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
WHO. Diabetes. https://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed 09 Feb 2024
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)
Selvin, E., Juraschek, S.P.: Diabetes epidemiology in the COVID-19 pandemic. Diab. Care 43(8), 1690–1694 (2020)
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)
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)
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)
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)
Bent, B., et al.: Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches. NPJ Digital Med. 4(1), 89 (2021)
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)
Lehmann, V., et al.: Noninvasive hypoglycemia detection in people with diabetes using smartwatch data. Diab. Care 46(5), 993–997 (2023)
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)
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)
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)
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)
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
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)
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)
Sharma, M., Kacker, S., Sharma, M.: A brief introduction and review on galvanic skin response. Int. J. Med. Res. Prof 2, 13–17 (2016)
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)
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)
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)
Posada-Quintero, H.F., et al.: Time-varying analysis of electrodermal activity during exercise. PLoS ONE 13(6), e0198328 (2018)
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)
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)
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)
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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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