Prediction of response to incision using the mutual information of electroencephalograms during anaesthesia

Analysis of EEG background activity in Alzheimer's disease patients with Lempel–Ziv complexity and central tendency measure

“…Preliminary evidence suggests that, applied to EEGs, LZ complexity is predictive of epileptic seizures [31] and can be useful to quantify the depth of anaesthesia [6,34]. Moreover, it has been applied to extract complexity from mutual information time series of EEGs in order to predict response during isoflurane anaesthesia with artificial neural networks [35].…”

Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients

“…On the other hand, the mutual information (MI) provides a measure of both linear and non-linear statistical dependencies between two time series [25]. Applied to the EEG, MI has been used to describe the information transmission in the brain in different states [30], [48], to extract characterizing features in epileptic seizures [46] and to predict the response to anaesthesia [22]. In particular, the AMI -i.e.…”

Analysis of the magnetoencephalogram background activity in Alzheimer's disease patients with auto-mutual information

“…Na et al [21] have investigated the EEG information transmission in schizophrenic patients using cross mutual information (CMI). In [22], Lempel-Ziv complexity measures were extracted from the MI time series of EEGs in order to predict response during isoflurane anaesthesia. These measures have also been used to investigate evoked activity.…”