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Open AccessArticle

Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients

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Department of Information Engineering, University of Padova, 35131 Padova, Italy
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Department of Neurosciences, University of Padova, 35128 Padova, Italy
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Department of Nephrology and Endocrinology, Nordsjællands Hospital, 3400 Hillerød, Denmark
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Department of Obstetrics and Gynaecology, Nordsjællands Hospital, 3400 Hillerød, Denmark
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(1), 81; https://doi.org/10.3390/e22010081
Received: 30 September 2019 / Revised: 23 December 2019 / Accepted: 7 January 2020 / Published: 9 January 2020
(This article belongs to the Special Issue Entropy, Nonlinear Dynamics and Complexity)
Previous literature has demonstrated that hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable scalp electroencephalography (EEG) changes in power spectral density. In the present study, we used a dataset of 19-channel scalp EEG recordings in 34 patients with T1D who underwent a hyperinsulinemic–hypoglycemic clamp study. We found that hypoglycemic events are also characterized by EEG complexity changes that are quantifiable at the single-channel level through empirical conditional and permutation entropy and fractal dimension indices, i.e., the Higuchi index, residuals, and tortuosity. Moreover, we demonstrated that the EEG complexity indices computed in parallel in more than one channel can be used as the input for a neural network aimed at identifying hypoglycemia and euglycemia. The accuracy was about 90%, suggesting that nonlinear indices applied to EEG signals might be useful in revealing hypoglycemic events from EEG recordings in patients with T1D. View Full-Text
Keywords: entropy; complexity measures; time-series analysis; EEG; type 1 diabetes; hypoglycemia; neural network classification entropy; complexity measures; time-series analysis; EEG; type 1 diabetes; hypoglycemia; neural network classification
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Rubega, M.; Scarpa, F.; Teodori, D.; Sejling, A.-S.; Frandsen, C.S.; Sparacino, G. Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients. Entropy 2020, 22, 81.

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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3465213
    Link: https://zenodo.org/record/3465213#.XZHRqEYzaUk
    Description: Data and MATLAB code associated with the publication "Detection of hypoglycemia using measures of EEG complexity in type 1 diabetes patients" available to readers
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