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

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.

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Single-Dose Euglycemic Clamp Study Demonstrating Pharmacokinetic and Pharmacodynamic Similarity Between SAR341402 Insulin Aspart and US- and EU-Approved Versions of Insulin Aspart in Subjects with Type 1 Diabetes

Diabetes Technology & Therapeutics ◽

10.1089/dia.2019.0351 ◽

2020 ◽

Vol 22 (4) ◽

pp. 278-284 ◽

Cited By ~ 3

Author(s):

Christoph Kapitza ◽

Leszek Nosek ◽

Wolfgang Schmider ◽

Lenore Teichert ◽

Irene Nowotny

Keyword(s):

Type 1 Diabetes ◽

Single Dose ◽

Insulin Aspart ◽

Euglycemic Clamp ◽

Clamp Study

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Exploring Neurofeedback Training for BMI Power Augmentation of Upper Limbs: A Pilot Study

Entropy ◽

10.3390/e23040443 ◽

2021 ◽

Vol 23 (4) ◽

pp. 443

Author(s):

Hongbo Liang ◽

Shota Maedono ◽

Yingxin Yu ◽

Chang Liu ◽

Naoya Ueda ◽

...

Keyword(s):

Training Session ◽

Training System ◽

Eeg Signals ◽

Shoulder Joints ◽

Power Spectral ◽

Training Stage ◽

Beta Power ◽

Eeg Changes ◽

Machine Interface ◽

Flexion And Extension

Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power augmentation through EEG-NFB training. First, we constructed an EEG-NFB training system for power augmentation. Then, three subjects were assigned to three NFB training stages, based on a 6-day consecutive training session as one stage. The subjects received real-time feedback from their EEG signals by a robotic arm while conducting flexion and extension movement with their elbow and shoulder joints, respectively. EEG signals were compared with each NFB training stage. The training results showed that EEG beta (12–40 Hz) power increased after the NFB training for both the elbow and the shoulder joints’ movements. EEG beta power showed sustained improvements during the 3-stage training, which revealed that even the short-term training could improve EEG signals significantly. Moreover, the training effect of the shoulder joints was more obvious than that of the elbow joints. These results suggest that NFB training can improve EEG signals and clarify the specific EEG changes during the movement. Our results may even provide insights into how the neural effects of NFB can be better applied to the BMI power augmentation system and improve the performance of healthy individuals.

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Review for "Single‐dose euglycemic clamp study in two cohorts to compare exposure of SAR341402 (insulin aspart) Mix 70/30 to US ‐ and EU‐Approved versions of insulin aspart Mix 70/30 and SAR341402 rapid‐acting solution in subjects with type 1 diabetes"

10.1111/dom.14260/v1/review1 ◽

2020 ◽

Keyword(s):

Type 1 Diabetes ◽

Single Dose ◽

Insulin Aspart ◽

Euglycemic Clamp ◽

Clamp Study

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Wavelet Design for Automatic Real-Time Eye Blink Detection and Recognition in EEG Signals

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2019.3.3516 ◽

2019 ◽

Vol 14 (3) ◽

pp. 375-387

Author(s):

Michael Gabriel Miranda ◽

Renato Alberto Salinas ◽

Ulrich Raff ◽

Oscar Magna

Keyword(s):

Real Time ◽

Single Channel ◽

Specific Pattern ◽

Eeg Signal ◽

Eeg Signals ◽

Time Operation ◽

Real Time Operation ◽

Blink Detection ◽

Eeg Recordings ◽

Detection And Localization

The blinking of an eye can be detected in electroencephalographic (EEG) recordings and can be understood as a useful control signal in some information processing tasks. The detection of a specific pattern associated with the blinking of an eye in real time using EEG signals of a single channel has been analyzed. This study considers both theoretical and practical principles enabling the design and implementation of a system capable of precise real-time detection of eye blinks within the EEG signal. This signal or pattern is subject to considerable scale changes and multiple incidences. In our proposed approach, a new wavelet was designed to improve the detection and localization of the eye blinking signal. The detection of multiple occurrences of the blinking perturbation in the recordings performed in real-time operation is achieved with a window giving a time-limited projection of an ongoing analysis of the sampled EEG signal.

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Four-sphere head model for EEG signals revisited

10.1101/124875 ◽

2017 ◽

Cited By ~ 2

Author(s):

Solveig Næss ◽

Chaitanya Chintaluri ◽

Torbjørn V. Ness ◽

Anders M. Dale ◽

Gaute T. Einevoll ◽

...

Keyword(s):

Analytical Formula ◽

Mathematical Expression ◽

Head Model ◽

The Finite Element Method ◽

Sphere Model ◽

Eeg Signals ◽

Scalp Eeg ◽

Spherical Head ◽

Analytical Formulas ◽

Eeg Recordings

AbstractElectric potential recorded at the scalp (EEG) is dominated by contributions from current dipoles set by active neurons in the cortex. Estimation of these currents, called ’inverse modeling’, requires a ’forward’ model, which gives the potential when the positions, sizes, and directions of the current dipoles are known. Diﬀerent models of varying complexity and realism are used in the field. An important analytical example is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. This model has been used extensively in the analysis of EEG recordings. Since it is analytical, it can also serve as a benchmark against which numerical schemes, such as the Finite Element Method (FEM), can be tested. While conceptually clear, the mathematical expression for the scalp potentials in the four-sphere model is quite cumbersome, and we observed the formulas presented in the literature to contain errors. We here derive and present the correct analytical formulas for future reference. They are compared with the results of FEM simulations of four-sphere model. We also provide scripts for computing EEG potentials in this model with the correct analytical formula and using FEM.

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