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Dynamical properties of large ensembles of neurons in the brain during sleep are found to be highly complex; therefore, nonlinear methods have found application in the analysis of random looking EEG signals. Here we have addressed the issue of the transition process from NREM to REM and vice versa using wavelet based multifractal formalism in healthy humans. We have used the technique based on wavelet...
The wavelet transform is a natural tool for characterizing self-similar signals. In this work, the spectral exponent ?? derived from the wavelet-based representation for 1/f processes is used to investigate the self-similarity of electrocorticography (intracranial EEG) signals from an epilepsy patient. An increase in ?? leads to sample signals with smoother temporal patterns. Our computational results...
We are developing a posturographic sleepiness tester that employs wavelet and fractal dimension analysis. It detects and characterizes transients in postural sway signals. We measured 6 subjects' postural steadiness (eyes open/closed) with a Nintendo Wii Fit reg balance board every hour during 10 h. During the next two weeks, we performed three additional measurements with different time awake (TA)...
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