1Dipartimento Area Critica Medico Chirurgica, Sez. di Anestesiologia e Rianimazione, Università di Firenze, Italy. 2Dipartimento Di Patologia e Biologia Molecolare e Cellulare, Universita Federico II, Napoli, Italy. 3Dipartimento di Sistemi e Informatica, Universita di Firenze, Italy. 4Dipartimento di Ingegneria dellInformazione, Universita di Siena, Italy. 5Department of Engineering Science, University of Oxford, Oxford, UK*Corresponding author: Via Carducci 42, I-80121 Naples, Italy
Accepted for publication: January 3, 2002
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Abstract |
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Methods. Two hundred patients, who underwent general abdominal surgery, were recruited for our trial. For anaesthesia we used a total i.v. technique, tracheal intubation, and artificial ventilation. Fourteen EEG variables, including the BIS, were extracted from the EEG, monitored with an EEG computerized monitor, and then stored on a computer. Data from 150 patients were used to train the neural network. All the variables, excluding the BIS, were used as input data in the neural network. The output targets of the network were provided by anaesthesia scores ranging from 10 to 100 assigned by the anaesthesiologist according to the observers assessment of alertness and sedation (OAA/S) and other clinical means of assessing depth of anaesthesia. Data from the other 50 patients were used to test the model and for statistical analysis.
Results. The artificial neural network was successfully trained to predict an anaesthesia depth index, the NED (neural network evaluated depth), ranging from 0 to 100. The correlation coefficient between the NED and the BIS over the test set was 0.94 (P<0.0001).
Conclusion. We have developed a neural network model, which evaluates 13 processed EEG parameters to produce an index of anaesthesia depth, which correlates very well with the BIS during total i.v. anaesthesia with propofol.
Br J Anaesth 2002; 88: 6448
Keywords: anaesthesia, depth; anaesthetics i.v., propofol; analgesics opioid, remifentanil; brain, electroencephalgraphy; monitoring, bispectral index; monitoring, electroencephalography
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Introduction |
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In this study we aimed to use an artificial neural network (ANN) to integrate different processed EEG variables in order to estimate the depth of anaesthesia. ANNs can provide models of non-linear or complex systems such as EEG signals where the informative content and data associations are too complex to be extracted by traditional algorithms. The index of anaesthetic depth we derived was compared with the BIS.
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Patients and methods |
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Anaesthesia procedure
Patients received ringer-lactate 700 ml and atropine 0.01 mg kg1 as pre-medication. Anaesthesia was induced using a total i.v. technique: remifentanil 15 µg kg1 h1 (no initial bolus) and propofol 1.5 mg kg1 as a bolus, followed by an infusion at 10 mg kg1 h1, pancuronium 0.1 mg kg1. The trachea was intubated and the lungs mechanically ventilated with oxygen/air (FIO2=0.4). The amounts of remifentanil and propofol administered were monitored during anaesthesia and varied as needed according to the BIS values and the anaesthestists clinical evaluation. Patients who needed an adjustment of more than 10% from the initial infusion rates were not included in the trial.
EEG monitoring and data acquisition
Fourteen EEG variables were extracted from the EEG (Table 1) and monitored with an Aspect A-1000 EEG monitor (Aspect Medical Systems, Natick, USA) using four Zipprep self-sticking frontal surface electrodes placed on both sides of the outer malar bone (At1 and At2) with Fpz as reference and Fp1 as the ground. Every 5 s the Aspect A-1000 calculated the BIS and the other EEG variables based on a running average of the last 120 artefact-free epochs of data (each epoch represents 0.5 s). Data collected during the whole anaesthesia procedure at 5 s intervals, starting 3 min before the induction and ending when the patient was completely awake, were automatically sent through a serial RS232 interface to a computer (Toshiba, Satellite 4000 CDT) using in-home software.
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The analysis of the ANN performance on the training set led us to choose a final multi-layer perceptron (MLP) network with 13 input nodes, one hidden layer with 18 nodes, and one output node. The network was trained using the standard back-propagation method.7 The network learning rate was 0.38 and the momentum was 0.8. The NeuralSIM software took 10 h to optimize the network, and the ANN was then trained for 17 h. Training was stopped when the average absolute error was less than 4 and the root mean square error was less than 5. The resulting neural network evaluated depth (NED) ranged from 0 to 100. During training the ANN calculated the correlation coefficient between the target outputs and the corresponding predicted values (NED) produced by the network. The correlation coefficient of the final network (Table 4) shows that the ANN was satisfactorily trained.3
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Results |
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Discussion |
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Our experiments show that neural networks can be used to analyse processed EEG data to provide a depth of anaesthesia index as informative as the BIS, during total i.v. anaesthesia with propofol.
The BIS may also prove not robust enough when artefactual signals are present. BIS alterations have been found with pacers during cardiac surgery,9 with some electrical blankets10 and, in eye surgery, a sudden increase of the BIS may be observed when some vitrectomy coagulators are used. Automated recognition systems based on autoregressive modelling and neural network analysis of the EEG signals can, in theory, be trained to recognize and filter noise artefacts. Experiments confirm that neural network analysis of the EEG achieves good discrimination between awake and anaesthetized states both in i.v. anaesthesia and in anaesthesia with volatile agents with a good rejection of artefactual signals.1113 The authors conclude that the flexibility and non-linearity of an ANN approach are important factors providing reliability to a monitoring device for depth of anaesthesia. Naturally a good performance of an ANN highly depends on the quality of training.
Statistical evaluation of our data demonstrates that the Pearsons correlation coefficient r calculated between NED and BIS is higher than the r coefficient obtained with the multiple linear regression model by correlating the 13 EEG variables (used to calculate the NED) with the BIS. This is an obvious indication that the performance of the neural model is better because the correlation between the EEG processed data and the BIS are not completely linear.
In conclusion, we have developed a neural network based system that can evaluate the depth of anaesthesia from 13 processed EEG variables (excluding BIS). Comparison with correspondent BIS values confirms that neural networks can deal with the data from processed fronto-parietal EEG, where phase information is lost, producing evaluations on the depth of anaesthesia, such as the NED, that can perform as well as the BIS in accuracy and reliability during total i.v. anaesthesia with propofol. Neural network performance may be improved further by using bispectral analysis data.
Finally, we have to consider that the information included in the analysis of the processed EEG is contained in the raw EEG signal. A neural network could, therefore, be trained to predict anaesthesia depth directly from the raw EEG. Successful experiments along these line have been performed by using neural network technology associated with autoregressive models and stochastic complexity measures.14 15
We regard our initial results as extremely encouraging for the evaluation and control of the depth of anaesthesia, both in open and closed loop systems, via neural network based EEG analysis. Trials are in progress to extend the NED evaluation to other anaesthesia regimens.
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References |
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