The use of fuzzy integrals and bispectral analysis of the electroencephalogram to predict movement under anesthesia

Control of sevoflurane anesthetic agent via neural network using electroencephalogram signals during anesthesia

In this study, power spectrum of the EEG data and the heartbeat data obtained from 250 patients has been applied to the designed Neural network system. A backpropagation artificial neural network has been developed which contains 53 nodes in the input layer, 27 nodes in the hidden and 1 node in the output layer. In the artificial neural network inputs, the power spectral density values corresponding 1-50 Hz frequency interval of the EEG slices which has 10 seconds of time interval, the ratio of the total of the PSD values of current EEG slice to the total PSD values of EEG slice of pre-anesthesia, the ratio of the total PSD values of the EEG data to the total PSD values of the previous EEG data, and the previous anaesthetic gas ratio values have been applied and the network has been educated. The designed neural network system has been tested by using 10 data set obtained from 4 different patients. In the anesthetic gas prediction according to the anesthesia level, successful results have been obtained with the designed system. The system has been able to correctly purposeful responses in average accuracy of 94% of the cases. This method is also computationally fast and acceptable real-time clinical performance has been obtained.

Estimation of medicine amount used anesthesia by an artificial neural network

In this study, Elman's recurrent neural networks using Resilient Back Propagation (RP) algorithm and feed-forward neural networks using adaptive learning rate algorithm (gdx) have been compared in order to determine the depth of anesthesia in the continuation stage of anesthesia and to estimate the amount of medicine to be applied at that moment. EEG data have been recorded by being sampled once in every 2 ms. From 30 patients, 57 distinct EEG recordings have been collected prior to during anaesthesia of different levels. The applied artificial neural network is composed of three layers, namely the input layer, the middle layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. Prediction has been made by means of ANN. Training and testing the ANN have been used previous anaesthesia amount, total power/normal power and total power/previous. When Elman Resilient BP and feed-forward network are compared, it is observed that resilient back propagation algorithm has generated values which are quite close to the applied anesthesia amount compared to gdx which is an adaptive learning algorithm. The system has been able to correctly purposeful responses in average accuracy of 95% of the cases. This method is also computationally fast and acceptable real-time clinical performance has been obtained.

Determining the amount of anesthetic medicine to be applied by using Elman's recurrent neural networks via resilient back propagation

In this study, Elman recurrent neural networks have been defined by using Resilient Back Propagation in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. From 30 patients, 57 distinct EEG recordings have been collected prior to during anaesthesia of different levels. The applied artificial neural network is composed of three layers, namely the input layer, the middle layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. Prediction has been made by means of ANN. Training and testing the ANN have been used previous anaesthesia amount, total power/normal power and total power/previous. The system has been able to correctly purposeful responses in average accuracy of 95% of the cases. This method is also computationally fast and acceptable real-time clinical performance has been obtained.

Using Elman recurrent neural networks with conjugate gradient algorithm in determining the anesthetic the amount of anesthetic medicine to be applied

In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.

Automated detection of anesthetic depth levels using chaotic features with artificial neural networks

Monitoring the depth of anesthesia (DOA) during surgery is very important in order to avoid patients' interoperative awareness. Since the traditional methods of assessing DOA which involve monitoring the heart rate, pupil size, sweating etc, may vary from patient to patient depending on the type of surgery and the type of drug administered, modern methods based on electroencephalogram (EEG) are preferred. EEG being a nonlinear signal, it is appropriate to use nonlinear chaotic parameters to identify the anesthetic depth levels. This paper discusses an automated detection method of anesthetic depth levels based on EEG recordings using non-linear chaotic features and neural network classifiers. Three nonlinear parameters, namely, correlation dimension (CD), Lyapunov exponent (LE) and Hurst exponent (HE) are used as features and two neural network models, namely, multi-layer perceptron network (feed forward model) and Elman network (feedback model) are used for classification. The neural network models are trained and tested with single and multiple features derived from chaotic parameters and the performances are evaluated in terms of sensitivity, specificity and overall accuracy. It is found from the experimental results that the Lyapunov exponent feature with Elman network yields an overall accuracy of 99% in detecting the anesthetic depth levels.

Design of ECG-based anaesthesia monitor/pain monitor

Design of Anaesthesia monitor/ Pain monitor, using ECG signal only, is presented in this paper. During surgical operations, ECG is picked-up as a routine procedure; the same ECG is used here, hence it is almost noninvasive. Initially heart rate variability (HRV) spectrum is obtained from R-R intervals (RRIs) and the respiratory peak is identified. Following the contour of this peak, a bandpass filter is designed using frequency sampling technique. The entire RRIs is then passed through this filter. The output contains the information about the respiratory cycles only and RSA (respiratory sinus arrhythmia) is obtained from it. It is assumed that, level of consciousness is proportional to RSA. Relaxation in the muscle is taken as overall relaxation of the body and parasympathetic dominance, as a measure of it, is obtained from the ratio of power in the respiratory and baroreceptor peaks. It is seen that the same instrument may be used as pain-monitor because unbalancing in the autonomic system (ANS) is also created during pain. A total of three dozen patients were tested, two dozens with anaesthetic drugs and the rest with thermal probe creating erythema. The results, in both the cases, are close to the expectation of the anaesthetists.

Classifying depth of anesthesia using EEG features, a comparison

Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to find the excellent features or combination of them than can discriminate between different anesthesia states. Conducting a clinical study on 22 patients we could define 4 distinct anesthetic states: awake, moderate, general anesthesia, and isoelectric. We examined features that have been used in earlier studies using single-channel EEG signal processing method. The maximum accuracy (99.02%) achieved using approximate entropy as the feature. Some other features could well discriminate a particular state of anesthesia. We could completely classify the patterns by means of 3 features and Bayesian classifier.

Beat-by-beat cardiovascular index to predict unexpected intraoperative movement in anesthetized unparalyzed patients: a retrospective analysis

OBJECTIVE

Unexpected intraoperative movement may be detrimental during delicate surgery. This study tested retrospectively an algorithm based on beat-by-beat circulatory variables (incorporated into a Cardiovascular depth of anesthesia index: CARDEAN in relationship to unexpected movement, and compared its performance to that of the electroencephalogram (EEG)-derived index: BIS-XP 4.0.

METHODS

40 ASA I or II patients presenting for knee surgery had EEG (BIS XP 4.0), beat-by-beat (Finapres) finger non-invasive blood pressure (BP), conventional brachial BP and electrocardiogram (EKG) monitors attached. Anesthesia was induced and maintained with propofol and remifentanil. Before incision, the propofol concentration was set to maintain BIS < 60. From incision to emergence, the anesthesiologist was denied access to BIS or Finapres. Anesthesia adjustment was titrated at the discretion of the anesthesiologist according to conventional signs only: brachial BP, EKG, eyelash reflex, movement. Occurrences of movement and eye signs (divergence of eyeballs, tears, corneal reflex, eyelash reflex) were observed. The CARDEAN algorithm was written retrospectively and tested vs. BIS.

RESULTS

11 movements occurred in 8 patients. CARDEAN > 60 predicted movement in 30% of the cases, 15 to 274 s before movement (sensitivity: 100%, specificity: 95%; relative operating curve ROC = 0.98; prediction probability pk = 0.98). BIS > 60 predicted movement in 19% of cases (sensitivity: 64%; specificity: 94%, ROC: 0.85, pk: 0.85).

CONCLUSION

Retrospectively, a cardiovascular index predicted unexpected intraoperative movements. Prospective validation is needed.

Using EEG to monitor anesthesia drug effects during surgery

The use of processed electroencephalography (EEG) using a simple frontal lead system has been made available for assessing the impact of anesthetic medications during surgery. This review discusses the basic principles behind these devices. The foundations of anesthesia monitoring rest on the observations of Guedel with ether that the depth of anesthesia relates to the cortical, brainstem and spinal effects of the anesthetic agents. Anesthesiologists strive to have a patient who is immobile, is unconscious, is hemodynamically stable and who has no intraoperative awareness or recall. These anesthetic management principles apply today, despite the absence of ether from the available anesthetic medications. The use of the EEG as a supplement to the usual monitoring techniques rests on the observation that anesthetic medications all alter the synaptic function which produces the EEG. Frontal EEG can be viewed as a surrogate for the drug effects on the entire central nervous system (CNS). Using mathematical processing techniques, commercial EEG devices create an index usually between 0 and 100 to characterize this drug effect. Critical aspects of memory formation occur in the frontal lobes making EEG monitoring in this area a possible method to assess risk of recall. Integration of processed EEG monitoring into anesthetic management is evolving and its ability to characterize all of the anesthetic effects on the CNS (in particular awareness and recall) and improve decision making is under study.

An effective correlation dimension and burst suppression ratio of the EEG in rat. Correlation with sevoflurane induced anaesthetic depth

BACKGROUND AND OBJECTIVE

Anaesthesiologists need parameters that measure the depth of anaesthesia. In the context of this need, the present study investigated in rats how two variables from the electroencephalogram, the burst suppression ratio and effective correlation dimension correlated with a measure of anaesthetic depth as measured in the strength of a noxious withdrawal reflex.

METHODS

Eight rats were exposed to different inspiratory concentrations of sevoflurane, each rat in two separate experiments. In the first experiment, spontaneously breathing animals could move freely and no painful stimuli were applied. In the second experiment, in mechanically ventilated restrained anaesthetized rats, the withdrawal reflex was measured every 80 s. In both experiments the electroencephalogram was continuously recorded. The concentration in the effector compartment was estimated using a first order two compartment model. Correlation dimension was computed following the Grassberger/Procaccia/Takens approach with optimized parameter settings to achieve maximum sensitivity to anaesthetic drug effects and enable real-time computation. The Hill, equation was fitted to the data, describing the effect as a function of sevoflurane concentration.

RESULTS

Good correlations of Depth of Anaesthesia with correlation dimension as well as burst suppression ratio were established in both types of experiments. Arousal by noxious stimuli decreased burst suppression ratio and increased correlation dimension. The effective sevoflurane concentration associated with 50% of the maximum effect (C50) was higher in experiment II (stimulation) than in experiment I (no stimulation): i.e. for correlation dimension 2.18% vs. 0.60% and for burst suppression ratio 3.07% vs. 1.73%. The slope factors were: gammaCD = 4.15 vs. gammaCD = 1.73 and gammaBSR = 5.2 vs. gammaBSR = 5.4. Correlation dimension and burst suppression ratio both correlated with the strength of the withdrawal reflex with correlation coefficients of 0.46 and 0.66 respectively (P < 0.001).

CONCLUSIONS

Both correlation dimension and burst suppression ratio are related to anaesthetic depth and are affected by noxious stimuli. The relationship between anaesthetic depth and burst suppression ratio is confirmed and the potential of correlation dimension is demonstrated.

Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia

Based on an adaptive genetic fuzzy clustering algorithm, a derived fuzzy knowledge model is proposed for quantitatively estimating the systolic arterial pressure (SAP), heart rate (HR), and bispectral index (BIS) using 12 patients and it validates them according to pharmacological reasoning. Also, a genetic proportional integral derivative controller (GPIDC) to adaptive three controller parameters and a genetic fuzzy logic controller (GFLC) to adaptive controller rules using genetic algorithms (GAs) were simulated and compared each other in a patient model using the BIS value as a controlled variable. Each controller was tested using a set of 12 virtual patients undergoing a Gaussian random surgical disturbance repeated with BIS targets set at 40, 50, and 60. Controller performance was assessed using mean absolute error (MAE) of the BIS target, the percentage of time with acceptable BIS control (PTABC), and drug consumption (DC). It was found that the MAE value of the BIS target was significantly lower (P < 0.05) and the values of PTABC and DC of BIS target were significantly higher (P < 0.05) in BIS targets set at 40 than at 50 or 60 in both GPIDC and GFLC. However, when compared with two controllers in terms of the values of MAE, PTABC, and DC each other in BIS targets set at 40, 50, and 60, there were no significant differences (P > 0.05). Furthermore, when the simulation results in these two controllers were compared with routine standard practice of 12 clinical trials (i.e., manual control) in BIS target set at 50, the values of PTABC in both GPIDC and GFLC groups were significantly higher (P < 0.05) than in the manual control group. In contrast, there were no significant differences (P > 0.05) for these three groups in terms of drug consumption. This indicates that either GPIDC or GFLC can control the BIS target set at 50 better than manual control, although the similar drug consumption is used.

Prediction of movement following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia by means of middle latency auditory evoked responses

This paper investigates the applicability of generalized dynamic neural networks for the design of a two-valued anesthetic depth indicator during isoflurane/nitrous oxide anesthesia. The indicator construction is based on the processing of middle latency auditory evoked responses (MLAER) in combination with the observation of the patient's movement reaction to skin incision. The framework of generalized dynamic neural networks does not require any data preprocessing, visual data inspection or subjective feature extraction. The study is based on a data set of 106 patients scheduled for elective surgery under isoflurane/nitrous oxide anesthesia. The processing of the measured MLAER is performed by a recurrent neural network that transforms the MLAER signals into signals having a very uncomplex structure. The evaluation of these signals is self-evident, and yields to a simple threshold classifier. Using only evoked potentials before the pain stimulus, the patient's reaction could be predicted with a probability of 81.5%. The MLAER is closely associated to the patient's reaction to skin incision following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia. In combination with other parameters, MLAER could contribute to an objective and trustworthy movement prediction to noxious stimulation.

Electroencephalogram processing using neural networks

The electroencephalogram (EEG), a highly complex signal, is one of the most common sources of information used to study brain function and neurological disorders. More than 100 current neural network applications dedicated to EEG processing are presented. Works are categorized according to their objective (sleep analysis, monitoring anesthesia depth, brain-computer interface, EEG artifact detection, EEG source-based localization, etc.). Each application involves a specific approach (long-term analysis or short-term EEG segment analysis, real-time or time delayed processing, single or multiple EEG-channel analysis, etc.), for which neural networks were generally successful. The promising performances observed are demonstrative of the efficiency and efficacy of systems developed. This review can aid researchers, clinicians and implementors to understand up-to-date interest in neural network tools for EEG processing. The extended bibliography provides a database to assist in possible new concepts and idea development.

Monitoring anesthesia using neural networks: a survey

New methods of data processing combined with advances in computer technology have revolutionized monitoring of patients under anesthesia. The development of systems based on analysis of brain electrical activity (EEG or evoked potentials) by neural networks has provided impetus to many investigators. Though not claiming to be the end-all in patient monitoring, the potential and efficiency of the combination does indeed stand out. Various strategies are presented and discussed, as well as suggestions for further investigation.

EEG complexity as a measure of depth of anesthesia for patients

A new approach for quantifying the relationship between brain activity patterns and depth of anesthesia (DOA) is presented by analyzing the spatio-temporal patterns in the electroencephalogram (EEG) using Lempel-Ziv complexity analysis. Twenty-seven patients undergoing vascular surgery were studied under general anesthesia with sevoflurane, isoflurane, propofol, or desflurane. The EEG was recorded continuously during the procedure and patients' anesthesia states were assessed according to the responsiveness component of the observer's assessment of alertness/sedation (OAA/S) score. An OAA/S score of zero or one was considered asleep and two or greater was considered awake. Complexity of the EEG was quantitatively estimated by the measure C(n), whose performance in discriminating awake and asleep states was analyzed by statistics for different anesthetic techniques and different patient populations. Compared with other measures, such as approximate entropy, spectral entropy, and median frequency, C(n) not only demonstrates better performance (93% accuracy) across all of the patients, but also is an easier algorithm to implement for real-time use. The study shows that C(n) is a very useful and promising EEG-derived parameter for characterizing the (DOA) under clinical situations.