Monitoring depth of anesthesia utilizing a combination of electroencephalographic and standard measures.

BACKGROUND: For decades, monitoring depth of anesthesia was mainly based on unspecific effects of anesthetics, for example, blood pressure, heart rate, or drug concentrations. Today, electroencephalogram-based monitors promise a more specific assessment of the brain function. To date, most approaches were focused on a "head-to-head" comparison of either electroencephalogram- or standard parameter-based monitoring. In the current study, a multimodal indicator based on a combination of both electro encephalographic and standard anesthesia monitoring parameters is defined for quantification of "anesthesia depth." METHODS: Two hundred sixty-three adult patients from six European centers undergoing surgery with general anesthesia were assigned to 1 of 10 anesthetic combinations according to standards of the enrolling hospital. The anesthesia multimodal index of consciousness was developed using a data-driven approach, which maps standard monitoring and electroencephalographic parameters into an output indicator that separates different levels of anesthesia from awake to electroencephalographic burst suppression. Obtained results were compared with either a combination of standard monitoring parameters or the electroencephalogram-based bispectral index. RESULTS: The anesthesia multimodal index of consciousness showed prediction probability (P(K)) of 0.96 (95% CI, 0.95 to 0.97) to separate different levels of anesthesia (wakefulness to burst suppression), whereas the bispectral index had significantly lower PK of 0.80 (0.76 to 0.81) at corrected threshold P value of less than 0.05. At the transition between consciousness and unconsciousness, anesthesia multimodal index of consciousness yielded a PK of 0.88 (0.85 to 0.91). CONCLUSION: A multimodal integration of both standard monitoring and electroencephalographic parameters may more precisely reflect the level of anesthesia compared with monitoring based on one of these aspects alone.

Evaluating the effectiveness of a memory aid system.

BACKGROUND: The ability to remember future intentions is compromised in both healthy and cognitively impaired older adults. Assistive technology provides older adults with promising solutions to cope with this age-related problem. However, the effectiveness and efficiency of such systems as memory aids is seldom evaluated in controlled, randomized trials. OBJECTIVES: We evaluated the effectiveness of a memory aid system, the InBad (engl. InBath), for bathroom-related daily care. Conceptually, the InBad learns user behavior patterns and detects deviations from the learned pattern in order to notify the user of a forgotten task. METHODS: We simulated a challenging morning routine consisting of 22 bathroom activities with a sample of 60 healthy older adults. Participants were randomly assigned to three groups: (1) 'no memory support', i.e., participants received no support at all, (2) 'list support', i.e., participants could retrieve a list of all activities, and (3) 'system support', i.e., participants received prompts for specific activities that had not yet been executed. RESULTS: Both support groups executed significantly more activities compared to the 'no support' group. In addition, system support resulted in significantly better performance compared to list support with no significant differences between the two groups in overall task duration. CONCLUSION: The assistive support system was the most effective and efficient memory aid. The results suggest that assistive technology has the potential to enable older adults to remain safe and independent in their own home.

Adapted variable precision rough set approach for EEG analysis.

OBJECTIVE: Rough set theory (RST) provides powerful methods for reduction of attributes and creation of decision rules, which have successfully been applied in numerous medical applications. The variable precision rough set model (VPRS model), an extension of the original rough set approach, tolerates some degree of misclassification of the training data. The basic idea of the VPRS model is to change the class information of those objects whose class information cannot be induced without contradiction from the available attributes. Thereafter, original methods of RST are applied. An approach of this model is presented that allows uncertain objects to change class information during the process of attribute reduction and rule generation. This method is referred to as variable precision rough set approach with flexible classification of uncertain objects (VPRS(FC) approach) and needs only slight modifications of the original VPRS model. METHODS AND MATERIAL: To compare the VPRS model and VPRS(FC) approach both methods are applied to a clinical data set based on electroencephalogram of awake and anesthetized patients. For comparison, a second data set obtained from the UCI machine learning repository is used. It describes the shape of different vehicle types. Further well known feature selection methods were applied to both data sets to compare their results with the results provided by rough set based approaches. RESULTS: The VPRS(FC) approach requires higher computational effort, but is able to achieve better reduction of attributes for noisy or inconsistent data and provides smaller rule sets. CONCLUSION: The presented approach is a useful method for substantial attribute reduction in noisy and inconsistent data sets.

A combination of electroencephalogram and auditory evoked potentials separates different levels of anesthesia in volunteers.

BACKGROUND: It has been shown that the combination of electroencephalogram (EEG) and auditory evoked potentials (AEP) allows a good separation of consciousness from unconsciousness. In the present study, we sought a combined EEG/AEP indicator that allows both separation of consciousness from unconsciousness and discrimination among different levels of sedation and hypnosis over a wider range of anesthesia. METHODS: Fifteen unpremedicated volunteers received mono-anesthesia with sevoflurane or propofol in a randomized crossover design in two consecutive sessions. Loss of consciousness (LOC) and EEG burst suppression (BSP) defined end-points from the upper and lower range of general anesthesia. In addition to those two extremes, the difference between anesthetic concentration at BSP and LOC was divided into three equal intervals, resulting in two intermediate levels which divided the concentration from LOC (minimum) to BSP (maximum) into three equal steps. This data set was used to test whether a previously described combined EEG/AEP indicator "detector of consciousness" can also discriminate among degrees of anesthetic effects from the awake state to BSP. Furthermore, a new improved combined EEG/AEP indicator was developed on the basis of the data from the current study, and subsequently this new indicator was tested for its ability to separate consciousness from unconsciousness with the patient data set. RESULTS: The former "detector of consciousness" showed a prediction probability (P(K)) of 0.77 to separate different levels of anesthesia from the current study, whereas for the new combined EEG/AEP indicator, P(K) was 0.94. The new indicator was further applied to the previous study and achieved a P(K) of 0.89. CONCLUSIONS: These results show that with the new indicator presented here, a combination of EEG and AEP parameters can be used to differentiate degrees of anesthetic effects over a wide range of hypnosis, from the conscious state to deep anesthesia (i.e., BSP).

Electroencephalographic order pattern analysis for the separation of consciousness and unconsciousness: an analysis of approximate entropy, permutation entropy, recurrence rate, and phase coupling of order recurrence plots.

BACKGROUND: Nonlinear electroencephalographic parameters, e.g., approximate entropy, have been suggested as measures of the hypnotic component of anesthesia. Compared with linear methods, they may detect additional information and quantify the irregularity of a dynamical system. High dimensionality of a signal and disturbances may affect these parameters and change their ability to distinguish consciousness from unconsciousness. Methods of order pattern analysis, in this investigation represented by permutation entropy, recurrence rate, and phase coupling of order recurrence plots, are suitable for any type of time series, whether deterministic or noisy. They may provide a better estimation of the hypnotic component of anesthesia than other nonlinear parameters. METHODS: The current analysis is based on electroencephalographic data from two similar clinical studies in adult patients undergoing general anesthesia with sevoflurane or propofol. The study period was from induction until patients followed command after surgery, including a reduction of the hypnotic agent after tracheal intubation until patients followed command. Prediction probability was calculated to assess the parameter's ability to separate consciousness from unconsciousness at the transition between both states. RESULTS: Parameters of order pattern analysis provide a prediction probability of maximal 0.85 (training study) and 0.78 (evaluation study) with frequencies from 0 to 30 Hz, and maximal 0.87 (training study) and 0.83 (evaluation study) including frequencies up to 70 Hz, both higher than 0.77 (approximate entropy). CONCLUSIONS: Parameters of the nonlinear method order pattern analysis separate consciousness from unconsciousness and are grossly independent of high-frequency components of the electroencephalogram.

Median frequency revisited: an approach to improve a classic spectral electroencephalographic parameter for the separation of consciousness from unconsciousness.

BACKGROUND: In the past, several electroencephalographic parameters have been presented and discussed with regard to their reliability in discerning consciousness from unconsciousness. Some of them, such as the median frequency and spectral edge frequency, are based on classic spectral analysis, and it has been demonstrated that they are of limited capacity in differing consciousness and unconsciousness. METHODS: A generalized approach based on the Fourier transform is presented to improve the performance of electroencephalographic parameters with respect to the separation of consciousness from unconsciousness. Electroencephalographic data from two similar clinical studies (for parameter development and evaluation) in adult patients undergoing general anesthesia with sevoflurane or propofol are used. The study period was from induction of anesthesia until patients followed command after surgery and includes a reduction of the hypnotic agent after tracheal intubation until patients followed command. Prediction probability was calculated to assess the ability of the parameters to separate consciousness from unconsciousness. RESULTS: On the basis of the training set of 40 patients, a new spectral parameter called weighted spectral median frequency was designed, achieving a prediction probability of 0.82 on the basis of the "classic" electroencephalographic frequency range up to 30 Hz. Next, in the evaluation data set, the prediction probability was 0.79, which is higher than the prediction probability of median frequency (0.58) or spectral edge frequency (0.59) and the Bispectral Index (0.68) as calculated from the same data set. CONCLUSIONS: A more general approach of the design of spectral parameters leads to a new electroencephalographic spectral parameter that separates consciousness from unconsciousness significantly better than the Bispectral Index.

Auditory evoked potentials for the assessment of depth of anaesthesia: different configurations of artefact detection algorithms.

Monitoring the depth of anaesthesia has become an important research topic in the field of biosignal processing. Auditory evoked potentials (AEPs) have been shown to be a promising tool for this purpose. Signals recorded in the noisy environment of an operating theatre are often contaminated by artefacts. Thus, artefact detection and elimination in the underlying electroencephalogram (EEG) are mandatory before AEP extraction. Determination of a suitable artefact detection configuration based on EEG data from a clinical study is described. Artefact detection algorithms and an AEP extraction procedure encompassing the artefact detection results are presented. Different configurations of artefact detection algorithms are evaluated using an AEP verification procedure and support vector machines to determine a suitable configuration for the assessment of depth of anaesthesia using AEPs.

NeuMonD: a tool for the development of new indicators of anaesthetic effect.

Electroencephalogram (EEG) signals and auditory evoked potentials (AEPs) have been suggested as a measure of depth of anaesthesia, because they reflect activity of the main target organ of anaesthesia, the brain. The online signal processing module NeuMonD is part of a PC-based development platform for monitoring "depth" of anaesthesia using EEG and AEP data. NeuMonD allows collection of signals from different clinical monitors, and calculation and simultaneous visualisation of several potentially useful parameters indicating "depth" of anaesthesia using different signal processing methods. The main advantage of NeuMonD is the possibility of early evaluation of the performance of parameters or indicators by the anaesthetist in the clinical environment which may accelerate the process of developing new, multiparametric indicators of anaesthetic "depth".

Construction of the electroencephalogram player: a device to present electroencephalogram data to electroencephalogram-based anesthesia monitors.

BACKGROUND: Recently, an increasing number of electroencephalogram (EEG)-based monitors of the hypnotic component of anesthesia has become available. Most of these monitors calculate a numerical index reflecting the hypnotic component of anesthesia. Most of the underlying algorithms are proprietary. Therefore, a quality check or comparison of different indices is very complex. METHODS: Because there is limited information about the algorithms used for index calculation of the different monitors, a reliable comparison or test of the monitors is possible only if the same set of EEG data are presented to each monitor. RESULTS: Parallel EEG monitoring during surgery is limited to two or three monitors because the space for electrode placement on the head is limited. This problem can be solved by using the EEG player to play back recorded EEG data to different monitors. CONCLUSIONS: The output of the player corresponds to the original EEG signal. A comparison of different indices based on identical EEGs is therefore possible. The index reproducibility can also be checked, if the same signal is presented to different monitors.

EEG parameters and their combination as indicators of depth of anaesthesia.

EEG parameters for assessment of depth of anaesthesia are typically based on different signal processing methods, such as spectral and complexity analysis. In the present study, the parameters investigated (WSMF, qWSMF, approximate entropy and Lempel-Ziv complexity) do not correlate monotonically to depth of anaesthesia. To obtain this correlation, parameters are combined based on fuzzy inference, whereby each parameter only operates in a specific range. Fuzzy inference seems to be a suitable approach, as the indicator designed separates wakefulness from unconsciousness as well as the best single parameter does and correlates to the depth of anaesthesia.

Detection of consciousness by electroencephalogram and auditory evoked potentials.

BACKGROUND: A set of electroencephalographic and auditory evoked potential (AEP) parameters should be identified that allows separation of consciousness from unconsciousness (reflected by responsiveness/unresponsiveness to command). METHODS: Forty unpremedicated patients received anesthesia with remifentanil and either sevoflurane or propofol. With remifentanil infusion (0.2 microg . kg . min), patients were asked every 30 s to squeeze the investigator's hand. Sevoflurane or propofol was given until loss of consciousness. After intubation, propofol or sevoflurane was stopped until patients followed the command (return of consciousness). Thereafter, propofol or sevoflurane was started again (loss of consciousness), and surgery was performed. Return of consciousness was observed after surgery. The electroencephalogram and AEP from immediately before and after the transitions were selected. Logistic regression was calculated to identify models for the separation between consciousness and unconsciousness. For the top 10 models, 1,000-fold cross-validation was performed. Backward variable selection was applied to identify a minimal model. Prediction probability was calculated. The digitized electroencephalogram was replayed, and the Bispectral Index was measured and accordingly analyzed. RESULTS: The best full model (prediction probability 0.89) contained 15 AEP and 4 electroencephalographic parameters. The best minimal model (prediction probability 0.87) contained 2 AEP and 2 electroencephalographic parameters (median frequency of the amplitude spectrum from 8-30 Hz and approximate entropy). The prediction probability of the Bispectral Index was 0.737. CONCLUSIONS: A combination of electroencephalographic and AEP parameters can be used to differentiate between consciousness and unconsciousness even in a very challenging data set. The minimal model contains a combination of AEP and electroencephalographic parameters and has a higher prediction probability than Bispectral Index for the separation between consciousness and unconsciousness.

Signal verification of middle latency auditory evoked potentials by automated detection of the brainstem response.

BACKGROUND: The midlatency components of auditory evoked potentials (AEPs) are gradually suppressed with increasing concentrations of anesthetics. Thus, they have been proposed as a monitor of anesthetic depth. However, undetected malfunction or disconnection of headphones and undetected hearing loss also result in suppressed midlatency AEPs that in turn may be misinterpreted as signs of deep anesthesia. As the brainstem component of the AEP is minimally influenced by anesthetics, its presence or absence can be used to verify that the recorded signal is a true AEP rather than an artifact. In this study, an online-capable procedure for detection of the brainstem component of the AEP was developed. METHODS: One hundred and ninety perioperatively recorded AEPs (binaural stimuli, 500 sweeps) were selected from a database with electroencephalographic and concomitant AEP stimulus information. Identical electroencephalogram regions were used to produce nonstimulus synchronized averaged signals (500 sweeps, "non-AEP"). The 190 AEPs and 190 "non-AEPs" were used to develop a detector of the brainstem component of AEPs. AEPs and "non-AEPs" were wavelet transformed (discrete wavelet decomposition, biorthogonal 2.2 mother-wavelet), and the coefficient with the best separation of the two classes of signals was selected. Receiver operating characteristic curve analysis was performed to determine the optimum threshold value for this coefficient. RESULTS: The third coefficient of the third level was selected. In AEP signals, retransform of this coefficient produces a peak that resembles peak V of the brainstem response. The developed detector of the brainstem component of AEP had a sensitivity of 97.90% and a specificity of 99.48%. CONCLUSIONS: This detector of the AEP brainstem component can be used to verify that the signal reflects the response to an auditory stimulus. An alternative approach, used in the Danmeter AEP monitor, is based on the signal-to-noise ratio of the midlatency components of the AEP. Because the midlatency components of AEP are suppressed by anesthesia, a false alarm "low AEP/no AEP" is generated during deep anesthesia. This, in turn, may suggest disconnection of headphones or technical problems whenever anesthesia is deep. This disadvantage has been overcome by our detector, which is based on the identification of the brainstem component of AEP.