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Background: Hyperventilation during sevoflurane-N2O-O2 mask induction in adults is associated with a hyperdynamic circulatory response and epileptiform electroencephalogram (EEG). We tested the hypothesis that delaying onset of hyperventilation will prevent severe (periodic) epileptiform EEG and hyperdynamic response.Methods: Thirty patients were randomized to receive either delayed (group D, n=15) or immediate (group I, n=15) onset of hyperventilation during sevoflurane (8% in N2O 50%) mask inhalation induction with single-breath method for unconsciousness. Fifteen patients were allowed to breathe spontaneously for 2 min after loss of consciousness and controlled hyperventilation (ETCO2 <4%) was started thereafter. In 15 patients controlled hyperventilation was started immediately after loss of consciousness. EEG was recorded, and mean arterial pressure (MAP) and heart rate (HR) registered.Results: Epileptiform EEG patterns were seen in 13 patients in group I and in 9 patients in group D (n.s.). Periodic epileptiform discharges (PED) tended to occur more often in group I (P=0.07). Heart rate and MAP were higher in group I than in group D from 2 min to 3 min (P < 0.05), and both HR and MAP rose significantly from the baseline in group I. In group D, HR but not MAP rose significantly from baseline.Conclusion: Regardless of its timing, hyperventilation at a high sevoflurane concentration produced severe epileptiform EEG with a hyperdynamic response. PED tended to occur more often with immediate onset of hyperventilation. [ABSTRACT FROM AUTHOR]

Nonlinear dynamics has recently been extensively used to study epilepsy due to the complex nature of the neuronal systems. This study presents a novel method that characterizes the dynamic behavior of pediatric seizure events and introduces a systematic approach to locate the nullclines on the phase space when the governing differential equations are unknown. Nullclines represent the locus of points in the solution space where the components of the velocity vectors are zero. A simulation study over 5 benchmark nonlinear systems with well-known differential equations in three-dimensional exhibits the characterization efficiency and accuracy of the proposed approach that is solely based on the reconstructed solution trajectory. Due to their unique characteristics in the nonlinear dynamics of epilepsy, discriminative features can be extracted based on the nullclines concept. Using a limited training data (only 25% of each EEG record) in order to mimic the real-world clinical practice, the proposed approach achieves 91.15% average sensitivity and 95.16% average specificity over the benchmark CHB-MIT dataset. Together with an elegant computational efficiency, the proposed approach can, therefore, be an automatic and reliable solution for patient-specific seizure detection in long EEG recordings.

Synchrony patterns between the EEG measured from the subthalamic nucleus and scalp are analyzed during induction of propofol anesthesia. Various methods such as Coherence, Phase Locking Value, Partial Directed Coherence, Mutual Information, and Transfer Entropy are applied. The results indicate that, in general, the synchrony patterns seen in the depth-scalp electrode pair are similar to those seen between two scalp electrodes of opposite hemispheres. A clear change of state is revealed at about 10 minutes before burst suppression; this event is revealed by linear (Coherence) as well as nonlinear (Phase Locking Value) and information theoretic (Mutual Information and Transfer Entropy) measures. The results suggest that relatively large depth electrodes such as those used in the treatment of the Parkinson's disease are predominantly sensitive to cortical activity.

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 analyzed. We show that the selection of algorithm parameters and the use of normalization are important issues in interpretation and comparison of the results. We also show that permutation entropy is the most sensitive to phase information among the studied measures and that the EEG signal during high amplitude delta activity in deep anesthesia is of highly nonlinear nature.

Penicillin-induced focal epilepsy is a well-known model in epilepsy research. In this model, epileptic activity is generated by delivering penicillin focally to the cortex. The drug induces interictal electroencephalographic (EEG) spikes which evolve in time and may later change to ictal discharges. This paper proposes a method for automatic classification of these interictal epileptic spikes using iterative K-means clustering. The method is shown to be able to detect different spike waveforms and describe their characteristic occurrence in time during penicillin-induced focal epilepsy. The study offers potential for future research by providing a method to objectively and quantitatively analyze the time sequence of interictal epileptic activity.

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 linear filter is adapted according to the instantaneous frequency. The algorithm is shown to perform well in tracking the alpha rhythm (the alpha coma pattern) in critically ill patients sedated with midazolam.

Sevoflurane is a volatile anesthetic which is reported to cause epileptiform EEG changes together with undesired symptoms such as convulsions. In this paper, an algorithm for the automatic detection of these EEG changes is presented which could enable safer induction of anesthesia with sevoflurane by informing the clinicians about the epileptiform EEG. EEG was recorded from 60 healthy female patients during sevoflurane anesthesia. A neurophysiologist classified the EEG waveforms. Each anesthesia period lasted 6 minutes. 48 signal features were extracted from the raw EEG. 5-sec segments of EEG were classified based on the extracted features using a decision tree with a logistic regression based decisions and the classification results were compared to the neurophysiologist's classifications. Awake EEG was recognized with 69%/96% (sensitivity/specificity), Burst suppression with 56%/98%, Epileptiform EEG with 83%/87%, normal slow anesthesia EEG with 86%/64%, slow anesthesia EEG with monophasic pattern with 65%/80%, and slow anesthesia EEG with monophasic pattern and spikes with 54%/84%