invasive measurement of glucose levels. Conclusions The findings suggest that our NIBGMS holds significant promise for clinical application. It reduces the discomfort associated with blood sampling and provides reliable measurements that are comparable to those of existing invasive methods. The successful development of this device paves the way for further commercial translation and could significantly improve the quality of life for individuals with diabetes, by facilitating easier and more frequent monitoring.
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Affiliation(s)
- Iftekar Alam
- Department of Medical Physics, Adamas University, Kolkata, IND
| | - Anjaneyulu Dunde
- Department of Internal Medicine, Baptist Medical Center South, Montgomery, USA
| | - Kartheek R Balapala
- Department of Physiology, Michael Chilufya Sata School of Medicine, The Copperbelt University, Kitwe, ZMB
| | | | - Saikat Dewanjee
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, IND
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Rajeswari SVKR, Vijayakumar P. Development of sensor system and data analytic framework for non-invasive blood glucose prediction. Sci Rep 2024; 14:9206. [PMID: 38649731 PMCID: PMC11035575 DOI: 10.1038/s41598-024-59744-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 04/15/2024] [Indexed: 04/25/2024] Open
Abstract
Periodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900-1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed 'niGLUC-2.0v'. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland-Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.
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Affiliation(s)
- S V K R Rajeswari
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India
| | - P Vijayakumar
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India.
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Naresh M, Nagaraju VS, Kollem S, Kumar J, Peddakrishna S. Non-invasive glucose prediction and classification using NIR technology with machine learning. Heliyon 2024; 10:e28720. [PMID: 38601525 PMCID: PMC11004754 DOI: 10.1016/j.heliyon.2024.e28720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/12/2024] Open
Abstract
In this paper, a dual wavelength short near-infrared system is described for the detection of glucose levels. The system aims to improve the accuracy of blood glucose detection in a cost-effective and non-invasive way. The accuracy of the method is evaluated using real-time samples collected with the reference finger prick glucose device. A feed forward neural network (FFNN) regression method is employed to predict glucose levels based on the input data obtained from NIR technology. The system calculates glucose evaluation metrics and performs Surveillance error grid (SEG) analysis. The coefficient of determination R 2 and mean absolute error are observed 0.99 and 2.49 mg/dl, respectively. Additionally, the system determines the root mean square error (RMSE) as 3.02 mg/dl. It also shows that the mean absolute percentage error (MAPE) is 1.94% and mean squared error (MSE) is 9.16 ( m g / d l ) 2 for FFNN. The SEG analysis shows that the glucose values measured by the system fall within the clinically acceptable range when compared to the reference method. Finally, the system uses the multi-class classification method of the multilayer perceptron (MLP) and K-nearest neighbors (KNN) classifier to classify glucose levels with an accuracy of 99%.
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Affiliation(s)
- M. Naresh
- School of Electronics Engineering, VIT-AP University, Amaravti, Guntur, 522241, Andhra Pradesh, India
| | - V. Siva Nagaraju
- Department of ECE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, 500043, Telangana, India
| | - Sreedhar Kollem
- Department of ECE, School of Engineering, SR University, Warangal, 506371, Telangana, India
| | - Jayendra Kumar
- School of Electronics Engineering, VIT-AP University, Amaravti, Guntur, 522241, Andhra Pradesh, India
| | - Samineni Peddakrishna
- School of Electronics Engineering, VIT-AP University, Amaravti, Guntur, 522241, Andhra Pradesh, India
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Gao M, Guo D, Wang J, Tan Y, Liu K, Gao L, Zhang Y, Ding Z, Gu Y, Li P. High-accuracy noninvasive continuous glucose monitoring using OCT angiography-purified blood scattering signals in human skin. BIOMEDICAL OPTICS EXPRESS 2024; 15:991-1003. [PMID: 38404306 PMCID: PMC10890863 DOI: 10.1364/boe.506092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/21/2023] [Accepted: 01/15/2024] [Indexed: 02/27/2024]
Abstract
The accuracy of noninvasive continuous glucose monitoring (CGM) through near-infrared scattering is challenged by mixed scattering signals from different compartments, where glucose has a positive correlation with a blood scattering coefficient but a negative correlation with a tissue scattering coefficient. In this study, we developed a high-accuracy noninvasive CGM based on OCT angiography (OCTA)-purified blood scattering signals. The blood optical scattering coefficient (BOC) was initially extracted from the depth attenuation of backscattered light in OCT and then purified by eliminating the scattering signals from the surrounding tissues under the guidance of a 3D OCTA vascular map in human skin. The purified BOC was used to estimate the optical blood glucose concentration (BGC) through a linear calibration. The optical and reference BGC measurements were highly correlated (R = 0.94) without apparent time delay. The mean absolute relative difference was 6.09%. All optical BGC measurements were within the clinically acceptable Zones A + B, with 96.69% falling in Zone A on Parke's error grids. The blood glucose response during OGTT was mapped with a high spatiotemporal resolution of the single vessel and 5 seconds. This noninvasive OCTA-based CGM shows promising accuracy for clinical use. Future research will involve larger sample sizes and diabetic participants to confirm these preliminary findings.
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Affiliation(s)
- Mengqin Gao
- State Key Lab of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Dayou Guo
- State Key Lab of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Jiahao Wang
- State Key Lab of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yizhou Tan
- Department of Laser Medicine, First Medical Center of PLA General Hospital, Beijing 100853, China
| | - Kaiyuan Liu
- State Key Lab of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Lei Gao
- Jiaxing Key Laboratory of Photonic Sensing and Intelligent Imaging, Jiaxing 314000, China
- Intelligent Optics and Photonics Research Center, Jiaxing Research Institute, Zhejiang University, Jiaxing 314000, China
| | - Yulei Zhang
- State Key Lab of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Zhihua Ding
- State Key Lab of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Ying Gu
- Department of Laser Medicine, First Medical Center of PLA General Hospital, Beijing 100853, China
| | - Peng Li
- State Key Lab of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Jiaxing Key Laboratory of Photonic Sensing and Intelligent Imaging, Jiaxing 314000, China
- Intelligent Optics and Photonics Research Center, Jiaxing Research Institute, Zhejiang University, Jiaxing 314000, China
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Di Filippo D, Sunstrum FN, Khan JU, Welsh AW. Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians. SENSORS (BASEL, SWITZERLAND) 2023; 23:9130. [PMID: 38005523 PMCID: PMC10674292 DOI: 10.3390/s23229130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/01/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023]
Abstract
Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.
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Affiliation(s)
- Daria Di Filippo
- Discipline of Women’s Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Frédérique N. Sunstrum
- Product Design, School of Design, Faculty of Design, Architecture and Built Environment, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Jawairia U. Khan
- Institute for Biomedical Materials and Devices, School of Mathematical and Physical Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Alec W. Welsh
- Discipline of Women’s Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia;
- Department of Maternal-Fetal Medicine, Royal Hospital for Women, Randwick, NSW 2031, Australia
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Chu J, Chang YT, Liaw SK, Yang FL. Implicit HbA1c Achieving 87% Accuracy within 90 Days in Non-Invasive Fasting Blood Glucose Measurements Using Photoplethysmography. Bioengineering (Basel) 2023; 10:1207. [PMID: 37892937 PMCID: PMC10604272 DOI: 10.3390/bioengineering10101207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/04/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023] Open
Abstract
To reduce the error induced by overfitting or underfitting in predicting non-invasive fasting blood glucose (NIBG) levels using photoplethysmography (PPG) data alone, we previously demonstrated that incorporating HbA1c led to a notable 10% improvement in NIBG prediction accuracy (the ratio in zone A of Clarke's error grid). However, this enhancement came at the cost of requiring an additional HbA1c measurement, thus being unfriendly to users. In this study, the enhanced HbA1c NIBG deep learning model (blood glucose level predicted from PPG and HbA1c) was trained with 1494 measurements, and we replaced the HbA1c measurement (explicit HbA1c) with "implicit HbA1c" which is reversely derived from pretested PPG and finger-pricked blood glucose levels. The implicit HbA1c is then evaluated across intervals up to 90 days since the pretest, achieving an impressive 87% accuracy, while the remaining 13% falls near the CEG zone A boundary. The implicit HbA1c approach exhibits a remarkable 16% improvement over the explicit HbA1c method by covering personal correction items automatically. This improvement not only refines the accuracy of the model but also enhances the practicality of the previously proposed model that relied on an HbA1c input. The nonparametric Wilcoxon paired test conducted on the percentage error of implicit and explicit HbA1c prediction results reveals a substantial difference, with a p-value of 2.75 × 10-7.
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Affiliation(s)
- Justin Chu
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Taipei City 10607, Taiwan; (J.C.); (S.-K.L.)
- Research Center for Applied Sciences, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang, Taipei City 115-29, Taiwan
| | - Yao-Ting Chang
- Division of Cardiology, Department of Internal Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 289, Jianguo Rd., Xindian Dist., New Taipei City 231-42, Taiwan;
| | - Shien-Kuei Liaw
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Taipei City 10607, Taiwan; (J.C.); (S.-K.L.)
| | - Fu-Liang Yang
- Research Center for Applied Sciences, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang, Taipei City 115-29, Taiwan
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10
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Farias LR, Panero JDS, Riss JSP, Correa APF, Vital MJS, Panero FDS. Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2023; 23:7336. [PMID: 37687792 PMCID: PMC10490430 DOI: 10.3390/s23177336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 09/10/2023]
Abstract
Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.
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Affiliation(s)
- Leovergildo R. Farias
- Instituto Federal de Roraima, Campus Boa Vista, Av. Glaycon de Paiva, 2496 Pricumã, Boa Vista 69303-340, Brazil; (L.R.F.); (J.d.S.P.)
| | - João dos S. Panero
- Instituto Federal de Roraima, Campus Boa Vista, Av. Glaycon de Paiva, 2496 Pricumã, Boa Vista 69303-340, Brazil; (L.R.F.); (J.d.S.P.)
| | - Jordana S. P. Riss
- Instituto Federal de Roraima, Campus Novo Paraíso, BR-174, Km-512—Vila Novo Paraíso, Caracaraí 69365-000, Brazil;
| | - Ana P. F. Correa
- Postgraduate Program in Natural Resources-PRONAT, Universidade Federal de Roraima, Av. Cap. Ene Garcês, 2413-Aeroporto, Boa Vista 69310-000, Brazil; (A.P.F.C.); (M.J.S.V.)
| | - Marcos J. S. Vital
- Postgraduate Program in Natural Resources-PRONAT, Universidade Federal de Roraima, Av. Cap. Ene Garcês, 2413-Aeroporto, Boa Vista 69310-000, Brazil; (A.P.F.C.); (M.J.S.V.)
| | - Francisco dos S. Panero
- Postgraduate Program in Natural Resources-PRONAT, Universidade Federal de Roraima, Av. Cap. Ene Garcês, 2413-Aeroporto, Boa Vista 69310-000, Brazil; (A.P.F.C.); (M.J.S.V.)
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11
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Kaysir MR, Song J, Rassel S, Aloraynan A, Ban D. Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection. BIOSENSORS 2023; 13:716. [PMID: 37504114 PMCID: PMC10377086 DOI: 10.3390/bios13070716] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 06/26/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
The prevalence of diabetes is rapidly increasing worldwide and can lead to a range of severe health complications that have the potential to be life-threatening. Patients need to monitor and control blood glucose levels as it has no cure. The development of non-invasive techniques for the measurement of blood glucose based on photoacoustic spectroscopy (PAS) has advanced tremendously in the last couple of years. Among them, PAS in the mid-infrared (MIR) region shows great promise as it shows the distinct fingerprint region for glucose. However, two problems are generally encountered when it is applied to monitor real samples for in vivo measurements in this MIR spectral range: (i) low penetration depth of MIR light into the human skin, and (ii) the effect of other interfering components in blood, which affects the selectivity of the detection system. This review paper systematically describes the basics of PAS in the MIR region, along with recent developments, technical challenges, and data analysis strategies, and proposes improvements for the detection sensitivity of glucose concentration in human bodies. It also highlights the recent trends of incorporating machine learning (ML) to enhance the detection sensitivity of the overall system. With further optimization of the experimental setup and incorporation of ML, this PAS in the MIR spectral region could be a viable solution for the non-invasive measurement of blood glucose in the near future.
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Affiliation(s)
- Md Rejvi Kaysir
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
- Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
- Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
| | - Jiaqi Song
- Department of Physics and Astronomy, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
| | - Shazzad Rassel
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
- Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
| | - Abdulrahman Aloraynan
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
- Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
| | - Dayan Ban
- Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
- Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
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Azuma L, Natsuaki R, Hirose A. Skin-Penetrating Interfaces for Millimeter-Wave Non-Invasive Glucose Concentration Estimation: Numerical Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083378 DOI: 10.1109/embc40787.2023.10341131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Millimeter-wave (MMW) glucose concentration estimation possesses a great advantage of non-invasiveness. The long history of investigation, however, has not yet reached practical applications because of its insufficient accuracy and stability. To solve these problems, this paper proposes two high skin-penetration interfaces, which we name equivalent quarter-wavelength interface and equivalent Brewster's-angle interface. We analyze their scattering characteristics in a frequency range of 60 - 90 GHz. Analysis results show that both the interfaces suppress the body-surface scattering, allowing the MMWs to penetrate through body surface into tissues to extract information on blood-glucose concentration with higher sensitivity, e.g., with 147-times enhancement of phase changes. These interfaces can be an important step toward realizing non-invasive blood glucose concentration estimation.
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13
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Tripathy HP, Pattanaik P, Mishra DK, Kamilla SK, Holderbaum W. Experimental and probabilistic model validation of ultrasonic MEMS transceiver for blood glucose sensing. Sci Rep 2022; 12:21259. [PMID: 36481774 PMCID: PMC9732296 DOI: 10.1038/s41598-022-25717-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
In contrast to traditional laboratory glucose monitoring, recent developments have focused on blood glucose self-monitoring and providing patients with a self-monitoring device. This paper proposes a system based on ultrasound principles for quantifying glucose levels in blood by conducting an in-vitro experiment with goat blood before human blood. The ultrasonic transceiver is powered by a frequency generator that operates at 40 kHz and 1.6 V, and variations in glucose level affect the ultrasonic transceiver readings. The RVM probabilistic model is used to determine the variation in glucose levels in a blood sample. Blood glucose levels are measured simultaneously using a commercial glucose metre for confirmation. The experimental data values proposed are highly correlated with commercial glucose metre readings. The proposed ultrasonic MEMS-based blood glucometer measures a glucose level of [Formula: see text] mg/dl. In the near future, the miniature version of the experimental model may be useful to human society.
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Affiliation(s)
- Hara Prasada Tripathy
- grid.412612.20000 0004 1760 9349Semiconductor Research Laboratory, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030 India
| | - Priyabrata Pattanaik
- grid.412612.20000 0004 1760 9349Semiconductor Research Laboratory, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030 India
| | - Dilip Kumar Mishra
- grid.412612.20000 0004 1760 9349Semiconductor Research Laboratory, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030 India
| | - Sushanta Kumar Kamilla
- grid.412612.20000 0004 1760 9349Semiconductor Research Laboratory, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030 India
| | - William Holderbaum
- grid.9435.b0000 0004 0457 9566School of Biological Science, Biomedical Engineering, University of Reading, Whiteknights, RG6 6AY UK
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14
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Salem M, Elkaseer A, El-Maddah IAM, Youssef KY, Scholz SG, Mohamed HK. Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176625. [PMID: 36081081 PMCID: PMC9460364 DOI: 10.3390/s22176625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/22/2022] [Accepted: 08/30/2022] [Indexed: 05/06/2023]
Abstract
The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.
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Affiliation(s)
- Mahmoud Salem
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Correspondence: ; Tel.: +49-0-721-608-25632
| | - Ahmed Elkaseer
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Faculty of Engineering, Port Said University, Port Said 42526, Egypt
| | | | - Khaled Y. Youssef
- Faculty of Navigation Science and Space Technology, Beni-Suef University, Beni-Suef 2731070, Egypt
| | - Steffen G. Scholz
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- Karlsruhe Nano Micro Facility, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
- College of Engineering, Swansea University, Swansea SA2 8PP, UK
| | - Hoda K. Mohamed
- Faculty of Engineering, Ain Shams University, Cairo 11535, Egypt
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