TY - JOUR
T1 - Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients
AU - Rubega, Maria
AU - Scarpa, Fabio
AU - Teodori, Debora
AU - Sejling, Anne-Sophie
AU - Frandsen, Christian S
AU - Sparacino, Giovanni
PY - 2020/1/9
Y1 - 2020/1/9
N2 - 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.
AB - 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.
KW - Complexity measures
KW - EEG
KW - Entropy
KW - Hypoglycemia
KW - Neural network classification
KW - Time-series analysis
KW - Type 1 diabetes
UR - http://www.scopus.com/inward/record.url?scp=85078531754&partnerID=8YFLogxK
U2 - 10.3390/e22010081
DO - 10.3390/e22010081
M3 - Journal article
C2 - 33285854
SN - 1099-4300
VL - 22
SP - 81
JO - Entropy (Basel, Switzerland)
JF - Entropy (Basel, Switzerland)
IS - 1
ER -