EEG complexity as a measure of depth of anesthesia for patients

By Xu-Sheng Zhang, Rob J. Roy, Erik Weber Jensen. IEEE Transactions on Biomedical Engineering, 2001

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An ensemble system for automatic sleep stage classification using single channel EEG signal (2012, methodology): “Non-linear features As EEG shows significant complex behavior with strong nonlinear and dynamical properties [26], the effectiveness of different non-linear measures like correlation dimension [44], Lyapunov exponent [45], approximate entropy [46], detrended fluctuation analysis [47], Higuchi fracta”
Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure. (2006, methodology): “The use of this quantitative complexity measure can help us gain a better insight into the system dynamics [6].”
Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis (2006, methodology): “Other authors have used LZ complexity to study ECG dynamics [33], to detect ventricular tachycardia and fibrillation [34], [35], to predict movement in anaesthesia in animals [36], to estimate the depth of anaesthesia [37], [38] and to quantify oscillations in uterine electromyography [39].”
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification (2018, unknown): “derived from nonlinear processes [8], [10]–[12].”