A. Anier

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Entropy and complexity of the electroencephalogram (EEG) have recently been proposed as measures of depth of anesthesia and sedation. Using surrogate data of predefined spectrum and probability distribution we show that the various algorithms used for the calculation of entropy and complexity actually measure different properties of the signal. The tested(More)
The ability of two easy-to-calculate nonlinear parameters, the Higuchi fractal dimension (HDf) and spectral entropy, to follow the depth of sedation in the intensive care unit is assessed. For comparison, the relative beta ratio is calculated. The results are evaluated using clinical assessment of the Ramsay score. The results show that the HD/sub f/(More)
BACKGROUND Several measures have been developed to quantify the change in EEG from wakefulness to deep anaesthesia. Measures of signal complexity or entropy have been popular and even applied in commercial monitors. These measures quantify different features of the signal, however, and may therefore behave in an incomparable way when calculated for(More)
A novel algorithm for the detection and tracking of rhythmic patterns in the EEG signal is presented. The algorithm includes the following steps: 1) linear filtering using symmetric impulse response, 2) calculation of the first intrinsic mode of the filter output and 3) calculation of instantaneous frequency and amplitude using the Hilbert transform. The(More)
In this paper 5 methods for the assessment of signal entropy are compared in their capability to follow the changes in the EEG signal during transition from continuous EEG to burst suppression in deep anesthesia. To study the sensitivity of the measures to phase information in the signal, phase randomization as well as amplitude adjusted surrogates are also(More)
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