WO2008043365A1

WO2008043365A1 - Method and apparatus for assessing the level of nociception during the awake state and general anaesthesia by auditory evoked potentials (aep)

Info

Publication number: WO2008043365A1
Application number: PCT/DK2007/000442
Authority: WO
Inventors: Erik Weber Jesen
Original assignee: Danmeter A/S
Priority date: 2006-10-12
Filing date: 2007-10-25
Publication date: 2008-04-17
Also published as: WO2008043365A8

Abstract

In order to guide the administration of analgesic drugs to a patient under anaesthesia, a method an and apparatus for assessing the level of nociception by calculating an index of nociception based on measurements of Auditory Evoked Potentials (AEP) is applied. Several parameters derived from the AEP signals are used as an input for an Adaptive Neuro Fuzzy Inference System (ANFIS) that calculates an index of nociception represented in a scale from 40 to 0, where a value of 40 means high sensitivity to noxious stimuli while a decreasing index means a lower sensitivity to noxious stimulation.

Description

Method and apparatus for assessing the level of nociception during the awake state end general anaesthesia by auditory evoked potentials (AEP).

This invention relates to a method for assessing the level of nociception during general anaesthesia by measuring the Auditory Evoked Potentials (AEP).

Further the invention relates to an apparatus assessing the level of nociception during general anaesthesia by measuring the Auditory Evoked Potentials (AEP).

In order to ensure patient safety and well-being during surgery, general anaesthesia aims to produce three effects: hypnosis (sleep), analgesia (decreased responses to pain), and muscular relaxation (reduced muscular tone, lack of movement). Accordingly, anaesthetic drugs are administered to the patient in order to achieve each effect. For example, hypnotic drugs such as thiopental and propofol may produce sleep without suppressing movement whereas opioids produce analgesia with only small hypnotic effect. Neuromuscular blocking agents (NMBAs) are administered to achieve muscular relaxation.

Assessment of the anaesthetic depth of a patient requires knowledge of the relationship between the dose of a given anaesthetic agent and its corresponding effect. However, clinical evaluation by the anaesthesiologist by the observation of movements, swallowing, tears, etc. as well as monitoring hemodynamic parameters like blood pressure and heart rate has poor sensitivity and specificity.

In order to objectively measure depth of anaesthesia and improve predictive values, several electronic monitors have been designed based on the analysis of the electroencephalographic (EEG) signal of the anesthetised patient. A number of these have been made available commercially for a number of years.

It has become clear that these devices mainly monitor the alteration of consciousness, which is only one aspect of anaesthesia. Few attempts have been made to develop specific monitors of the balance between painful stimulation and analgesia during anaesthesia and most of these were based on assessing the modulating effect of pain on the autonomic nervous system (pupil response or pulse transit time, for example) or on the occurrence of movement in response to painful stimulation.

However, some drugs used during anaesthesia interfere with autonomic regulation, patients may be receiving non-anaesthetic medications that alter autonomic responses, and neuromuscular blocking agents will attenuate motor responses to painful stimulation. Therefore, a monitor that could assess the perception of pain without relying on movement or responses directly related to the autonomic nervous system would be helpful, as it might help to guide the administration of analgesic drugs to the patient under anaesthesia.

The AEP signal is an evoked electrical activity, embedded in EEG activity that is elicited in a neural pathway by acoustic sensory stimulus provided by a train of acoustic pulses.

The variation on the characteristics of Auditory Evoked Potentials (AEP) has been used to assess the level of consciousness during anaesthesia. Nevertheless, a number of scientific studies indicate that the AEP waveform also exhibits graded variations according to the response to painful stimuli such as the insertion of a laryngeal mask, intubation and surgical incision. The present invention uses selected parameters of the AEP in order to assess nociception during general anaesthesia.

In particular the amplitude of the Middle Latency AEP is correlated to the response to noxious stimuli. Also the variation in the amplitude is correlated to the response to noxious stimuli, therefore the AEPamplitude and the standard deviation of the AEPamplitude (SDAEP) are used as input to a fuzzy logic classifier, for example an Adaptive Neuro Fuzzy Inference System. ANFIS is an acronym for Adaptive Neuro Fuzzy Inference System. ANFIS is one of the first hybrid neuro-fuzzy systems, and was developed by Jang JSR, "ANFIS: Adaptive-Network-Based Fuzzy Inference System, " IEEE Transactions on Systems, Man and Cybernetics, Vol. 23 (3), pp. 665-685, 1993. It represents a Sugeno-type fuzzy system in a special five-layer feed-forward network architecture where the inputs are not counted as a layer. The first order Sugeno fuzzy model was originally proposed by Takagi T, Sugeno M, "Fuzzy identification of systems and its applications to modelling and control, " IEEE Transactions on Systems, Man and Cybernetics, Vol. 15, pp. 116-132, 1985. and further elaborated by Sugeno M, Kang GT, "Structure identification of fuzzy models, " Fuzzy sets and systems, Vol. 28, pp. 15-33, 1988.

The object of the invention is therefore to make it possible to asses nocipitation during general anaesthesia.

The object is achieved in a method according to the invention in

(a) obtaining a signal, containing AEP, recorded from a subject's scalp with three electrodes positioned at middle forehead, left (right) forehead and the left (right) mastoid;

(b) calculating the amplitude of the AEP; (c) defining an index based on the sum of differences of an AEP in a post stimulus window of 80-200ms duration; (d) calculating the standard deviation of the AEPamplitude (SDAEP);

(e) calculating the standard deviation of the index based on the sum of differences of an AEP in a post stimulus window of 80- 200ms duration;

(f) the parameters AEP amplitude, standard deviation of the AEP amplitude, AEP index, standard deviation of the AEP index are used as input to an Adaptive Neuro Fuzzy Inference System (ANFIS) where the output is an index of nociception, termed AEPnoci, represented in a scale from 40 to 0, where a value of 40 means high sensitivity to noxious stimuli while a decreasing index means lower sensitivity to noxious stimulation.

Expedient embodiments of the method are defined in claims 2 - 8.

As mentioned the invention also relates to an apparatus. This apparatus is characterized in a recording device having an input receiving signals from three electrodes placed on a scalp of a patient an amplifier receiving at its input the output from the recording device said ampfliers output is fed to an A/D converter said A/D converters output is fed to a CPU that is adapted to extract at least an EEG signal said EEG signal is in a feature extraction device at its output delivering two parameters namely an AEP amplitude and an SDAEP amplitude from the EEG signal at its output said output is fed to an Adaptive Neuro Interference circuit that is adapted to calculate an assessment of nociception during anaesthesia. The invention will be described more fully below with reference to the accompanying drawing, which shows:

fig. 1 a block diagram of the apparatus according to the invention,

fig. 2 a time course of an AEPnoci for a patient,

fig. 3 a block diagram showing an Adaptive Neuro Inference having five layers including the antecedents end consequents.

As shown in fig. 1 a signal is recorded in a scalp recording device 1 from the scalp 1a of patient and amplified by a high quality amplifier 2 with high Common Mode Rejection Ratio (CMRR). The analogue signal is converted into a digital signal in an A/D converter 3 which can be processed by a CPU 4.

The following sub-signals from the CPU that are subtracted from the recorded signal, are a EEG 5, EMG 6 and AEP 7 signal. It is noted that a method to monitor AEP signals is disclosed in the published International patent application no. WO 01/74248. According to this published application it is possible within a very short time to measure a reliable AEP signal which is calculated from an autoregressive model with exogenous input. A practical measuring apparatus for carrying out measurements of AEP signals is described in WO 02/071550 A1. From the AEP a feature extraction is carried in an extraction device 8 that produces two parameters, an AEP-amplitude 9 and the standard deviation of the AEP-amplitude, termed SDAEPamplitude 10. These two parameters are fed into the ANFIS inference system 11 , which generates the final output, the index termed AEPnoci 11. A number of other parameters can be derived from the AEP, such as absolute sum of differences, amplitude of the AEP, peak latencies, those can used as additional input for a fuzzy logic system that defines the output, which is the index of nociception.

It is also noted that the parameters EEG 5 and EMG 6 derived from the CPU 4 are optional and can be used for other purposes.

Fig. 2 represents the time course of an AEPnoci for one of a patient and the randomly sampled values of the Ramsay score the dots in the bottom of the figure.

Now in connection with fig. 3 it will be explained how the Adaptive Nero Interference circuit functions.

The five layers of ANFIS, shown in figure 3, have the following functions:

• Each unit in Layer 1 stores three parameters to define a bell-shaped membership function. Each unit is connected to exactly one input unit and computes the membership degree of the input value obtained.

• Each rule is represented by one unit in Layer 2. Each unit is connected to those units in the previous layer, which are from the antecedent of the rule. The inputs into a unit are degrees of membership, which are multiplied to determine the degree of fulfilment for the rule represented.

• In Layer 3, for each rule there is a unit that computes its relative degree of fulfilment by means of a normalisation equation. Each unit is connected to all the rule units in Layer 2.

• The units of Layer 4 are connected to all input units and to exactly one unit in Layer 3. Each unit computes the output of a rule.

• An output unit in Layer 5 computes the final output by summing all the outputs from Layer 4.

Standard learning procedures from neural network theory are applied in ANFIS. Back-propagation is used to learn the antecedent parameters, i.e. the membership functions, and least squares estimation is used to determine the coefficients of the linear combinations in the rules' consequents. A step in the learning procedure has two passes. In the first pass, the forward pass, the input patterns are propagated, and the optimal consequent parameters are estimated by an iterative least mean squares procedure, while the antecedent parameters are fixed for the current cycle through the training set. In the second pass, the backward pass, the patterns are propagated again, and in this pass back-propagation is used to modify the antecedent parameters, while the consequent parameters remain fixed. This procedure is then iterated through the desired number of epochs. If the antecedent parameters initially are chosen appropriately, based on expert knowledge, one epoch is often sufficient as the LMS algorithm determines the optimal consequent parameters in one pass and if the antecedents do not change significantly by use of the gradient descent method, neither will the LMS calculation of the consequents lead to another result. For example in a 2-input, 2-rule system, rule 1 is defined by

where p, q and r are linear, termed consequent parameters or only consequents. Most common is f of first order as higher order Sugeno fuzzy models introduce great complexity with little obvious merit.

Example

The assessment of nociception during anaesthesia might be useful for the administration of analgesics assuring that patients receive an adequate protection to prevent response against noxious stimuli during surgery or other procedures. Several articles, mostly the work by Thornton et al in the 1980'es and 1990'es, showed that the amplitude of the AEP correlates to the response to noxious stimuli while the latencies of the Pa and Pb peaks correlate to the level of hypnosis, see Thornton C et al, The effects of halothane and enflurane with controlled ventilation on auditory evoked potentials. British Journal of Anesthesia 1984;56:315-323.

In a recent work Henneberg SW et al. Peroperative depth of anaesthesia may influence postoperative opioid requirements. Acta Anaesthesiol Scand. 2005

Mar;49(3):293-6, showed that the postoperative opioid consumption was related to the fluctuations of the AAI. AAI is a hybrid "depth of hypnosis" index using both

EEG and AEP parameters. Based on these findings, a new index of nociception has been designed, AEPnoci, integrating the maximal amplitude of the AEP and the Standard Deviation of the AEP amplitude (SDAEP). The AEPnoci was defined by feeding the two AEP derived parameters into an Adaptive Neuro

Fuzzy Inference System (ANFIS), where the output was the AEPnoci.

Under IRB approval and written informed consent, 35 patients undergoing ultrasonographic endoscopy (USE) were randomly assigned to receive a fixed concentration of either propofol (0, 1.5, 2, 3 mcg/mL) or remifentanil (0, 0.5, 1 , 2 ng/mL) while the other drug was allowed to change depending on the clinical requirements. AEP and AAI/2, (Danmeter A/S) were recorded online (Rugloop II) and stored pending analysis. BIS, Skin Conductance and hemodynamic parameters were recorded as well. A TCI system targeting the effect site was used to administer propofol and remifentanil. The Ramsay Sedation Score (RSS) was assessed at random time periods. The RSS is a clinical scale for assessing the level of sedation where levels 1 to 3 corresponds to awake, while 4 to 6 indicates deeper sedation. A noxious stimulus, strong pressure in the nailbed of the patient, was applied to discriminate between RSS 5 and 6. The prediction probability (Pk) is a measure of association between the clinical scale, here the Ramsay scale, and the electronic index. A Pk of 1 means that the index can make a perfect prediction of the Ramsay value, while a Pk of 0.5 means that the method is not better than tossing a coin. Results.

The results of the Pk analysis are shown in the table 1.

Table 1. The Pk mean (SE) value of a 10 s interval before the noxious stimulus.

In particular the amplitude of the Middle Latency AEP is correlated to the response to noxious stimuli. Also the variation in the amplitude is correlated to the response to noxious stimuli, therefore the AEPampiitude and the standard deviation of the AEPampiitude (SDAEP) are used as input to a fuzzy logic classifier, for example an Adaptive Neuro Fuzzy Inference System. (ANFIS)

ANFIS is an acronym for Adaptive Neuro Fuzzy Inference System. ANFIS is one of the first hybrid neuro-fuzzy systems, and was developed by Jang JSR, "ANFIS: Adaptive-Network-Based Fuzzy Inference System, " IEEE Transactions on Systems, Man and Cybernetics, Vol. 23 (3), pp. 665-685, 1993. It represents a Sugeno-type fuzzy system in a special five-layer feed-forward network architecture where the inputs are not counted as a layer. The first order Sugeno fuzzy model was originally proposed by Takagi T, Sugeno M, "Fuzzy identification of systems and its applications to modelling and control, " IEEE Transactions on Systems, Man and Cybernetics, Vol. 15, pp. 116-132, 1985. and further elaborated by Sugeno M, Kang GT, "Structure identification of fuzzy models, " Fuzzy sets and systems, Vol. 28, pp. 15-33, 1988. For the sake of completeness the following references to the patent literature and other references are listed below.

US patents:

Other literature references:

V. Bonhomme*, V. Llabres, P.-Y. Dewandre, J. F. Brichant and P. Hans Combined use of Bispectral IndexTM and A-LineTM Autoregressive IndexTM to assess anti-nociceptive component of balanced anaesthesia during lumbar arthrodesis. Br. J. Anaesth., March 2006; 96: 353 - 360. Thornton C et al. The effects of halothane and enflurane with controlled Ventilation on auditory evoked potentials. British Journal of Anesthesia 1984;56:315-323

Nishiyama T, Matsukawa T, Hanaoka K. Is the ARX index a more sensitive indicator of anesthetic depth than the bispectral index during sevoflurane/ nitrous oxide anaesthesia? Acta Anaesthesiol Scand 2004; 48: 1028—1032

Struys MM, Vereecke H, Moerman A et al. Ability of the bispectral index, utoregressive modelling with exogenous input-derived auditory evoked potentials, and predicted propofol concentrations to measure patient responsiveness during anaesthesia with propofol and remifentanil. Anesthesiology 2003; 99: 802—12.

Thornton C et al, The effects of halothane and enflurane with controlled ventilation on auditory evoked potentials. British Journal of Anesthesia 1984;56:315-323

Henneberg SW et al. Peroperative depth of anaesthesia may influence postoperative opioid requirements. Acta Anaesthesiol Scand. 2005 Mar;49(3):293-6.

Claims

1. A method for assessing the level of nociception during general anaesthesia by measuring the Auditory Evoked Potentials (AEP),

characterized by the following steps:

(g) obtaining a signal, containing AEP, recorded from a subject's scalp with three electrodes positioned at middle forehead, left (right) forehead and the left (right) mastoid;

(h) calculating the amplitude of the AEP; (i) defining an index based on the sum of differences of an AEP in a post stimulus window of 80-200ms duration; (j) calculating the standard deviation of the AEPamplitude (SDAEP);

(k) calculating the standard deviation of the index based on the sum of differences of an AEP in a post stimulus window of 80- 200ms duration;

(I) the parameters AEP amplitude, standard deviation of the AEP amplitude, AEP index, standard deviation of the AEP index are used as input to an Adaptive Neuro Fuzzy Inference System (ANFIS) where the output is an index of nociception, termed AEPnoci, represented in a scale from 40 to 0, where a value of 40 means high sensitivity to noxious stimuli while a decreasing index means lower sensitivity to noxious stimulation.

2. The method according to claim 1 wherein step (a) is further defined as the position of the electrodes can be either middle forehead (Fp, in the international 10-20 electrode positioning system), left forehead (F7) and the left mastoid or the electrode position can alternatively be middle forehead, right forehead and the right mastoid.

3. The method according to claim 1 wherein step (b) is further refined by calculating, over a 0-100 ms window after the acoustic stimulus, the difference between the minimum and maximum value defining then the amplitude.

4. The method according to claim 1 where step (c) is further elaborated as the absolute sum of differences between consecutive samples starting at first sample after the acoustic stimulus until the sample corresponding to a window length between 80 and 200 ms after stimulus.

5. The method according to claim 1 wherein step (d) is further defined as the standard deviation of the AEP calculated in a 10 s window.

6. The methods according to claims 1-5 wherein variables derived from the AEP are used for defining the AEPnoci, some of the variables or all are included in an Adaptive Neuro Fuzzy Inference System, where the output of the discriminatory function is the AEPnoci, an index describing the response of the patient to noxious stimuli.

7. The method according to claim 1- 6 wherein a Signal to Noise Ratio assessment is further defined as the sum of the synchronized average of the AEP divided by randomly averaged sweeps of the same duration.

8. The method according to claim 1 - 7 where click stimulus is provided to the patients scalp through wireless radio communication.

9. The methods according to claims 8 wherein the Signal to Noise Ratio is used to control the intensity of the click stimulus.

10. Apparatus assessing the level of nociception during general anaesthesia by measuring the Auditory Evoked Potentials (AEP) characterized in

a recording device (1) having an input receiving signals from three electrodes placed on a scalp of a patient an amplifier (2) receiving at its input the output from the recording device said ampfliers output is fed to an A/D converter (3) said A/D converters output is fed to a CPU (4) that is adapted to extract at least an EEG signal said EEG signal is in a feature extraction device (8) at its output delivering two parameters namely an AEP amplitude and an SDAEP amplitude from the EEG signal at its output said output is fed to an Adaptive Neuro Interference circuit (11) that is adapted to calculate an assessment of nociception during anaesthesia.