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EEG signal is a typical nonlinear time series and its correlation dimension can be calculated to measure the series. In this paper, the correlation dimensions of 30 healthy samples and 30 patient samples are calculated. By the statistic result, an important conclusion is represented that the values of correlation dimension of different groups exist great difference. Based on more clinical experiments,...
In this paper an advanced algorithm based on G-P algorithm is introduced to calculate the correlative dimension (CD) of HRV signals. Moreover, Theiler's correction is considered to avoid the autocorrelation effect of the time serials. It will reduce the possibility to get spurious dimension. The algorithm is applied to clinical HRV data which are collected from twelve young healthy subjects under...
The analysis of heart rate variability (HRV) has become a tool for noninvasively detecting the cardiovascular modulation of autonomic nervous system (ANS). Traditional analysis in frequency-domain mainly includes calculating the power and the peak frequency of each physiological frequency component. Whether employing the non-parametric or parametric method to estimate the power spectrum density (PSD),...
Nonlinear analysis of electroencephalogram (EEG) signals provides a means for studying the dynamical changes in cortical networks related to brain electrical activity. In this study, the correlation dimension (D/sub 2/) and point correlation dimension (PD/sub 2/) were used to investigate the quantitative complexity of EEG during cognitive processes. EEGs were recorded in 30 normal subjects under seven...
Cognition process is directly related to the brain functionality and is a dynamically changing system. Nonlinear analysis of EEG signals has been used as a means for studying the dynamical changes in cortical networks. In the course of cognition process, the activity complexity of the neuronal units is continually shifting. This phenomenon can be viewed with the topographic map and nonlinear EEG measures...
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