Jukka Kortelainen

Effects of Remifentanil on the Spectrum and Quantitative Parameters of Electroencephalogram in Propofol Anesthesia

Background:A high dose of opioids associated with a low dose of propofol has become a popular anesthetic technique. However, the influence of opioids on the electroencephalographic phenomenon related to induction of anesthesia and, thereby, on the quantitative parameters used in the depth-of-anesthesia estimation is not well known. Methods:Twenty-seven patients were divided into three groups to receive saline, low-dose remifentanil (7.5 μg · kg−1 · h−1) or high-dose remifentanil (30 μg · kg−1 · h−1) during induction of anesthesia with propofol (30 mg · kg−1 · h−1). Electroencephalogram was recorded from Fz electrode, and its time-frequency properties in the patient groups were analyzed from the induction of anesthesia to the occurrence of burst suppression pattern. The group differences in 14 quantitative spectral parameters used in the depth-of-anesthesia estimation were examined as well. Results:The time-frequency properties of electroencephalogram were different between groups. The high-frequency (greater than 14 Hz) activity during light anesthesia was decreased in remifentanil groups; whereas, increased activity in extended alpha band (7–14 Hz) and decreased activity in delta band (0.5–4 Hz) was observed during deep anesthesia. This resulted in statistically significant changes in all 14 quantitative parameters. Conclusions:The effect of remifentanil on the spectrum and quantitative parameters of electroencephalogram is significant and strongly dependent on the level of anesthesia. Coadministration of opioids therefore challenges the reliability of the spectral properties of electroencephalogram in the depth-of-anesthesia estimation by using a frontal montage. Furthermore, the finding has implications for design of opioid coadministration studies.

Isomap Approach to EEG-Based Assessment of Neurophysiological Changes During Anesthesia

Increasing concentrations of anesthetics in the blood induce a continuum of neurophysiological changes, which reflect on the electroencephalogram (EEG). EEG-based depth of anesthesia assessment requires that the signal samples are correctly associated with the neurophysiological changes occurring at different anesthetic levels. A novel method is presented to estimate the phase of the continuum using the feature data extracted from EEG. The feature data calculated from EEG sequences corresponding to continuously deepening anesthesia are considered to form a one-dimensional nonlinear manifold in the multidimensional feature space. Utilizing a recently proposed algorithm, Isomap, the dimensionality of the feature data is reduced to achieve a one-dimensional embedding representing this manifold and thereby the continuum of neurophysiological changes during induction of anesthesia. The Isomap-based estimation is validated with data recorded from nine patients during induction of propofol anesthesia. The proposed method provides a novel approach to assess neurophysiological changes during anesthesia and offers potential for the development of more advanced systems for the depth of anesthesia monitoring.

Invariant trajectory classification of dynamical systems with a case study on ECG

An invariant pattern recognition framework for classification of phase space trajectories of nonlinear dynamical systems is presented. Using statistical shape theory, known external influences can be discriminated from true changes of the system. The external effects are modeled as a transformation group acting on the phase space, and variation of the trajectories not explained by the transformations is accounted for using principal component analysis. The approach suggested is highly adaptable to a wide range of situations and individual differences. The methodology presented is applied to detect abnormalities in electrocardiograms. Results based on measured data indicate that the model developed is resistant to the effects of respiration and body position changes, which are abundant in ambulatory conditions and cause significant morphological artifacts in the signal. The results also show that the detection of an artificially induced acute myocardial infarction is achieved with high performance. Due to its low computational complexity, the method developed can be implemented in real-time. The method developed also adapts to morphological changes caused by various heart conditions.

Experimental comparison of connectivity measures with simulated EEG signals

Directional connectivity measures exist with different theoretical backgrounds, i.e., information theoretic, parametric-modeling based or phase related. In this paper, we perform the first comparison in this extend of a set of conventional and directed connectivity measures [cross-correlation, coherence, phase slope index (PSI), directed transfer function (DTF), partial-directed coherence (PDC) and transfer entropy (TE)] with eight-node simulation data based on real resting closed eye electroencephalogram (EEG) source signal. The ability of the measures to differentiate the direct causal connections from the non-causal connections was evaluated with the simulated data. Also, the effects of signal-to-noise ratio (SNR) and decimation were explored. All the measures were able to distinguish the direct causal interactions from the non-causal relations. PDC detected less non-causal connections compared to the other measures. Low SNR was tolerated better with DTF and PDC than with the other measures. Decimation affected most the results of TE, DTF and PDC. In conclusion, parametric-modeling-based measures (DTF, PDC) had the highest sensitivity of connections and tolerance to SNR in simulations based on resting closed eye EEG. However, decimation of data has to be carefully considered with these measures.

EEG Frequency Progression during Induction of Anesthesia: from Start of Infusion to Onset of Burst Suppression Pattern

The anesthetic infusion with propofol influences EEG activity rather smoothly by changing the amplitude activity in different frequency bands. This results in a frequency progression pattern (FPP) which can be related to the depth of anesthesia. An iterative algorithm is proposed for the estimation of the shape of this pattern. The presented method is applied to the data recorded from the start of the propofol anesthetic infusion to the onset of the burst suppression pattern (BSP) with nine patients. The results reveal the underlying FPP and how the onset of the BSP is related to it. The proposed method offers potential for the development of automatic assessment systems for the depth of anesthesia.

Time-frequency properties of electroencephalogram during induction of anesthesia.

A method for detailed description of the time-frequency characteristics of electroencephalogram during induction of anesthesia is proposed. The method, based on averaging of time-normalized smoothed pseudo-Wigner-Ville distributions, is applied to data recorded from nine patients undergoing propofol anesthesia. An extensive representation of the frequency progression pattern related to the induction of anesthesia is given and the time-frequency characteristics that are consistent/not consistent between patients are determined. It is also illustrated how four different clinical end-points, generally used in the assessment of the depth of anesthesia, can be related to different phases of the frequency progression pattern. The method presented has importance in providing information about the neurophysiological phenomenon during induction of anesthesia and can therefore be used in the development of new monitoring algorithms.

Depth of Anesthesia During Multidrug Infusion: Separating the Effects of Propofol and Remifentanil Using the Spectral Features of EEG

General anesthesia is usually induced with a combination of drugs. In addition to the hypnotic agent, such as propofol, opioids are often used due to their synergistic hypnotic and analgesic properties. However, the effects of opioids on the EEG changes and the clinical state of the patient during anesthesia are complex and hinder the interpretation of the EEG-based depth of anesthesia indexes. In this paper, a novel technology for separating the anesthetic effects of propofol and an ultrashort-acting opioid, remifentanil, using the spectral features of EEG is proposed. By applying a floating search method, a well-performing feature set is achieved to estimate the effects of propofol during induction of anesthesia and to classify whether or not remifentanil has been coadministered. It is shown that including the detection of the presence of opioids to the estimated effect of propofol significantly improves the determination of the clinical state of the patient, i.e., if the patient will respond to a painful stimulation.

Remifentanil Modifies the Relation of Electroencephalographic Spectral Changes and Clinical Endpoints in Propofol Anesthesia

Background:Depth-of-anesthesia monitoring with the electroencephalogram has become widely used in anesthesia practice. Generally, the methods presented are based on the spectral changes of the electroencephalogram. In this study, the authors evaluate the influence of remifentanil on the relation of timely occurrence of clinical endpoints and the spectral behavior of the electroencephalogram. Methods:Twenty-seven patients scheduled to undergo a surgical procedure were randomly assigned to three groups. Patients blindly received equal volumes of saline or remifentanil (7.5 or 30 &mgr;g · kg−1 · h−1) 1 min before induction of anesthesia with infusion of propofol (30 mg · kg−1 · h−1). The occurrence of loss of counting, loss of obeying verbal command, and loss of reaction to tetanic stimulation was assessed. The electroencephalogram was recorded from electrode Fz referenced to the common average, and an iterative algorithm was applied to solve the underlying frequency progression pattern. The positions of the clinical endpoints on the pattern were analyzed. Results:The administration of remifentanil during induction of anesthesia with propofol led to an earlier occurrence of the clinical endpoints on the frequency progression pattern. A significant difference (P < 0.05) was observed between the saline and high-dose patient groups in all three endpoints. The effect of remifentanil was proportional to the infusion rate. Conclusions:The infusion of remifentanil during propofol anesthesia significantly modifies the mutual relations of the electroencephalographic spectral characteristics and the endpoints in a predictable and quantifiable manner. This finding suggests that the electroencephalographic phenomena and the endpoints may not be identical but rather to some extent separate manifestations of hypnotic drug effect.

Multimodal emotion recognition by combining physiological signals and facial expressions: A preliminary study

Lately, multimodal approaches for automatic emotion recognition have gained significant scientific interest. In this paper, emotion recognition by combining physiological signals and facial expressions was studied. Heart rate variability parameters, respiration frequency, and facial expressions were used to classify persons emotions while watching pictures with emotional content. Three classes were used for both valence and arousal. The preliminary results show that, over the proposed channels, detecting arousal seem to be easier compared to valence. While the classification performance of 54.5% was attained with arousal, only 38.0% of the samples were classified correctly in terms of valence. In future, additional modalities as well as feature selection will be utilized to improve the results.

Increased electroencephalographic gamma activity reveals awakening from isoflurane anaesthesia in rats

BACKGROUND Animal studies often require reliable measures for anaesthetic drug effects. Lately, EEG-based depth of anaesthesia estimation has been widely applied to rat models. This study investigated the reliability of different EEG spectral properties in revealing awakening from isoflurane anaesthesia in rats. METHODS Adult Wistar rats with previously implanted frontal epidural electrodes were anaesthetized using isoflurane. The anaesthesia was slowly lightened until awakening, as observed by the first spontaneous movement, after which anaesthesia was induced again by increasing the isoflurane concentration. EEG was recorded during the recovery and induction periods, and the spectrograms and 23 quantitative spectral parameters used in the depth of anaesthesia estimation were calculated from the signals. RESULTS The awakening was accompanied by a decrease in EEG activity at frequencies below 25 Hz, while the activity at higher frequencies (25-150 Hz) was increased. Whereas the behaviour of parameters used to measure activity in the lower frequencies was subject to variability between animals, the increase in higher frequency activity was more consistent, resulting in a statistically significant change in the relative gamma power parameters at the moment of awakening. CONCLUSIONS The increase in frontal relative gamma activity, especially in the 50-150 Hz frequency band, seems to be the most reliable EEG indicator for the awakening of a rat from isoflurane anaesthesia. A number of other spectral measures can also be used to detect this event. However, the role of gamma frequencies in the performance of these parameters is crucial.