Non‑Invasive Glucose Diabetic Prediction using Deep Neural Network and PPG Signals
Keywords:
Blood Glucose Diabetic Prediction, photoplethysmography Signal, Deep neural Network, ScalingAbstract
This study introduces a novel approach for predicting blood glucose levels using dual wavelength photoplethysmography (PPG), which offers a non-invasive alternative to the usual invasive methods for blood glucose measurement. This suggested system aims to alleviate the pain associated with traditional techniques while providing accurate blood glucose estimations for the purpose of diabetes prediction. The PPG signals that were obtained were subjected to pre-processing and subsequently underwent min-max scaling. Additionally, feature selection was performed. The use of the Deep Learning neural network (DNN) was utilised to forecast the blood glucose level for the purpose of diabetes prediction. In order to validate the system's operational reliability, this study gathered data from a total of 182 participants and generated a comprehensive database. Based on the empirical findings, the system exhibits a root mean squared error of 5.05 mg/dl surpassing the performance of the Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) regression models employed in this investigation. When compared with recent work, the proposed model in this study observed the RMSE of 5.05 mg/dl in comparison with 9.14 mg/dl in recent study.
Downloads
References
H. D. Nickerson and S. Dutta, “Diabetic complications: current challenges and opportunities,” J. Cardiovasc. Transl. Res., vol. 5, no. 4, pp. 375–379, Aug. 2012, doi: 10.1007/S12265-012-9388-1.
N. A. Binti, A. Salam, W. Hidayat, M. Saad, Z. B. Manap, and F. Bte Salehuddin, “The Evolution of Non-invasive Blood Glucose Monitoring System for Personal Application,” J. Telecommun. Electron. Comput. Eng., vol. 8, no. 1, pp. 59–65, Apr. 2016, Accessed: Nov. 13, 2023. [Online]. Available: https://jtec.utem.edu.my/jtec/article
/view/680
E. Y. Park, J. Baik, H. Kim, S. M. Park, and C. Kim, “Ultrasound-modulated optical glucose sensing using a 1645 nm laser,” Sci. Reports 2020 101, vol. 10, no. 1, pp. 1–9, Aug. 2020, doi: 10.1038/s41598-020-70305-6.
D. Rodbard, “Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities,” Diabetes Technol. Ther., vol. 18 Suppl 2, no. Suppl 2, pp. S23–S213, Feb. 2016, doi: 10.1089/DIA.2015.0417.
W. V. Gonzales, A. T. Mobashsher, and A. Abbosh, “The Progress of Glucose Monitoring-A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors,” Sensors (Basel)., vol. 19, no. 4, Feb. 2019, doi: 10.3390/S19040800.
V. Hartmann, H. Liu, F. Chen, Q. Qiu, S. Hughes, and D. Zheng, “Quantitative Comparison of Photoplethysmographic Waveform Characteristics: Effect of Measurement Site,” Front. Physiol., vol. 10, no. MAR, 2019, doi: 10.3389/FPHYS.2019.00198.
C. T. Yen, U. H. Chen, G. C. Wang, and Z. X. Chen, “Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring,” Sensors 2022, Vol. 22, Page 4452, vol. 22, no. 12, p. 4452, Jun. 2022, doi: 10.3390/S22124452.
S. Deepthi, N. S. Valke, J. P. Parvathy, A. K. Sai, and G. Sajjan, “Non-invasive Blood Glucose Measurement System,” IEEE Int. WIE Conf. Electr. Comput. Eng., Nov. 2019, doi: 10.1109/WIECON-ECE48653.2019.9019919.
T. Hamdi et al., “Artificial neural network for blood glucose level prediction,” 2017 Int. Conf. Smart, Monit. Control. Cities, SM2C 2017, pp. 91–95, Oct. 2017, doi: 10.1109/SM2C.
8071825.
P. Prabhu and S. Selvabharathi, “Deep Belief Neural Network Model for Prediction of Diabetes Mellitus,” 2019 3rd Int. Conf. Imaging, Signal Process. Commun. ICISPC 2019, pp. 138–142, Jul. 2019, doi: 10.1109/ICISPC.2019.8935838.
B. E. Manurung, H. R. Munggaran, G. F. Ramadhan, and A. P. Koesoema, “Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning,” IEEE Reg. 10 Humanit. Technol. Conf. R10-HTC, vol. 2019-November, Nov. 2019, doi: 10.1109/R10-HTC47129.2019.9042479.
A. Hina and W. Saadeh, “A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography,” IEEE Trans. Biomed. Circuits Syst., vol. 14, no. 3, pp. 504–515, Jun. 2020, doi: 10.1109/TBCAS.2020.2979514.
M. M. Fouad, D. Y. Mahmoud, and M. A. Abd El Ghany, “Joint NIR-BIS Based Non-Invasive Glucose Monitoring System,” Int. Congr. Math., vol. 2018-December, pp. 88–91, Jul. 2018, doi: 10.1109/ICM.2018.8704063.
N. D. Nanayakkara, S. C. Munasingha, and G. P. Ruwanpathirana, “Non-invasive blood glucose monitoring using a hybrid technique,” MERCon 2018 - 4th Int. Multidiscip. Moratuwa Eng. Res. Conf., pp. 7–12, Jul. 2018, doi: 10.1109/MERCON.2018.8421885.
K. D. Pathirage, P. Roopasinghe, J. J. Sooriyaarachchi, R. Weththasinghe, and N. D. Nanayakkara, “Removing subject dependencies on Non-Invasive Blood Glucose Measurement using Hybrid Techniques,” Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Int. Conf., vol. 2019, pp. 7197–7200, Jul. 2019, doi: 10.1109/EMBC.
8856391.
S. Maqsood, S. Xu, M. Springer, and R. Mohawesh, “A Benchmark Study of Machine Learning for Analysis of Signal Feature Extraction Techniques for Blood Pressure Estimation Using Photoplethysmography (PPG),” IEEE Access, vol. 9, pp. 138817–138833, 2021, doi: 10.1109/ACCESS.2021.3117969.
Y. Liang, M. Elgendi, Z. Chen, and R. Ward, “An optimal filter for short photoplethysmogram signals,” Sci. Data 2018 51, vol. 5, no. 1, pp. 1–12, May 2018, doi: 10.1038/sdata.2018.76.
J. De Moura, J. Novo, and M. Ortega, “Deep Feature Analysis in a Transfer Learning-based Approach for the Automatic Identification of Diabetic Macular Edema,” Proc. Int. Jt. Conf. Neural Networks, vol. 2019-July, Jul. 2019, doi: 10.1109/IJCNN.2019.8852196.
A. M. Joshi, P. Jain, S. P. Mohanty, and N. Agrawal, “IGLU 2.0: A New Wearable for Accurate Non-Invasive Continuous Serum Glucose Measurement in IoMT Framework,” IEEE Trans. Consum. Electron., vol. 66, no. 4, pp. 327–335, Nov. 2020, doi: 10.1109/TCE.2020.3011966.
K. Song, U. Ha, S. Park, J. Bae, and H. J. Yoo, “An Impedance and Multi-Wavelength Near-Infrared Spectroscopy IC for Non-Invasive Blood Glucose Estimation,” IEEE J. Solid-State Circuits, vol. 50, no. 4, pp. 1025–1037, Apr. 2015, doi: 10.1109/JSSC.2014.2384037.
P. Jain, A. M. Joshi, and S. P. Mohanty, “iGLU: An Intelligent Device for Accurate Noninvasive Blood Glucose-Level Monitoring in Smart Healthcare,” IEEE Consum. Electron. Mag., vol. 9, no. 1, pp. 35–42, Jan. 2020, doi: 10.1109/MCE.2019.2940855.
H. Agrawal, P. Jain, and A. M. Joshi, “Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare,” Health Technol. (Berl)., vol. 12, no. 5, pp. 955–970, 2022, doi: 10.1007/s12553-022-00690-7.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
