Mika Sarkela

Description of the Entropy™ algorithm as applied in the Datex-Ohmeda S/5™ Entropy Module

H. VIERTIÖ-OJA, V. MAJA, M. SÄRKELÄ, P. TALJA, N. TENKANEN, H. TOLVANEN-LAAKSO, M. PALOHEIMO, A. VAKKURI, A. YLI-HANKALA and P. MERILÄINEN Datex-Ohmeda Division, Instrumentarium Corp., Helsinki, Department of Anesthesia, Eye and Ear Hospital, Helsinki University Hospital, Helsinki, Department of Anesthesia, Surgical Hospital, Helsinki University Hospital, Helsinki, Department of Anesthesia, Tampere University Hospital, and University of Tampere, Medical School, Tampere, and Instrumentarium Corp., Helsinki, Finland

Electroencephalogram spindle activity during dexmedetomidine sedation and physiological sleep

Background: Dexmedetomidine, a selective α2‐adrenoceptor agonist, induces a unique, sleep‐like state of sedation. The objective of the present work was to study human electroencephalogram (EEG) sleep spindles during dexmedetomidine sedation and compare them with spindles during normal physiological sleep, to test the hypothesis that dexmedetomidine exerts its effects via normal sleep‐promoting pathways.

Epileptiform Electroencephalogram during Mask Induction of Anesthesia with Sevoflurane

BACKGROUND Sevoflurane is suggested as a suitable anesthetic agent for mask induction in adults. The authors recently found that hyperventilation during sevoflurane-nitrous oxide-oxygen mask induction is associated with cardiovascular hyperdynamic response. We tested the hypothesis that the hyperdynamic response can be explained by electroencephalography (EEG) findings. METHODS Thirty women were randomly allocated to receive sevoflurane-nitrous oxygen-oxygen mask induction using a single-breath method, followed by either spontaneous breathing (n = 15) or controlled hyperventilation (n = 15) for 6 min. EEG was recorded. Blood pressure and heart rate were recorded at 1-min intervals. RESULTS Epileptiform EEG activity (spikes or polyspikes) was seen in all patients with controlled hyperventilation, and in seven patients with spontaneous breathing (P < 0.01). Jerking movements were seen in three patients with controlled hyperventilation. In the controlled hyperventilation group, heart rate increased 54% from baseline at 4 min after induction (P < 0.001). Mean arterial pressure increased 17% (P < 0.05), peaking at 3 min. In the spontaneous breathing group, heart rate showed no change, and mean arterial pressure decreased by 14% (P < 0.01) at 6 min. Heart rate and mean arterial pressure differed significantly between the groups from 2 min after beginning of the induction to the end of the trial. An increase in heart rate of more than 30% from baseline always was associated with epileptiform EEG activity. CONCLUSIONS Sevoflurane mask induction elicits epileptiform EEG patterns. These are associated with an increase in heart rate in patients with controlled hyperventilation and also during spontaneous breathing of sevoflurane.

Hypothermia-treated cardiac arrest patients with good neurological outcome differ early in quantitative variables of EEG suppression and epileptiform activity.

Objective:To evaluate electroencephalogram-derived quantitative variables after out-of-hospital cardiac arrest. Design:Prospective study. Setting:University hospital intensive care unit. Patients:Thirty comatose adult patients resuscitated from a witnessed out-of-hospital ventricular fibrillation cardiac arrest and treated with induced hypothermia (33°C) for 24 hrs. Interventions:None. Measurements and Main Results:Electroencephalography was registered from the arrival at the intensive care unit until the patient was extubated or transferred to the ward, or 5 days had elapsed from cardiac arrest. Burst-suppression ratio, response entropy, state entropy, and wavelet subband entropy were derived. Serum neuron-specific enolase and protein 100B were measured. The Pulsatility Index of Transcranial Doppler Ultrasonography was used to estimate cerebral blood flow velocity. The Glasgow-Pittsburgh Cerebral Performance Categories was used to assess the neurologic outcome during 6 mos after cardiac arrest. Twenty patients had Cerebral Performance Categories of 1 to 2, one patient had a Cerebral Performance Categories of 3, and nine patients had died (Cerebral Performance Categories of 5). Burst-suppression ratio, response entropy, and state entropy already differed between good (Cerebral Performance Categories 1–2) and poor (Cerebral Performance Categories 3–5) outcome groups (p = .011, p = .011, p = .008) during the first 24 hrs after cardiac arrest. Wavelet subband entropy was higher in the good outcome group between 24 and 48 hrs after cardiac arrest (p = .050). All patients with status epilepticus died, and their wavelet subband entropy values were lower (p = .022). Protein 100B was lower in the good outcome group on arrival at ICU (p = .010). After hypothermia treatment, neuron-specific enolase and protein 100B values were lower (p = .002 for both) in the good outcome group. The Pulsatility Index was also lower in the good outcome group (p = .004). Conclusions:Quantitative electroencephalographic variables may be used to differentiate patients with good neurologic outcomes from those with poor outcomes after out-of-hospital cardiac arrest. The predictive values need to be determined in a larger, separate group of patients.

Automatic analysis and monitoring of burst suppression in anesthesia

Objective.We studied the spectral characteristics of the EEGburst suppression patterns (BSP) of two intravenous anesthetics,propofol and thiopental. Based on the obtained results, we developed amethod for automatic segmentation, classification and compactpresentation of burst suppression patterns. Methods.The spectralanalysis was performed with the short time Fourier transform and withautoregressive modeling to provide information of frequency contents ofbursts. This information was used when designing appropriate filters forsegmentation algorithms. The adaptive segmentation was carried out usingtwo different nonparametric methods. The first one was based on theabsolute values of amplitudes and is referred to as the ADIF method. Thesecond method used the absolute values of the Nonlinear Energy Operator(NLEO) and is referred to as the NLEO method. Both methods have beendescribed earlier but they were modified for the purposes of BSPdetection. The signal was classified to bursts, suppressions andartifacts. Automatic classification was compared with manualclassification. Results.The NLEO method was more accurate,especially in the detection of artifacts. NLEO method classifiedcorrectly 94.0% of the propofol data and 92.8% of thethiopental data. With the ADIF method, the results were 90.5% and88.1% respectively. Conclusions.Our results show thatburst suppression caused by the different anesthetics can be reliablydetected with our segmentation and classification methods. The analysisof normal and pathological EEG, however, should include information ofthe anesthetic used. Knowledge of the normal variation of the EEG isnecessary in order to detect the abnormal BSP of, for instance, seizurepatients.

Increase in high frequency EEG activity explains the poor performance of EEG spectral entropy monitor during S-ketamine anesthesia

OBJECTIVE To study the effects of S-ketamine on the EEG and to investigate whether spectral entropy of the EEG can be used to assess the depth of hypnosis during S-ketamine anesthesia. METHODS The effects of sub-anesthetic (159 (21); mean (SD) ng/ml) and anesthetic (1,959 (442) ng/ml) serum concentrations of S-ketamine on state entropy (SE), response entropy (RE) and classical EEG spectral power variables (recorded using the Entropy Module, GE Healthcare, Helsinki, Finland) were studied in 8 healthy males. These EEG data were compared with EEG recordings from 6 matching subjects anesthetized with propofol. RESULTS The entropy values decreased from the baseline SE 85 (3) and RE 96 (3) to SE 55 (18) and RE 72 (17) during S-ketamine anesthesia but both inter- and intra-individual variation of entropy indices was wide and their specificity to indicate unconsciousness was poor. Propofol induced more pronounced increase in delta power (P<0.02) than S-ketamine, whereas anesthetic S-ketamine induced more high frequency EEG activity in the gamma band (P<0.001). Relative power of 20-70 Hz EEG activity was associated with high SE (P=0.02) and RE (P=0.03) values during S-ketamine anesthesia. CONCLUSIONS These differences in low and high frequency EEG power bands probably explain why entropy monitor, while adequate for propofol, is not suitable for assessing the depth of S-ketamine anesthesia. SIGNIFICANCE The entropy monitor is not adequate for monitoring S-ketamine-induced hypnosis.

Wide inter-individual variability of bispectral index and spectral entropy at loss of consciousness during increasing concentrations of dexmedetomidine, propofol, and sevoflurane

BACKGROUND The bispectral index (BIS) and the spectral entropy (state entropy, SE, and response entropy, RE) are depth-of-anaesthesia monitors derived from EEG and have been developed to measure the effects of anaesthetics on the cerebral cortex. We studied whether they can differentiate consciousness from unconsciousness during increasing doses of three different anaesthetic agents. METHODS Thirty healthy male volunteers aged 19-30 yr were recruited and divided into three 10-volunteer groups to receive either dexmedetomidine, propofol, or sevoflurane in escalating concentrations at 10 min intervals until loss of consciousness (LOC) was reached. Consciousness was tested at 5 min intervals and after drug discontinuation at 1 min intervals by requesting the subjects to open their eyes. LOC was defined as unresponsiveness to the request and pre-LOC as the last meaningful response. The first meaningful response to the request after drug discontinuation was defined as regaining of consciousness (ROC). For the statistical analysis, pre-LOC and ROC values were pooled to represent the responsive state while LOC values represented the unresponsive state. Prediction probability (P(K)) was estimated with the jack-knife method. RESULTS The lowest mean values for BIS, SE, and RE were recorded at LOC with all three drugs. The P(K) values were low for dexmedetomidine (BIS 0.62, SE 0.58, RE 0.59), propofol (BIS 0.73, SE 0.72, RE 0.72), and sevoflurane (BIS 0.70, SE 0.52, RE 0.62). CONCLUSIONS Because of wide inter-individual variability, BIS and entropy were not able to reliably differentiate consciousness from unconsciousness during and after stepwise increasing concentrations of dexmedetomidine, propofol, and sevoflurane.

Bispectral Index, Entropy, and Quantitative Electroencephalogram during Single-agent Xenon Anesthesia

Background:The aim was to evaluate the performance of anesthesia depth monitors, Bispectral Index (BIS) and Entropy, during single-agent xenon anesthesia in 17 healthy subjects. Methods:After mask induction with xenon and intubation, anesthesia was continued with xenon only. BIS, State Entropy and Response Entropy, and electroencephalogram were monitored throughout induction, steady-state anesthesia, and emergence. The performance of BIS, State Entropy, and Response Entropy were evaluated with prediction probability, sensitivity, and specificity analyses. The power spectrum of the raw electroencephalogram signal was calculated. Results:The mean (SD) xenon concentration during anesthesia was 66.4% (2.4%). BIS, State Entropy, and Response Entropy demonstrated low prediction probability values at loss of response (0.455, 0.656, and 0.619) but 1 min after that the values were high (0.804, 0.941, and 0.929). Thereafter, equally good performance was demonstrated for all indices. At emergence, the prediction probability values to distinguish between steady-state anesthesia and return of response for BIS, State Entropy, and Response Entropy were 0.988, 0.892, and 0.992. No statistical differences between the performances of the monitors were observed. Quantitative electroencephalogram analyses showed generalized increase in total power (P < 0.001), delta (P < 0.001) and theta activity (P < 0.001), and increased alpha activity (P = 0.003) in the frontal brain regions. Conclusions:Electroencephalogram-derived depth of sedation indices BIS and Entropy showed a delay to detect loss of response during induction of xenon anesthesia. Both monitors performed well in distinguishing between conscious and unconscious states during steady-state anesthesia. Xenon-induced changes in electroencephalogram closely resemble those induced by propofol.

Assessing the depth of dexmedetomidine‐induced sedation with electroencephalogram (EEG)‐based spectral entropy

Background:  Adequate sedation of critically ill patients improves the outcome of intensive care. Maintaining an optimal level of sedation in the intensive care unit (ICU) is difficult because of a lack of appropriate monitoring methods to guide drug dosing. Dexmedetomidine, a selective α2‐adrenoceptor agonist, has recently been introduced for the sedation of ICU patients. This study investigated the utility of electroencephalogram (EEG)‐based spectral entropy monitoring (with M‐ENTROPY™, GE Healthcare, Helsinki, Finland) for the assessment of dexmedetomidine‐induced sedation.

Quantification of Epileptiform Electroencephalographic Activity during Sevoflurane Mask Induction

Background:Sevoflurane may induce epileptiform electroencephalographic activity leading to unstable Bispectral Index numbers, underestimating the hypnotic depth of anesthesia. The authors developed a method for the quantification of epileptiform electroencephalographic activity during sevoflurane anesthesia. Methods:Electroencephalographic data from 60 patients under sevoflurane mask induction were used in the analysis. Electroencephalographic data were visually classified. A novel electroencephalogram-derived quantity, wavelet subband entropy (WSE), was developed. WSE variables were calculated from different frequency bands. Performance of the WSE in detection and quantification of epileptiform electroencephalographic activity and the ability of the WSE to recognize misleading Bispectral Index readings caused by epileptiform activity were evaluated. Results:Two WSE variables were found to be sufficient for the quantification of epileptiform activity: WSE from the frequency bands 4–16 and 16–32 Hz. The lower frequency band was used for monophasic pattern monitoring, and the higher frequency band was used for spike activity monitoring. WSE values of the lower and higher bands followed the time evolution of epileptiform activity with prediction probabilities of 0.809 (SE, 0.007) and 0.804 (SE, 0.007), respectively. In deep anesthesia with epileptiform activity, WSE detected electroencephalographic patterns causing Bispectral Index readings greater than 60, with event sensitivity of 97.1%. Conclusions:The developed method proved useful in detection and quantification of epileptiform electroencephalographic activity during sevoflurane anesthesia. In the future, it may improve the understanding of electroencephalogram-derived information by assisting in recognizing misleading readings of depth-of-anesthesia monitors. The method also may assist in minimizing the occurrence of epileptiform activity and seizures during sevoflurane anesthesia.