scholarly journals Anesthesia Assessment Based on ICA Permutation Entropy Analysis of Two-Channel EEG Signals

2019 ◽  
pp. 244-253
Author(s):  
Tianning Li ◽  
Prashanth Sivakumar ◽  
Xiaohui Tao
Keyword(s):  
Permutation Entropy ◽  
Entropy Analysis ◽  
Eeg Signals

scholarly journals Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

Entropy ◽  
2014 ◽  
Vol 16 (6) ◽  
pp. 3049-3061 ◽  
Author(s):  
Jing Li ◽  
Jiaqing Yan ◽  
Xianzeng Liu ◽  
Gaoxiang Ouyang
Keyword(s):  
Permutation Entropy ◽  
Eeg Signals ◽  
Absence Seizures

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

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 81 ◽  
Author(s):  
Maria Rubega ◽  
Fabio Scarpa ◽  
Debora Teodori ◽  
Anne-Sophie Sejling ◽  
Christian S. Frandsen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Single Channel ◽  
Permutation Entropy ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Power Spectral ◽  
Eeg Changes ◽  
Eeg Recordings ◽  
Clamp Study

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.

scholarly journals Weighted-Permutation Entropy Analysis of Resting State EEG from Diabetics with Amnestic Mild Cognitive Impairment

Entropy ◽  
2016 ◽  
Vol 18 (8) ◽  
pp. 307 ◽  
Author(s):  
Zhijie Bian ◽  
Gaoxiang Ouyang ◽  
Zheng Li ◽  
Qiuli Li ◽  
Lei Wang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Amnestic Mild Cognitive Impairment ◽  
Permutation Entropy ◽  
Entropy Analysis

scholarly journals Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhixian Yang ◽  
Yinghua Wang ◽  
Gaoxiang Ouyang
Keyword(s):  
Fuzzy Inference System ◽  
Slow Wave Sleep ◽  
Fuzzy Inference ◽  
Permutation Entropy ◽  
Eeg Signals ◽  
Inference System ◽  
Control Subjects ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Background Eeg

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.

Permutation entropy analysis of vital signs data for outcome prediction of patients with severe traumatic brain injury

2015 ◽  
Vol 56 ◽  
pp. 167-174 ◽  
Author(s):  
Konstantinos Kalpakis ◽  
Shiming Yang ◽  
Peter F. Hu ◽  
Colin F. Mackenzie ◽  
Lynn G. Stansbury ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Severe Traumatic Brain Injury ◽  
Outcome Prediction ◽  
Vital Signs ◽  
Permutation Entropy ◽  
Entropy Analysis
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