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Analysis of the Relationship Between Electrodermal Activity and Blood Glucose Level in Diabetics

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Bioinformatics and Biomedical Engineering (IWBBIO 2024)

Abstract

Diabetes is a chronic disease associated with impaired regulation of blood glucose level (BGL). A drop in BGL below the critical level of 70 mg/dl indicates hypoglycemia, and above 200 mg/dl is a sign of hyperglycemia. Many attempts have been made to noninvasively determine BGL based on signals measured by wearable devices. One of them is electrodermal activity (EDA). The aim of this study was to investigate which of EDA features recently used in the context of BGL fluctuations, change statistically significantly in the states of hypo- (H) and hyperglycemia (P) compared to normal BGL (N). To this end, data including BGL and EDA from 12 diabetics were collected for a few weeks and analyzed. These signals were synchronized and divided into episodes representing the N, H and P states. All episodes were separated into tonic (SCL) and phasic (SCR) components using discrete wavelet transform, and then their power and statistical features were determined. Finally, the Mann-Whitney U-tests were performed to check whether these features change significantly in N vs. H and N vs. P states. Generally, the connections between EDA and BGL in the examined subjects were not entirely consistent. Only some of the features proved to be sensitive to hyperglycemia. These are: power of the mean-free SCL component normalized by the power of EDA, average frequency of SCR arousals, and the standard deviation of the SCL. None of the analyzed features turned out to be significant enough in distinguishing hypoglycemia.

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References

  1. WHO. Diabetes. https://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed 09 Feb 2024

  2. Das, S.K., Nayak, K.K., Krishnaswamy, P.R., Kumar, V., Bhat, N.: Electrochemistry and other emerging technologies for continuous glucose monitoring devices. ECS Sens. Plus 1, 031601 (2022)

    Article  Google Scholar 

  3. Selvin, E., Juraschek, S.P.: Diabetes epidemiology in the COVID-19 pandemic. Diab. Care 43(8), 1690–1694 (2020)

    Article  Google Scholar 

  4. Calbimonte, J.P., Ranvier, J.E., Dubosson, F., Aberer, K.: Semantic representation and processing of hypoglycemic events derived from wearable sensor data. J. Amb. Intel. Smart En. 9(1), 97–109 (2017)

    Google Scholar 

  5. Islam, M.M., Manjur, S.M.: Design and implementation of a wearable system for non-invasive glucose level monitoring. In: IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), pp. 29–32. IEEE (2019)

    Google Scholar 

  6. Mahmud, T., et al.: Non-invasive blood glucose estimation using multi-sensor based portable and wearable system. In: IEEE Global Humanitarian Technology Conference (GHTC), pp. 1–5. IEEE (2019)

    Google Scholar 

  7. Yin, H., Mukadam, B., Dai, X., Jha, N.K.: DiabDeep: pervasive diabetes diagnosis based on wearable medical sensors and efficient neural networks. IEEE Trans. Emerg. Topics Comput 9(3), 1139–1150 (2019)

    Article  Google Scholar 

  8. Bent, B., et al.: Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches. NPJ Digital Med. 4(1), 89 (2021)

    Article  MathSciNet  Google Scholar 

  9. Bent, B., et al.: Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diab. Res. Care 9(1), e002027 (2021)

    Article  MathSciNet  Google Scholar 

  10. Lehmann, V., et al.: Noninvasive hypoglycemia detection in people with diabetes using smartwatch data. Diab. Care 46(5), 993–997 (2023)

    Article  Google Scholar 

  11. Snekhalatha, U., Rajalakshmi, T., Vinitha Sri, C.H., Balachander, G., Shankar, K.S.: Non-invasive blood glucose analysis based on galvanic skin response for diabetic patients. Biomed. Eng. - Appl. Basis Commun. 30(02), 1850009 (2018)

    Article  Google Scholar 

  12. Saad, W.H.M., et al.: Analysis on continuous wearable device for blood glucose detection using GSR sensor. Int. J. Nanoelectron. Mater. 13(8), 9–16 (2020)

    Google Scholar 

  13. Donelli, M., Espa, G., Feraco, P., Manekiya, M.: Wearable non-invasive blood glucose monitor system based on galvanic skin resistance measurement. Electron. Lett. 57(24), 901–902 (2021)

    Article  Google Scholar 

  14. Kumar, J.K.J., Kaythry, P., Santhosh, S., Sheeba, M.: IoT based non-invasive blood glucose measurement using galvinic skin response sensor. J. Next Gener. Inf. Technol. 3(1), 23–30 (2023)

    Google Scholar 

  15. Roy, J. C., Boucsein, W., Fowles, D. C., Gruzelier, J.: Progress in electrodermal research. Vol. 249. Springer Science & Business Media (2012). https://doi.org/10.1007/978-1-4615-2864-7

  16. Pagiatakis, C., Rivest-Hénault, D., Roy, D., Thibault, F., Jiang, D.: Intelligent interaction interface for medical emergencies: application to mobile hypoglycemia management. Smart Health 15, 100091 (2020)

    Article  Google Scholar 

  17. Zahed, K., Sasangohar, F., Mehta, R., Erraguntla, M., Qaraqe, K.: Diabetes management experience and the state of hypoglycemia: national online survey study. JMIR Diab. 5(2), e17890 (2020)

    Article  Google Scholar 

  18. Sharma, M., Kacker, S., Sharma, M.: A brief introduction and review on galvanic skin response. Int. J. Med. Res. Prof 2, 13–17 (2016)

    Google Scholar 

  19. Greco, A., Valenza, G., Lanata, A., Scilingo, E.P., Citi, L.: CvxEDA: a convex optimization approach to electrodermal activity processing. IEEE Trans. Biomed. Eng. 63(4), 797–804 (2016)

    Google Scholar 

  20. Hernando-Gallego, F., Luengo, D., Artés-Rodríguez, A.: Feature extraction of galvanic skin responses by nonnegative sparse deconvolution. IEEE J. Biomed. Health Inform. 22(5), 1385–1394 (2018)

    Article  Google Scholar 

  21. Posada-Quintero, H.F., Florian, J.P., Orjuela-Cañón, A.D., Aljama-Corrales, T., Charleston-Villalobos, S., Chon, K.H.: Power spectral density analysis of electrodermal activity for sympathetic function assessment. Ann. Biomed. Eng. 44, 3124–3135 (2016)

    Article  Google Scholar 

  22. Posada-Quintero, H.F., et al.: Time-varying analysis of electrodermal activity during exercise. PLoS ONE 13(6), e0198328 (2018)

    Article  Google Scholar 

  23. Vijendra, V., Kulkarni, M.: ECG signal filtering using DWT haar wavelets coefficient techniques. In: International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), pp. 1–6. IEEE (2016)

    Google Scholar 

  24. Polak, A.G., Klich, B., Saganowski, S., Prucnal, M.A., Kazienko, P.: Processing photoplethysmograms recorded by smartwatches to improve the quality of derived pulse rate variability. Sensors 22(18), 7047 (2022)

    Article  Google Scholar 

  25. Hossain, M.B., Posada-Quintero, H.F., Kong, Y., McNaboe, R., Chon, K.H.: Automatic motion artifact detection in electrodermal activity data using machine learning. Biomed. Signal Process. Control 74, 103483 (2022)

    Article  Google Scholar 

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Correspondence to Adam G. Polak .

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Polak, A.G., Prucnal, M.A., Adamczyk, K. (2024). Analysis of the Relationship Between Electrodermal Activity and Blood Glucose Level in Diabetics. In: Rojas, I., Ortuño, F., Rojas, F., Herrera, L.J., Valenzuela, O. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2024. Lecture Notes in Computer Science(), vol 14848. Springer, Cham. https://doi.org/10.1007/978-3-031-64629-4_21

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  • DOI: https://doi.org/10.1007/978-3-031-64629-4_21

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  • Online ISBN: 978-3-031-64629-4

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