Assessment of the Depth of Anaesthesia in animals for

Assessment of the Depth of Anaesthesia in animalsfor automatic control

B. A. Costaand R. Figueiredoand J. M. Lemos

INESC-ID/IST/TU of LisbonR. Alves Redol 9, 1000-029 Lisboa Portugal

Email: [email protected]: [email protected]

A. Silvaand S. Camposand L. Antunes

CECAV-UTAD, Vila Real, PortugalEmail: [email protected]

Email: [email protected]

Abstract—This paper address the problem of designing asystem for automatic assessment of the Depth of Anesthesia (DoA)for general surgery in veterinary medicine. The aim is to developa DoA sensor that coupled with an automatic control methodol-ogy may bring the benefits of automation to veterinary medicine,to relief the veterinary surgeon from routine activities due to thehis/her dual role of surgeon and anaesthetist. At the core ofan automatic DoA assessment system are mathematical methodsused to extract features from the electroencephalogram (EEG)signal which are mapped into an index. These mathematicalmethods may use frequency/time domain analysis to evaluate thechange in the EEG signal such as frequency shift and amplitudechange. Other methods use a probabilistic approach to measurethe change of complexity in the EEG signal, such as, PermutationEntropy (PE) and Shannon Entropy (ShEn). The method that isevaluated in based on the concept of computing the mean powerof EEG frequency bands as in human anesthesia.

Keywords: Depth of Anesthesia, Electoencephalogram, Auto-matic Control

I. INTRODUCTION

Modern medicine is characterized by the intensive useof computerized equipments that help medical personal toassess and to improvement the life quality of patients, duringdiagnostic tests, or to support vital functions of patients incritical situations.

The practice of human general surgery and general anaes-thesia are examples where critical decisions are executed usingcomputerized equipments. The anaesthetist uses monitors,indexes and their trends to evaluate the state of the patientand to decide on the administration of drugs, to control thelevel of hypnosis (loss of consciousness), sedation (pain relief)and neuromuscular blockade (loss of muscle control) such thatthe surgery may be safely executed.

In human general anaesthesia several monitors are used toobtain a better assessment of the patient state, such as theneuromuscular blockade level (NMB) index [1], and the Depthof Anesthesia (DoA) indexes, the BIS [2], the Narcotrend

This work was supported by the FCT (INESC-ID multiannual funding)thought the PIDAC and by the research projects IDEA - Integrated Designfor Automation of Anaesthesia, reference - PTDC/EEA-ACR/69288/2006,and Galeno - Modeling and Control for personalized drug administration,reference - PTDC/SAU-BEB/103667/2008.

[3] and the PSI [4]. Ethical and economical factors justifythe development and improvement of DoA monitors [5].They allow the automation of anaesthesia [6] and that maycontribute to a better assessment of the patient state duringsurgery, to improve the administration of drugs by avoidingoverdoses due to uncertainty about the patient state, to reducethe toxicity side effects and have a better postoperative recover,and to decrease the costs and the workload of the surgery team.

The practice of animal anaesthesia is a huge area that hassimilar problems and aims as human anaesthesia, but with ahigher risk of mortality [7], with the additional factor thatit is used as a research test ground for the development oftechniques, procedures and new drugs that may be transferredto human medicine. However in animal anaesthesia, it isnot a current practice to use DoA monitors, in part becausethey are expensive and are calibrated for human anaesthesia.The monitor IOC [8] is a commercial monitor for humananaesthesia that is being tested in animal anaesthesia. Thesetwo points motivate this work which is being developed in theIDEA project framework.

This paper describes the work and the results obtainedwith a method that is based on the concept of computingthe mean power of EEG frequency bands as in humananesthesia but applied to EEG data obtained from a rabbit.After this introduction, section 2 describes the structure of aDoA monitor and provides an introduction to the problem ofDoA measurement. It describes anaesthesia states and relatedfeatures present on EEG signals. Section 3 describes theprocessing algorithm. Section 4 describes the implementationaspects of the processing algorithms and the results obtained.The conclusions are presented in the final section.

II. STRUCTURE OF A DOA MONITOR

Experimental evidence shows that the frequency and ampli-tude of the EEG is correlated to the state of consciousness ofthe person. The EEG of a normal human adult when arousedhas β waves from 13 to 30 Hz. During sleep the EEG showsδ waves from 0.5 to 4 Hz. When a human is relaxed andwith his eyes closed, the EEG’s frequency components arebetween 8 and 14 Hz, known as α waves. Above 30Hz, the

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Fig. 1. Typical EEG waves, [9].

Fig. 2. Structure of a DoA monitor index.

EEG signal has components caused by electromyography andfrom other sources such as 50Hz from the mains power system.There is the notion that an EEG desynchrony is correlatedwith a cognitively functioning brain, and an EEG synchronyis a characteristic of an unconscious brain. Figure 1 representssome EEG patterns. At the core of a DoA monitor is a dataprocessing algorithm, usually a proprietary algorithm, thatprocesses electroencephalographic (EEG) signals to extractfeatures that may be correlated with the drug anaestheticconcentration at the effect site, the brain, in a complex form.Figure 2 shows the structure of a DoA monitor. The electricalactivity of the brain when measured at the skull it is in therange of +/ − 200µV , it represents the sum of the activityof groups of neurons. The EEG signal is first filtered toremove noise and artefacts such as the 50 Hz of the mainspower system and bias components. Then it is processed byapplying frequency and time domains techniques to extractspectral power distributions and amplitude distributions thatmay change in time as a function of the drug concentration atthe effect site. The process of feature extraction may depend ona synchronization with an external stimuli (evoked potentials)or may rely only on the processing of the spontaneous EEG.The extracted features are then compared with features presentin a database that represent reference anaesthetic states for apopulation and for a given drug. The result of the processingis transformed in a continuous index normalized between 0and 100 based on a correlation with clinical assessment. Thelevel 0 is used to characterize the absence of EEG activity and

the 100 level is used to characterize the EEG correspondingto the normal state of conscience, awareness.

During human general anaesthesia the aim is to controlthe analgesia, hypnosis, amnesia and suppression of reflexesin a safe and reversible manner such that the patient doesnot fill pain or create memories and the surgery may beexecuted safely. With increasing levels of anaesthetic drugin the blood circulation, such as propofol, the EEG signalchanges from a low-amplitude, large-bandwidth, noise-likesignal to a high-amplitude and slow-waves signal. For anincreasing amount of anesthetic drug, the EEG signal beginsto show burst suppression periods alternating with brain ac-tivity periods, meaning that anaesthesia is reaching a deeperlevel. The percentage of time with burst suppression maybe used to characterize this anaesthesia state. The deepestanaesthesia level corresponds to an isoelectric signal, i. e.,there is no cortical activity [9]. Note that the anaesthetistuses the EEG signal and other physiological variables to helpon the assessment the DoA level, such as, blood pressure,pulse, sweat, lacrimation, reaction to painful stimulation, eyemovements oxygen saturation, urine output, etc. But part ofthis information is usually evaluated and used qualitatively.

The medical assessment of the states of anaesthesia is basedon clinical scales, the Guedel’s classification was the first oneto be used with the use of ether, more recent clinical scalesare the modified Ramsay Sedation Scale (RSS), which wasdescribed by Michael A. E. Ramsay [10], and the modifiedObserver’s Assessment of Alertness/Sedation Scale(OAA/S).In animal surgery the veterinarian also checks the clinical stateof the animal, using clinical signs such as cardiorespiratoryparameters and reflexes such as eye lid reflexes and responsesto painful stimuli: tail, ear or paw pinch reflex, but this lasttest is not possible if muscular blockade drugs are used.

III. METHODS TO EXTRACT EEG FEATURES

Fourier analysis, Bispectral analysis, wavelets and his-tograms may be seen as mathematical tools for generatingprobability density function (pdf) of features. The featuresthat usually are extracted from the EEG signal are related toamplitude and power of the signal, power density distributionon frequency or time domains. In simple terms the aim is toobtain an estimative of the pdf and how it changes over time.After this phase the aim is to map the change into an indexand correlate it to the clinical assessment of anaesthesia.

For instance, BIS index R© [2] combines frequency and timedomain techniques with burst suppression detection, the IoC-View R© [8] uses symbolic dynamics methods integrated withburst suppression and β ratio. The Narcotrend index [3] isbased on extraction of quantitative features from a databaseof EEG traces, in time and frequency domain, classifying intostages from A (awake state) to F (isoelectric signal). Medianfrequency, spectral edge frequency and band power ratio areexamples of other methods.

To develop an DoA index for animal anaesthesia it isnecessary to evaluate the EEG changes as a function of thedrug concentration at the effect site. This is a complex task


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that usually involves the development of a pharmacokineticsmodel to describe the dynamic relationship between the drugperfusion rate or bolus doses and the drug concentration inthe blood, and to develop a pharmacodynamic model thatrelates the drug concentration in the blood with the observableeffect. This implies taking blood samples at precise timingto measure the drug concentration in blood. However this isa complex task in small animals. Note that these dynamicmodels are also used to develop DoA automatic controlmethodologies. In [11] evaluation of five methods to assessDoA using EEG data collected from a healthy male Sprague-Dawley rat anesthetized with isoflurane is presented. Shannonentropy [12], permutation entropy [13] and a mapping methodproposed in [14] are used with the amplitude distribution ofthe EEG signal, the power density of the signal in frequencydomain and with the motifs extracted from the EEG in thetime domain. As the first step, only the drug perfusion rateand the EEG signal are measured and the EEG is analyzed todetermine features similar as the ones present in the humananaesthesia, such as power shift in the EEG frequencies, andpresence of burst suppression periods.

The method that is evaluated in this work computesthe mean power for the frequency bands δ [1.0; 4.0]Hz,θ [4.0; 8.0]Hz, α [8.0; 12.0]Hz, β1 [12.0; 16.0]Hz,β2 [16.0; 20.0]Hz, β3 [20.0; 30.0]Hz and γ [30.0; 45.0]Hz.The EEG is first filtered by a bandpass butterworth filter([0.5; 45.0]Hz) of order 10 to attenuate the 50Hz fromthe mains power system and to remove bias. The filteredsignal is clamped to reduce the effects of high intensityartefacts that occur while the veterinary is handling the an-imal during the procedure. The clamped signal is filteredaccording to the bands δ, θ, α, β1, β2, β3 and γ to pro-duce signals that are squared and time averaged to obtainthe mean power values for each band. These signals andthe power ratio between bands are assessed to evaluate theDoA. This approach is motivation by other monitors suchas BIS that uses the relative beta ratio, (computed as[(P [30.0; 47.0]Hz/P [11.0; 22.0]Hz]dB), as one com-ponent of the index. The band γ gives information aboutthe electromyography of the animal, it can be used as anindication of EEG contamination cause by muscular activityand information about the DoA.

IV. DATA ACQUISITION AND RESULTS

A rabbit with the weight of approximately 3.0kg wasprepared according with the standard ethic rules for animalexperimentation. Figure 3 shows the setup. The IOC monitorwas used as a data acquisition system, using a Bluetooth datalink. The IOC monitor allows the acquisition of the EEG signalat rate of 1024 samples per second and it provides the indexesIOC (Index of Consciousness, range 0-99), EMG (Electromyo-gram, range 0-100), ESR (EEG suppression ration, range 0-100), SQI (Signal quality index, range 0-100). The electrodeswere placed in the head of the rabbit according with theinformation of the IOC manual. The anesthetic drug propofolwith the concentration of 10mg/ml was administrated to

Fig. 3. Experimental set-up.

the rabbit using the perfusion syringe pump Alaris GH. Thecommand of the syringe pump was performed by a computerwith software developed in house. The experiment starts withthe administration of the first bolus (20 mg/kg), followed byadjustements of the anaesthetic flow rate as shown in figure4, where the IOC index is also shown. The aim was to putin evidence changes in the EEG as a function of the drugflow rate. The cardiorespiratory parameters were constantlychecked to detect and avoid the problems associated with deeprespiratory depression. From fig. 4 it may be concluded thatthe IOC monitor has a small sensitivity to changes in theanaesthetic flow rate. Apparently it stays near the value 50.Note that the time instants with values above 100 correspondto operation codes of IOC, like testing the the electrodeimpedances.

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The clinical assessment of DoA is shown in 5. Six lev-els are defined using standard veterinary methodologies thatevaluate the reflexes such as respiratory frequency, heart rate,arterial blood pressure, pupil size, eyeball position/movement,palpebral and corneal reflexes, muscular tone and nociceptiveresponse. The value 100 corresponds to the awake state, andthe 0 corresponds to the DoA deepest state. Figure 6 shows theEMG signal over time as a function of the anesthetic drug flowrate. It presents a clear correlation with the drug flow rate, theEMG decreases with the increase of drug flow rate, and theEMG decreases when the drug flow rate is decreased. It mustbe emphasized that there is a time lag between the change on


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the drug flow rate and the effect it causes in the EMG index,this is more clear at the beginning of the experiment after theadministration of the first bolus, the pharmacokinetics and thepharmacodynamics (Hill function) are playing their roles. The

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EEG suppression ratio index and the signal quality index areshown in 7. One interesting conclusion about the IOC, EMGand ESR indexes is, it seems that IOC index stays near 50when there is EMG activity and the ESR is zero, but the IOCindex starts to decrease as the ESR increases. The method thatcombines the two or more elements is unknown as it is thecase of BIS and with other monitors.

The corresponding EEG signal obtained from the exper-iment is shown in 8. It is clear that the EEG is affectedby artefacts, that cause large amplitudes. The main sourceis caused by the anesthetist while evaluating the state of therabbit, or while the rabbit is being ventilated. One conclusionis that the amplitude of the EEG signal is strongly correlatedwith the anesthetic drug flow rate. The amplitude decreaseswith the increase of the drug flow rate and duration time, and itincreases if the drug flow rate is decreased. The drug flow rateis modulating the EEG amplitude envelop. Other parameters

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Fig. 7. EEG Suppression Ratio index (ESR) over time and EEG signalquality index (SQI).

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Fig. 8. EEG over time as a function of the anesthetic flow rate (propofol).

that must be analyzed are the signal frequency/power shift.

A. Results obtained with the band filter bank

In order to apply the method described previously, the EEGsignal is first decimated to 256 sample per second. The inputbandpass filter and the clamping mechanism ( +/ − 80 µV )are applied to the EEG signal, producing a signal with thebias removed and with the effect of the 50 Hz componentdecreased. The power of each band in dB = 10log10(.) areshown in the 9, 10 and 11.

As it may be concluded the signals are strong correlatedwith the drug flow rate but show different ranges. Moreoverthe mean power evolutions of β1 and β2 bands are very similarto the EMG produced by the IOC monitor. The signal fromthe γ band, that corresponds to the electromyogram activity,shows a lot of spikes, its the noisiest, but the biggest problemto solve is how to select reference states with clinical meaning.One possibility is to define the minimal EEG amplitude,corresponding to the definition of EEG burst suppression zone.But this depends on the drug type and possible on the animalspecies. Other possibility is to build a database with studycases or to use evoked potentials to build a reference state.


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V. CONCLUSION

The paper address the problem of designing a systemfor automatic assessment of Depth of Anesthesia (DoA) forgeneral surgery in veterinary medicine. The EEG signal ofthe anesthetized animal is used to extract features that maybe correlated with DoA level and with the administration ofthe anesthetic drug. The method that is evaluated in based onthe concept of computing the mean power of EEG frequencybands as in human anesthesia. A commercial DoA monitor(IOC) was used as data acquisition and also to compare theDoA assessment. After processing the raw EEG signal it isshown that the results are similar to the EMG produced bythe IOC monitor. The main difficulty on evaluating DoA isto define reference states that are easily related with physio-logical states. Additionally the algorithm must incorporate thedetection of burst suppression. These tasks will be continuedin order to have a DoA monitor for animal anesthesia.

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[2] AspectMedical, ”www.aspectmedical.com”, 2010.[3] S. Kreuer and W. Wilhelm, ”The Narcotrend monitor”, Best Practice and

Research. Clinical Anaesthesiology, pp. 111–119, 2006.[4] L. S. Prichep, et al., ”The Patient State Index as an indicator of the level

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[5] , M. Jospin, P. Caminal, et al., ”Depth of Anesthesia Index usingCumulative Power Spectrum”, Proceedings of the 29th Annual Int. Conf.of the IEEE EMBS, pp. 15-18, 2007;

[6] T. Mendonca, J.M. Lemos, H. Alonso, P. Rocha and S. Esteves Drug De-livery for Neuromuscular Blockade With Supervised Multimodel AdaptiveControl, IEEE Trans. Control Systems Technology, Vol. 17, No. 6, pp.1237-1243, 2009

[7] R. S. Jones, ”Comparative mortality in anaesthesia”, British Journal ofAnaesthesia, 2001.

[8] , MorpheusMedical, www.morpheus-medical.com, 2010.[9] J. Malmivuo and R. Plansey, Bioelectromagnetism, Oxford University

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Alphadolone”, British Medical Journal, Vol. 2, pp. 656-659, 1974[11] R. Ricardo, B. Costa, A. Silva, J. Lemos and L. Antunes, ”Comparision

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[12] R. Ferenets, T. Lipping, et al., ”Comparison of Entropy and ComplexityMeasures of the Assessment of Depth of Sedation”, IEEE Transactionson Biomedical Engineering, Vol. 53, pp. 1067-1077, 2006.

[13] E. Olofsen, J. W. Sleigh and A. Dahan, ”Permutation entropy of theelectroencephalogram: a measure of anaesthetic drug effect”, BritishJournal of Anaesthesia, year=2008, Vol. 101, pp. 810-821, 2008.

[14] T. Zikov, et al, ”Quantifying Cortical Activity During General Anes-thesia Using Wavelet Analysis”, IEEE Transactions on Biomedical Engi-neering, Vol. 53, pp. 617-632, 2006.


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Assessment of the Depth of Anaesthesia in animals for