Multitasked closed-loop control in anesthesia

Multitasked Closed-LoopControl in AnesthesiaAutomatic Controllers Capable of Regulating MultiplePatient Outputs for Higher-Quality Anesthesia Treatment

The Automatic Control Laboratory atETH is developing a multitask autono-

mous anesthesia system in collaborationwith the University Hospital in Bern.Closed-loop controllers that regulate in-spired and expired anesthetic gas, O2 andCO2 concentrations have already beenused in clinical studies on humans. In thisarticle, two additional complex control-lers are discussed: control of mean arterialpressure (MAP) and control of hypnosisthrough bispectral index (BIS). Both con-trollers use Isoflurane as input, a hypnoticdrug that induces hypotension. The mod-eling effort and the control design, whichaccommodates input and output con-straints as well as model uncertainty, arediscussed for both controllers. Moreover,supervisory functions are outlined that arenecessary for application in the operatingroom (OR). In particular, artifact-tolerantcontrol schemes are presented. The exper-imental setup and the results of the appli-cation of automatic control during clinicalstudies are also discussed, together withan outline for future research.

Overview: Controlling AnesthesiaProblem Formulation

Adequate anesthesia can be defined asa reversible pharmacological state inwhich the patient’s muscle relaxation, an-algesia, and hypnosis are guaranteed. An-esthesiologists administer drugs andadjust several medical devices to achievesuch goals and to compensate for the ef-fect of surgical manipulation, while main-taining the vital functions of the patient.Figure 1 depicts the input/output (I/O)representation of the anesthesia state. Thecomponents of adequate anesthesia are la-beled “unmeasurable” because they mustbe assessed by correlating them to avail-able physiological measurements, as de-picted in Fig. 1.

Muscle relaxation is induced to facili-tate access to internal organs and to de-press movement responses to surgicalstimulation. The degree of relaxation canbe estimated by measuring the force ofthumb adduction induced by stimulationof the Ulnar nerve [51] or by single twitchforce depression (STFD) [3].

Analgesia is associated with pain reliefbut, at present, there are no specific mea-sures to quantify it, as it is even debatableto speak about pain perception when thepatient is unconscious [42]. Anothersource of complexity results from the factthat clinical signs such as tearing, pupil re-activity, eye movement, and grimacing[7] are partially suppressed by muscle re-laxants, vasodilators, and vasopressors.

Hypnosis is a general term indicatingunconsciousness and absence of postoper-ative recall of events that occurred duringsurgery [24]. Some authors believe there isa sharp distinction between conscious andunconscious states [42]. In this respect, itwould be improper to speak about depth ofanesthesia. However, the patterns of theelectroencephalogram (EEG), which is theonly noninvasive measure of central ner-vous system activity while the patient isunconscious, show gradual modificationsas the drug concentrations increase in thebody. Nowadays, the EEG is considered asthe major source of information to assessthe level of hypnosis.

Better accepted measures exist for thevital functions. Heart rate (HR) and MAPare considered the principal indicators forhemodynamic stability, while oxygen(O2 ) tissue saturation or end tidal carbondioxide (CO2 ) concentrations provideuseful feedback to anesthesiologists aboutthe adequacy of the artificial ventilation.

To achieve adequate anesthesia, anes-thesiologists regularly adjust the settings ofseveral drug infusion devices as well as theparameters of the breathing system to mod-

January/February 2001 IEEE ENGINEERING IN MEDICINE AND BIOLOGY 390739-5175/01/$10.00©2001IEEE

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Andrea Gentilini1, Christian W. Frei1,Adolf H. Glattfedler1, Manfred Morari1,

Thomas J. Sieber2, Rolf Wymann2,Thomas W. Schnider2, Alex M. Zbinden2

1Automatic Control Laboratory,ETH Zentrum, Zurich

2Department of Anaesthesiology,University Hospital, Bern

ify the manipulated variables listed in Fig. 1.These adjustments are done based on somepatient-specific target values and the moni-tor readings. Thus, anesthesiologists adoptthe role of a feedback controller, and it isnatural to ask whether automatic controllersare capable of taking over and/or improvingparts of such a complex decision process.

The Benefits of FeedbackControllers in Anesthesia

Several authors have recognized the ad-vantages associated with the use of auto-matic controllers in anesthesia [48, 6, 40].First, if the routine tasks are taken over byautomatic controllers, anesthesiologists

are able to concentrate on critical issuesthat may threaten the patient’s safety.

Secondly, by exploiting both accurateinfusion devices and newly developedmonitoring techniques, automatic con-trollers would be able to provide drug ad-ministration profiles that, among otheradvantages, would avoid overdosing.Moreover, controllers may take advan-tage of the drug synergies, for which aproper modeling framework has nowbeen developed [38]. The ultimate advan-tage would be a reduction in costs due tothe reduced drug consumption and theshorter time spent by the patient in thepostanesthesia care unit (PACU).

Further, if tuned properly, automaticcontrollers should be able to suppressinterindividual variability and to tailor thedrug administration profile to the particu-lar stimulation intensity of each surgicalprocedure [35]. Ultimately, automatic con-trollers could be used for research as a “ref-erence” anesthesiologist in clinical studies.

The Challenges of theOperating Room

Several complexities were faced in de-signing the real-time platform to testclosed-loop controllers in the OR. The is-sues and the solutions adopted are sum-marized below.

� Sensors and actuators: To provideadequate monitoring of the patient’svital functions and easy access todrug infusion devices, several sen-sors and actuators had to be assem-bled on the mobile real-time platformdepicted in Fig. 2.

� Safety critical system: All the im-plemented closed-loop controllersuse physiological signals that are po-tentially corrupted by artifacts. Cor-rupted signals may result either fromroutine manipulation or calibrationof the measuring devices and maycause, if not considered in the controlschemes, considerable damage to thepatient. To address this problem, su-pervisory system and fault-tolerantcontrollers were embedded in thereal-time platform to detect sensorfailures and/or other abnormal opera-tions. A more detailed description ofthese safety features of the platformwill be given below.

� Model uncertainty: A major sourceof difficulty results from the extremeuncertainty associated with the pub-lished models for drug distributionand effect. Nevertheless, anesthesi-ologists are able to compensate forinterindividual variability by tailor-ing drug administration to the indi-vidual patient’s needs, thereforeguaranteeing adequate performanceacross the population. In order to re-produce this feature, feedback con-trollers must be designed with robustand/or adaptive techniques. Robusttechniques lead to controllers for the“worst case” situation, which tendsto make them sluggish. Adaptiveschemes rely on the excitability bythe input signal, which is often lim-ited by ethical constraints. Our ap-proach was to use both data collectedfrom patients under ordinary patientsduring anesthesia and data acquired

40 IEEE ENGINEERING IN MEDICINE AND BIOLOGY January/February 2001

ManipulatedVariables

Disturbances

I.V. Anesthetics

Volatile Anesthetics

Muscle Relaxants

Ventilation Parameters

NaCl

Surgical Stimulus

Blood Loss

Hypnosis

Analgesia

Relaxation

EEG Pattern

Heart Rate

CO Conc.2

Blood Pressure

Insp/Exp Conc.

UnmeasurableOutputs

MeasurableOutputs

1. Input/output (I/O) representation of the anesthesia problem.

I.V. Pump

Monitor

ManualGas Dosing

EmergencyShut-Down

BreathingSystem

ElectronicGas Dosing

Touchscreen

Computer

BIS Monitor

2. The real-time platform for clinical studies.

from young healthy volunteers forthe design and the validation of theproposed control strategies.

� Clinical validation: Improvementsfor clinical practice and patient carethat are envisioned from the use ofautomatic controllers in anesthesiacannot be quantified unless extensiveclinical validation is performed.Therefore the ETH project has beenput on solid foundations by establish-ing a close cooperation between aclinic (University Hospital of Bern)and an anesthesia workstation manu-facturer (Dräger Medizintechnik.Lübeck, Germany). Among other ad-vantages, the partnership with thehospital enabled us to acquire datadirectly from young healthy volun-teers for model identification. Thesupport from the industrial partnersprovided us with the latest availabletechnology on the market.

Up to now, six different control strate-gies have been tested by the ETH-Uni-versity Hospital team on more than 150patients during general anesthesia. Thesecontrollers regulate O2 , CO2 , inspiredand expired anesthetic gas concentra-tions in the breathing system, as well asMAP and depth of hypnosis derived fromthe EEG [8, 15, 54, 19, 10, 14].

Closed-Loop Controlof Anesthetic Effect

Among the several controllers dis-cussed above, two single-input, sin-gle-output systems (SISO) extractedfrom the multiple-input, multiple-output(MIMO) control problem depicted inFig. 1 will be described extensively inthis article: MAP and bispectral index(BIS) control. Both feedback systemsuse the volatile anesthetic Isoflurane asinput and aim at controlling the anes-thetic effect.

Several reasons motivate MAP con-trol during surgery. MAP decreases withincreasing Isoflurane concentrations inthe internal organs. As such, MAP is of-ten viewed as an indirect measure of theanes the t ic ef fec t . Beyond tha t ,hypotension is also induced to minimizeblood losses and increase surgical visi-bility [17]. Moreover, maintaining MAPwithin an acceptable physiological rangeguarantees adequate perfusion of inter-nal organs. Finally, suppressing MAP re-actions to surgical stimuli enhances thepatient’s safety. MAP is measuredinvasively by a catheter placed in the ra-dial artery. The signal is transferred to

the monitors by a transducer and sampledat a frequency of 128 Hz. Every reposi-tioning of the patient, as well as calibra-tion of the transducer and flushing of thecatheter to prevent blood clogging, leadsto an artifact in the measured signal. Arti-facts are suppressed with a scheme dis-cussed below.

Hypnosis can be assessed with thebispectral index (BIS Index, Aspect Med-ical System. Newton, Massachusetts).This index is derived from the EEG bycombining the higher-order spectra of thesignal with other univariate indicatorssuch as spectral edge frequency (SEF) andmedian frequency (MF) [47, 49, 53]. Thiscombination of indicators is necessarysince, for instance, SEF and MF can pro-vide an estimate of the hypnotic state atdeep levels but are likely to fail in the re-gion of light sedation [20, 29]. BIS can re-veal, unlike simple Fourier analysis, thesynchrony of cortical brain signals, whichcharacterizes unconsciousness [43]. BISvalues lie in the range 0-100, where 100 isassociated with the EEG of an awake sub-ject and 0 denotes an isoelectric EEG sig-nal. BIS predicts accurately return ofconsciousness [11, 21, 28, 50], and it isthe only clinical monitor for hypnosisthat, to date, has received US FDA clear-ance. BIS has been developed and vali-dated based on EEG recordings of 5000subjects. More than 450 peer-reviewedarticles and abstracts provide a clinicalevaluation of BIS.

ModelingThe model required for control has to

account for two qualitatively differentsystems: the medical devices (actuators

and sensors) and the physiology involvingdrug distribution, metabolism, and effectsin the human body.

The Respiratory SystemA schematic drawing of the respiratory

system depicted at the bottom of Fig. 2 isgiven in Fig. 3. A pump forces the air intothe patient’s lungs while unidirectionalvalves impose a fixed orientation (oneway) for circulation of the gases. The freshgas stream enters on one branch and exces-sive air leaves on the other. This gas flowthrough the system leads to a constantflush. If a high fresh gas flow is used(Q0 4> l/min), the dynamics introducedby the respiratory system may be ne-glected, since a change in the fresh gascomposition is effective for the patient al-most immediately. During minimal flowanesthesia (Q0 1 1< min), fresh gas flowonly compensates for gas taken up by thepatient and the eliminated CO2, through theabsorber. At such low flows, the system iseconomically more efficient and safer forthe patient. In fact, due to recirculation, thepatient breathes a gas mixture at almostconstant humidity and temperature. Theexpired gases are now recirculated, whichintroduces considerable dynamics. A firstprinciples model of the minimal-flowbreathing system was derived to capturethe behaviors of the system in detail [10]. Asimplified description giving the same pre-diction accuracy can be obtained by con-sidering the respiratory system to be a well-stirred tank. Inspired concentrations de-pend on the vaporizer fresh anesthetic gasconcentrations, C 0, and the patient’sbreathing parameters through the follow-ing equation:

January/February 2001 IEEE ENGINEERING IN MEDICINE AND BIOLOGY 41

Y-Piece

UnidirectionalValves Pressure-Relief

Valve

Fresh FlowQ , C0 0

Absorber

BreathingBag

3. Schematic representation of the closed-circuit breathing system. Typical rangesfor the parameters of the respiratory system [see Eq. (1) for a detailed description]are : Q0 = 1-10 l/min, fR = 4-25 l/min, VT = 0.3-1.2 l, V = 4-6 l, ∆Q = 0.1-0.5 l/min,C 0 = 0-5%, expressed as Isoflurane volume percent in the breathing mixture.

( )

( )( )V

dC

dtQ C Q Q C

f V C CR T

insp

insp

insp

= − −

− − −

0 0 0

1

∆

∆

(1)where V [l] is the volume of the respira-tory system; C1 [%] is the alveolar con-centration or endtidal concentration,measured as volume percent of thebreathing mixture; fR [1/min] is the re-spiratory frequency; VT [l] is the tidalvolume; and ∆ [l] is the physiologicaldead space. ∆Q [l/min] represents thelosses of the breathing circuit through thepressure-relief valves. Q0 and C 0 [%] arethe fresh gas flow and its anesthetic con-centration entering the respiratory cir-cuit, respectively. C 0 is the manipulatedvariable in our control system. We willrefer to C 0 as the “vaporizer setting” inthe below sections.

Modeling the Effectof Anesthetic Drugs

The physiological mechanisms regu-lating drug distribution and effects areonly partially known. Therefore, firstprinciples modeling is almost impossible.Thus, one has to select from approximatefirst principles physiological models [2],black-box identification schemes [26],and knowledge-based modeling. All theseapproaches exhibit certain drawbacks. In

physiological models, parameters arehighly uncertain and collected from dif-ferent sources where experiments mayhave been performed under very differentconditions. Black-box models and knowl-edge-based models suffer from poor ex-trapolation properties.

For the design of both BIS and MAPcontrollers, we used physiology-basedmodels consisting of a pharmacokinetic(PK) part describing the drug distributioninto the internal organs, and a pharmaco-dynamic (PD) part describing the drug ef-fect on the physiological variables ofinterest. The structure of the PK part iscommon to both controllers, since bothuse Isoflurane as input. The PD part dif-fers in the two models and will be dis-cussed below.

Any PK model consists of differentialequations resulting from mass balancesfor the drug within different compart-ments [27]. For the generic ith compart-ment, we may write [62]:

( )VdC

dtQ C C k Ci

ii a i o i i= − −( ) ( ),C C

(2)

C C Ri o i i, /= (3)

where Qi is the blood flow bathing the or-gan, Vi is the distribution volume of thedrug, ki is the elimination rate, and Ri is

the apparent partition coefficient thatmacroscopically describes drug absorp-tion and metabolism as a ratio between in-ternal (C i) and outf low (C i o, )concentration [2]. C is the vector of drugconcentrations in the different compart-ments, andC a is the drug concentration inthe arterial pool. Since the pharmacologi-cal properties of Isoflurane have been ex-tensively documented [13, 57, 61, 60],some of the parameters in Eqs. (2) and (3)were derived from the literature. Theother parameters were estimated from thedata collected during general anesthesiawith Isoflurane. For further details on theidentification procedure, the reader is re-ferred to the literature [14].

Inspired and end tidal concentrationsof the anesthetic agent (Isoflurane) aremeasured on-line and provide a reliableindication of the patient’s drug uptake. Aninhalation anesthetic represents a clearadvantage over intravenous drugs, forwhich no measure of drug concentrationin the central body compartment are avail-able. This advantage was exploited inboth control schemes by imposing con-straints on end tidal concentrations to pre-vent overdosing.

In our models, ventilation and bloodflow are described as nonpulsatile phe-nomena. Since the equilibration time ofthe drug is greater than the respiratorycycle and the period of HR, this assump-tion does not affect performance of thecontrollers.

Modeling for Control of MAPTo model the PD effect of Isoflurane

on MAP, we assumed that the reactionof the hemodynamic system to the drugdoes not change during prolonged ad-ministration [57]. In other words, not ime vary ing phenomena due tosensitation or tolerance to Isofluranewere considered. Even though HR isknown to be strongly coupled withMAP, it was not taken directly into ac-count in our model. Several tachycardicepisodes have been reported duringIsoflurane anesthesia, and they maysuggest the need to model those effects[52]. However, it is not clear whetherthose episodes depend on beta sympa-thetic activation of baroreflex activity.In fact, at moderate to deep levels ofIsoflurane anesthesia, two clinical in-vestigations have reported contradic-tory results concerning the activity ofthe baroreflex, leaving the issue unre-solved [12, 31].


20

18

16

14

12

10

8

6

4

2

0

−20 1 2 3 4 5

Time [min]

∆MA

P[m

mH

g]

4. MAP deviations (o) from values before stimulation are plotted versus time after atetanus stimulus for one subject at 0.5% end tidal concentration of Isoflurane. Theneuronal (first peak) and humoral (second peak) components of the MAP reactionare distinguishable from the plot.

The modeling structure describing thehemodynamic effects of another inhalationanesthetic, Halothane [62], was adapted tothe characteristics of Isoflurane. WhileHalothane decreases cardiac output (CO),reducing the perfusion to internal organs[18, 55], Isoflurane decreases mainly sys-temic resistance through dilatation of theblood vessels [13]. We assumed that MAPand CO can be described through the fol-lowing PD relationship:

MAPQ

g

a

ii

N=

=∑( )

( )

C

C1 (4)

where gi represents the conductivity ofthe ith organ, increased by the presence of

Isoflurane, and Q Qa ii

N==∑ 1

represents

CO. Conductivities gis are assumed to de-pend exclusively on the anesthetic con-centration in the same compartment,whereas Qa is assumed to depend on thearterial (A), gray matter (G), and myocar-dial (M) concentrations through the affinerelationships:

( )Q Q a C a C a Ca a A A G G M M= + + +,0 1

(5)

( )g g b Ci i i i= +,0 1 . (6)

Qa ,0 and gi,0 are the baseline CO and con-ductivity in the ith organ, respectively.According to the previous discussion, theeffects of Isoflurane concentrations aremore pronounced in Eq. (6) than in Eq.(5) [14].

MAP must be kept within physiolog-ical limits during anesthesia, despitesurgical stimulations. Therefore, a phys-iological model describing the effects ofskin incision, skin closure, and trachealintubation on the systemic circulationwas derived [9]. Surgical stimulation ac-tivates the sympathetic part of the auton-omous nervous system, as do othersituations such as stress, infection, andhemorrhage. The sympathetic systemtriggers a neural and humoral reactions.The former causes the release of nore-pinephrine in the synaptic cleft, whereasthe latter is associated with the dischargeof norepinephrine and epinephrine fromthe adrenal medulla into the bloodstream. The two reactions show signifi-cantly different time constants, one in theorder of few seconds and the other in theorder of a minute, which is the character-istic time constant of blood circulation. Itis expected that the MAP reactions showthe same distinct time constants. These

effects have been experimentallyobserved during our clinical studies. Atypical example where both effects areclearly visible after a tetanus electricalstimulation is shown in Fig. 4. MAP mea-surements expressed as deviations fromthe value before stimulation are plottedversus time immediately after the stimu-lus for a patient. The solid line representsthe prediction of the model [5, 6]. Thismodel was validated during clinical stud-ies, and the possibility of suppressing theeffects of surgical stimulation by a feed-forward compensation with Isofluranewas investigated [15].

Even though the model for MAP wasextensively validated, it still contains agreat amount of uncertainties, which mayresult from the different types and sites ofsurgical stimulation and the different sub-jects’ sensitivities to pain [41].

Modeling for Control of BISTo model the PD effects of Isoflurane

on BIS, we enrolled 20 consenting volun-teers and put them under general anesthe-sia with Isoflurane. At the time of thestudy, there were no published data de-scribing the effect of Isoflurane on BIS.

For a more detailed description of thestudy design, the reader is referred to [19].

We assumed that the overall dynamicmodel from Isoflurane-inspired concen-trations to BIS is limited by the drug dis-tribution to the organs. Once the drugarrives at the receptor or effect site, wecan assume that the binding occurs in-stantaneously. Then, at the effect site, therelation between BIS and site anestheticconcentrations is represented by a staticnonlinearity. In light of these arguments,none of the compartments of the PKmodel could be considered the effectcompartment. Therefore, an artificial ef-fect compartment was added to themodel to compensate for this hidden dy-namic. The effect compartment can beregarded as an additional compartmentwith negligible volume, attached to thecentral compartment. This assumptionensures that its presence does not perturbthe mass balance equations of the PKmodel. Effect site concentrations are re-lated to end tidal concentrations by afirst-order delay:

( )dC

dtk C Ce

e e= −0 1 .(7)


100 100

90 90

80 80

70 70

60 60

50 50

40 40

30 30

20 20

10 10

0 00 00.5 0.51 11.5 1.52 2

Vol. [%] Vol. [%]

BIS

5. PD relationships for two patients who underwent general anesthesia withclosed-loop BIS control. In both plots, BIS values versus effect site concentrationsare shown. The solid line (——) and the dash dot line (−⋅ −) represent the optimal fitobtained for the individual and for the population of volunteers, respectively. Notehow the patient represented in the right plot differs from the average subject in thepopulation.

The parameter ke0 is referred to as theequilibration constant for the effect site.As the PD relationship, we adopted theclassical Emax model:

∆ ∆BIS BISC

C ECe

e

=+MAX

γ

γ γ50 (8)

where

∆BIS BIS BIS= − 0 (9)

BIS BIS BISMAX MAX= − 0. (10)

BIS0 is the baseline or awake state value,and BISMAX represents the minimum BIS.EC 50 represents the concentration at theeffect site for which the effect is half of themaximum achievable. γ represents thesubject’s sensitivity to small concentra-tion changes at the effect site, and it can beregarded as an index of the modelnonlinearity. We assumed BIS0 100= andBISMAX = 0 since high concentrations ofIsoflurane lead to an isoelectric EEG. Insuch cases BIS = 0.

The modeling assumptions togetherwith the available identification proce-dure for the effect site compartment arediscussed with more details in the litera-ture [60, 46]. It is worth mentioning thatthe variability of the estimated PD param-eters in our study is one order of magni-tude greater than for the PK parameters[19]. This fact is also confirmed byoff-line analysis of the data collected dur-ing the clinical evaluation of the BIS con-troller. Figure 5 displays the effect ofIsoflurane concentrations at the effect

compartment on BIS for two different pa-tients during clinical studies. Togetherwith the single individual’s PD relation-ships, the PD function identified from thepopulation of volunteers is shown. In theleft figure, a patient who behaves verysimilarly to the population of volunteers isrecognizable, whereas in the right plot, agreat difference is observed. In particular,it seems to be extremely difficult toachieve BIS values under 40 for the pa-tient in the right plot unless a large amountof Isoflurane is used. On-line estimationof a single individual’s PD characteristicsand, in particular, deviations from the be-havior of the average subject populationwould improve the controller perfor-mance. Adaptive schemes that would usethis information are currently being stud-ied in our research project.

Controller Design and TestingMAP Control

Control of MAP is performed bymeans of three observer-based state feed-back (OBSF) controllers whose intercon-nection is illustrated in Fig. 6: where y1and y2 represent MAP and end tidalIsoflurane concentrations, respectively.

The underlying idea of the controlstructure is that the main controller C1,regulating MAP, is active under normalconditions. However, constraints have tobe imposed on y2 . An upper bound y2 ,

**ref

is needed because high Isoflurane concen-tration may lead to hypotonic crisis, car-diac arrhythmias, or even cardiac arrest.

To comply with the upper limit, the over-ride controller C 2

**, which is in itself acomplete OBSF controller, was intro-duced. The minimum selector applied tothe control signals of C1 and C 2

** ensuresthat the upper limit y2 ,

**ref is complied.

A minimum end tidal concentrationy2 ,

*ref must also be guaranteed to prevent

light anesthesia and awareness. There-fore, we also introduced an override con-troller C 2

* to ensure a minimum end tidalconcentration. The stability analysis ofthe override structure was done accordingto the published literature [23, 22].

The classical state-feedback controllerwith observer [33] was modified with theadditional blocks shown in Fig. 16. Afeedforward compensation term forMAP, as well as integral action, wereadded for better setpoint tracking and tocompensate for the effect of surgical stim-ulation and modeling errors, respectively.The input saturation shown in Figs. 6 and16 limits the fresh gas concentrations be-tween 0% and 5%, expressed asIsoflurane volume fraction in the fresh gasflow. Therefore, an anti-windup compen-sation was added to cope with the inputconstraints, which is not depicted in Fig.16 for the sake of simplicity. The parame-ters of the controller were obtained as thesolution of a linear quadratic regulator(LQR) problem [4]. The controllers wereparametrized with the fresh gas flow Q0,the patient weight and the respiratory fre-quency fR such that it is possible to com-pute the controller parameters on-linewithout going through the whole LQR de-sign process.

BIS ControlTo control BIS, we adopted the cas-

caded internal model control (IMC) de-picted in Fig. 7, where the mastercontroller regulates BIS and the slavecontroller regulates end tidal concentra-tions. The model of the patient is split intwo parts: the PK model

~P2 , relating in-

puts at the vaporizer u to end tidal con-cent ra t ions y2 , and the ef fec tcompartment model

~P1, relating y2 to y1

or BIS. The overall model is nonlinear,where the nonlinearity is represented bythe PD relationship relating effect siteconcentrations to BIS [19]. According toFig. 7, the master controller Q1 providesend tidal concentration reference valuesy2 ,ref to the slave controller. This controlloop forces y2 to reach the referencevalue y2 ,ref specified by the master loop.


y2,ref**

y1,ref

y2,ref*

C2**

C2*

C1 min

max

u

P

y2

y1

6. Block diagram of the override structure to control MAP. y1 and y2 denote MAPand end tidal concentration measurements, respectively. C1 regulates MAP undernormal conditions whereas C 2

** and C 2* enforce the upper and lower limit for end

tidal concentrations. u represents the vaporizer setting.

The theoretical background and thetuning guidelines for IMC control sys-tems are given in the literature [39]. Inputsaturation was also added to the parallelmodel

~P2 as an anti-windup prevention.

The saturation block after Q1 limits endtidal concentration references between0.4% and 2.5%. Constraints on y2 ,ref en-sure that the limits for y2 are not violatedbut also represent a clear limitation on thecontroller bandwidth. To compensate forthis, anesthesiologists are able to enlargeor narrow the range of the constraints, attheir convenience, during administrationof anesthesia.

The parallel model~P1 in the master loop

is a linearization of the nonlinear PD modelaround a reference concentration. Thisconfiguration proved to be more robustwith respect to PD parametric uncertaintiesthan the full nonlinear model. The offsetconcentrations in the block diagram wereomitted for the sake of simplicity. Thetransfer functions of the parallel models

~P2

and~P1 were obtained by using the average

parameters identified from the populationof volunteers. The transfer functions of theIMC blocks Q2 and Q1 were chosen as thefiltered inverses of the nominal models

~P2

and~P1 [39].

The choice of a cascaded arrangementwith IMC controllers contributed signifi-cantly to the acceptance of the controllersin the OR. In fact, the cascade arrange-ment mimics the procedure adopted byanesthesiologists if they were asked tocontrol BIS manually. Namely, due tolack of clinical experience in controllinghypnosis through BIS values, an anesthe-siologist would first target a specificvalue for end tidal anesthetic concentra-tion. Then, she/he would adjust the endtidal concentration reference on the basisof the BIS values. In the control schemedeveloped, both tasks are achieved at the

same time. Also, the design was accom-plished by tuning just two parameters,which have a direct clear interpretation.These parameters affect the approximateclosed-loop time constant of the slaveand master controller, respectively. Fur-ther, in the ideal case when there is noplant (physical system)-model mis-match, the closed loop trajectory tracksreference changes with no overshoot,thereby preventing overdosing. The timeconstants of the IMC filters were set toachieve nominal settling times for suchcontrollers (defined as 90% of thesteady-state value) equal to ts = 2 min forthe endtidal controller and ts = 4 min forthe overall BIS controller.

Another advantage of the IMC strat-egy is that the control transfer functionscan be adjusted on-line when the operat-ing conditions of the closed-circuitbreathing system are changed. This is notunusual during surgery, since fR, VT , andQ0 are often modified for several reasons.For instance, short periods at high flows (Q0 5≥ l/min) are normally used when arapid wash-out of the drug is needed to-ward the end of the operation. Further, in-creasing alveolar ventilation f VR T( )− ∆ isa standard procedure to reduce high levelsof end tidal CO2 resulting from increasedmetabolism [5]. If respiratory parametersare changed by the anesthesiologist at anytime during automatic mode, the supervi-sory system will update the model

~P2 and

the Q2 block.

Clinical Validation of the ControllersMAP Control

For the evaluation of the MAP control-ler, 40 ASA-class I to III (relativelyhealthy) patients aged 20 to 65 scheduledfor elective abdominal, orthopedic, tho-racic, or neurosurgery were enrolled in thestudy. The goal was to compare MAP

control performed by anesthesiologistswith automatic MAP control. The studywas recently completed. The outcomes ofthe clinical investigation will be pub-lished in the medical literature. In all clini-cal validations of MAP control that wewill discuss, the lower y2 ,

*ref limit for end

tidal concentrations was set to 0.4%. Theupper limit y2 ,

**ref may vary from 1 to

1.5%, depending on the surgical proce-dure. MAP values around the MAP mea-sured at the arrival of the patient in the ORwere used as targets. For half the patientschosen at random, closed-loop adminis-tration of Isoflurane is switched offroughly in the middle of the surgery pe-riod. From then on, Isoflurane is manuallyadministered by the anesthesiologist. Forthe other half of the patients, the oppositesequence is done.


y1,ref y2,ref

Q2 Q1

e1

e2

u y2y1

P2 P1

kP2~

P1~

−−

−−

7. Block diagram of the cascaded internal model control (IMC) structure to control BIS. y1 and y2 denote BIS and end tidalconcentration measurements, respectively. The master controller Q1 provides end tidal concentration references y2 ,ref to theslave controller Q2 . The saturation block after Q1 was introduced to enforce upper and lower limits for y2 .

It is natural to ask

whether automatic

controllers are

capable of taking over

and/or improving

parts of such a

complex decision

process.

Clinical performance is evaluatedbased on several criteria such as the dura-tion of periods with MAP within ±10% ofthe target value, and the numbers andtypes of critical incidents in both groups.These incidents are periods with MAP <65 mmHg, systolic BP > 160 mmHg, orHR > 110 bpm.

Figures 8, 9, and 10 show a clinicalevaluation of the MAP controller. In all thefigures, the upper plot represents the MAPprofile during the study. The second plotrepresents the end tidal concentrations y2 ,together with the upper, y2 ,

**ref , and lower,

y2 ,*

ref , limits. The third and fourth plotsrepresent the vaporizer settings and the ac-tive controller, respectively. According tothe notation in Fig. 6, −1 denotes thatC 2

* isactive, +1 denotes that C 2

** is active, and 0denotes that C1 is active.

In Fig. 8, the MAP profile under auto-matic control during a liver surgery is rep-resented. The controller is able to achievegood performance during the periods oflight surgical stimulation occurring att = 38 min and t = 89 min. Note that dur-ing the period of light stimulation (t =20-35 min), the override selector oftenswitches between the MAP controllerC1and the lower endtidalC 2

* , even thoughend tidal concentrations are within the ac-ceptable range. Presumably, this switch-ing is a result of the particular sensitivityof the patient’s MAP to Isoflurane duringthe unstimulated period. In this case, thecontroller C1 would tend to reduceIsoflurane settings and consequently endtidal concentrations below the acceptedlimit. At roughly t = 50 min, intense sur-gical stimulation occurs. To compensatefor this, an end tidal concentration largerthan y2 ,

**ref would be required, which acti-

vates the upper override controllerC 2**, as

depicted in the fourth plot of Fig. 8.Figure 9 shows a comparison between

manual and automatic control. Up tot =192 min, anesthesia was conductedmanually. According to the anesthesiolo-gist, during this phase just minor adjust-ments of the vaporizer setting wereneeded. At t = 210 min automatic controlstarts. This phase is dominated by an in-tense disturbance starting at approxi-mately t = 220 min, and a period of goodregulation from t = 250 min on. Notefrom the second plot in Fig. 9 that the con-troller tends to make more use of the band-width of allowed end tidal concentrationsthan the anesthesiologist, also as a resultof the more intense surgical stimulation inthe second part of the operation.


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**ref and y2 ,

*ref ,

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9. Comparison of manual and automatic regulation of MAP. Automatic controlstarts at t = 220 min. For a detailed description of the variables in each plot, see thecaption of Fig. 8. Note in the second plot that automatic control makes use of awider bandwidth of end tidal concentrations than does the anesthesiologist. Thispresumably results from the more intense surgical stimulations in the second part ofthe operation.

Figure 10 shows a clinical evaluationwhere the controller was able to suppressthe effect of surgical stimulation occur-ring at t = 82 min and t =115 min, respec-tively. The period between t = 50 min andt = 80 min was not characterized by anysignificant stimulation. During this phase,the lower end tidal controller was active todeliver a minimum amount of Isoflurane.

The following conclusions can bedrawn from the above discussion. The con-troller is successfully able to compensatefor light disturbances, as shown in Fig. 8.The limited control action and the fast dis-turbance dynamics seriously limit the abil-ity to compensate for intense disturbances.These limitations also apply for manualcontrol. Therefore, we expect that the dif-ference between manual and automaticcontrol will not be substantial. An intuitiveway for improving blood pressure regula-tion is to provide the controller with infor-mation about future major disturbancessuch as skin incision [15]. This approach isnow being tested in pilot studies.

BIS ControlForty ASA-class I to III patients age 20

to 65 scheduled for elective abdominal, or-thopedic, thoracic or neurosurgery wereenrolled in the clinical evaluation of theBIS controller. The goal of the study is todetermine whether closed-loop titration ofIsoflurane to target specific values of BISimproves the quality of anesthesia.Time-to-eyes opening after interruption ofdrug administration, and average timespent by the patient in the PACU are re-corded, together with episodes where BISwas outside the range 30-70. These periodsindicate excessive and insufficient levelsof hypnosis, respectively. The study is nowin progress. Figure 11 shows the perfor-mance of the controller during a discus her-nia removal. To the best of the authors’knowledge, this is the first model-basedclosed-loop controller for hypnosis as-sessed with BIS using volatile anestheticsthat has been tested on humans. The con-troller was able to keep BIS in the 40-50range and to follow changes in the refer-ence signal appropriately. The perfor-mance of the slave controller is better thanthe master, as apparent when comparingthe first and second plot in Fig. 11. This isexpected, since the model uncertainty islower for the PK model than for the PDmodel. Further, end tidal measurementsare less noisy than the BIS. Note also thatthe end tidal reference concentrations thatare necessary to maintain BIS = 50 are

lower at the beginning of anesthesia (t =80-90 min) than in the central period (t =140-180 min). This may result from eitherthe residuals of the intravenous hypnoticused for induction, from the lower inten-sity, or from surgical stimulation. Regard-less of the reason, the controller was able toadapt the administration profile to the par-ticular situation.

Figure 12 shows another clinical vali-dation of the controller. The automaticcontroller was started at t = 60 min. Att = 80 min the fresh flow rate was de-creased from Q0 3= l/min to Q0 1= l/minto minimize anesthetic consumption. Thesupervisor recognizes the changes in thebreathing system and updates the modelin the controller. As a result, the vaporizersetting increased immediately, whichcompensates for the slower dynamics atlow flow conditions.

Interesting conclusions can be drawnwhen comparing the performance of theMAP and the BIS controller. Namely, sur-gical stimulation does not affect BIS—andtherefore the hypnotic state—as much as itaffects MAP. This is expected, since con-sciousness is depressed with lower anes-thetic concentrations than is the stressresponse—particularly hemodynamicvariability. The question is then whetherIsoflurane should be used to regulate BIS

or MAP. Since Isoflurane is a hypnotic, itwould seem more natural to use it to regu-late BIS. However, this also depends on theanesthesiologists’ confidence in the BISvalues. A SIMO controller that regulatesBIS and MAP with Isoflurane is now beingconsidered in our research project. Thecontrol objective is to administerIsoflurane to regulate BIS, while maintain-ing MAP within specified limits.

Supervisory FunctionsThe supervisory functionality may be

regarded as the superposition of all thenecessary functions that have to bewrapped around the basic feedback con-trol algorithms to make them routinely ap-plicable in the OR.

Even though the need for supervisoryfunctionality for automatic control appli-cation in anesthesia has clearly been recog-nized by a number of researchers, not allare implemented in real practice [17, 34].Often, just a brief outline of the necessarysupervisory functions is presented [16, 36,37, 40, 25, 59]. A supervisory system wasimplemented in our real-time platform ac-cording to the scheme represented in Fig.13. Four main layers can be identified.

D - Input and output conditioning:The measurements pass an input condi-tioning stage as they enter the anesthesia


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10. Automatic regulation of MAP during neurological surgery, for which no epi-sodes of manual regulation were performed. For a detailed description of the con-tents in each plot, refer to the caption of Fig. 8. Note that the lower overridecontroller C 2

* is active during the first period (t = 50-80 min) of light surgical stimu-lation to guarantee a minimal anesthetic administration.

system. Conditioning includes prepro-cessing of the signals, selection of themost reliable one out of a set of multiple

measurements, and rejection of measure-ment artifacts. Similarly, the control sig-nals pass through a comparable output

conditioning stage. Typical tasks of thispart are the shaping of test inputs for faultdetection and isolation (FDI), the min-maxselection of an override controller, or theswitch from manual to automatic control.

C - Process information: At thisstage, all the data available about the stateof the process are stored, including con-troller states. Typically, this informationis stored in data banks. From there the dataare accessed for storage or for display.

B - Algorithmic layer: This layer in-corporates feedback controllers, supervi-sor logic control (SLC), FDI algorithms,as well as decision support functions.The division into layers B1 and B2 sepa-rates dynamic form logic control. At thisstage, for instance, transition conditionsare checked to allow the anesthesiologistto switch to automatic control. FDI func-tions are responsible for detecting faultsin the system, such as equipment faultslike disconnected sensors or leaks as wellas the detection of critical patient physio-logical states like excessive blood loss.Decision support (DS) makes sugges-tions to the anesthesiologist on how tooptimize the anesthetic procedure. It pro-vides information that cannot be directlyread from monitors. Typical examplesare the display of the estimated time re-quired for the patient to wake up after theanesthetic is discontinued, or the esti-mated concentration of anesthetic in thedifferent compartments.

A - Man Machine Interface (MMI):This is the most user-oriented layer and istherefore drawn as the top layer of the su-pervisory structure. All information fromthe system to the anesthesiologist, andvice versa, must pass through the MMI.The MMI is typically implemented by agraphical user interface (GUI), but acous-tic alarms are also part of the MMI. As anexample, the control panel adopted duringBIS control is depicted in Fig. 14.

A detailed discussion of all the su-pervisory functions is beyond the scopeof this article but can be found in the lit-erature [14]. Instead, we will focus on aparticular aspect of the supervisorysystem instead of the treatment of mea-surement artifacts.

Artifact-Tolerant ControllersSeveral authors have noted the unde-

sirable implications of improper han-dling of artifacts during closed-loopcontrol of anesthesia [45, 44, 16]. A mi-nor consequence is illustrated in Fig. 15.Here, a model-based end tidal controller


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12. The plots illustrate the clinical study as described in the caption of Fig. 11. Auto-matic control was started at t = 60 min and the fresh gas flow rate was reduced fromQ0 3= l/min to Q0 1= l/min at t = 80 min. The supervisor instantaneously imple-ments a higher vaporizer input to compensate for the slower dynamics of the actua-tor at low flows.

is supposed to lower end tidal Isofluraneconcentration From 1.3% to 0.7%, as in-dicated by the dashed line. During thismaneuver, an automatic calibration ofthe monitor occurs, resulting in a zerovalue for the concentration measure-ment. This sudden change in the con-trolled output causes the controller tofully open the vaporizer. Although the ar-tifact terminates after 20 s, enoughIsoflurane has been supplied to the sys-tem to considerably overshoot and pro-long the maneuver. Fortunately, thissituation only deteriorates the controllerperformance; there are no critical conse-quences for the patient here. On the otherhand, artifacts in the blood pressure sig-nal might lead to sharp changes of bloodpressure during MAP control. An exam-ple of such critical transients is docu-mented by Fukui and Musuzawa [16].Fortunately, we have not encounteredsuch a situation.

Before presenting our solution to theartifact problem, we investigate how arti-facts deteriorate controller performance.Figure 16 represents a classical OBSFcontroller, where integral action and thefeedforward compensator V have beenintroduced.

In developing the strategy, we workwith a linear time invariant approximationof the system represented by the statespace matrices A,B,C,D. In Fig. 16, v( )tand w( )t represent process and measure-ment noise, respectively; �( )t representsthe artifacts. The observer serves to esti-mate the states �x of the system. The matrixL for update of the observer states can becomputed considering process and mea-surement noise characteristics; e.g., with aKalman filter design.

The artifacts �( )t , unlike white, mea-surement, or process noises, affect thecontroller in two ways. First, through theoutput injection matrix L, artifacts leadto a mismatch between observer statesand system states. Secondly, they lead toan offset of the integral part of the con-troller. This mismatch between control-ler states and reality can be viewed as acontroller wind-up [30]. A natural rem-edy to prevent artifacts from degradingthe controller performance is to makesure that the artifacts �( )t do not enter theobserver equations.

In a stochastic framework, the errorsignal e y yy t t t( ) ( ) �( )= − represents a vec-tor-valued zero mean and white stochasticprocess [1]. If v( )t and w( )t are zero meanwhite noise processes, and an artifact oc-

curs, e y t( ) is not zero mean but has a meanof �( )t . Thus, the detection of an artifactbased on the innovations signal e y t( ) isequivalent to detecting a sudden shift in

the mean of e y t( ). The test for the zeromean hypothesis may be formulated as astatistical decision based on the actualvalue of e y t( ).


Anesthesiologist

Level

A

B1

B2

C

D

Man Machine Interface

DecisionSupport

SupervisoryLogic

Control

Controllers

FaultDetection

andIsolation

Process Information

Signal Information

Input Output

Measurements Control Variables

13. The different layers of supervisory functions. At the lower level, physiologicalmeasurements and output for the actuators pass through a signal preprocessingstage. For instance, artifacts in the measurements are rejected during this phase. Atthe top level is the man-machine interface.

(a)

(e)

(b)

(c)

(d)

14. Operating panel for closed-loop regulation of hypnosis. (a) Central panel, whichdisplays parameters of the breathing system, BIS, and the actual control action; (b)buttons for the switch between passive, manual, and automatic control; (c) buttonsto inform the controller about significant events such as skin incision, skin closure,and intubation; (d) BIS controller panel, where the reference BIS is selected to-gether with tolerable limits for the end tidal anesthetic concentration; (e) informa-tion about active controller and reliability of both BIS and end tidal concentrationmeasurements.

In case of an artifact, the correspond-ing output injection vector is set toLi = 0, which prevents the artifact fromentering the observer equations. Thestatistical decision may be representedby a diagonal nonlinear block f e( ( ))y t ,multiplying the innovations e y t( ). Thatis:

( )f e t e te t e t T

e t Ti y yy y i

y ii i

i i

i

( ) ( )( ) ( )

( )⋅ =

≤

> 0

(11)where Ti is a specific threshold value.With a more general nonlinearity:

( )( ) ( )( )

ψ

ψ ψ

e y

y p y

t

e t e tp

( )

( ) , , ( )= ⋅⋅⋅diag 1 1

(12)

the observer dynamics become:

( )�� ( ) �( ) ( ) ( ) ( )x Ax B L e et t u t t ty y= + + ψ .

(13)

Typically,ψ i ye ti

( ( )) is such thatψ i( )0 1=and ψ ψi i( ) ( )−∞ = ∞ = 0. An analogousapproach may be taken to exclude arti-facts from the integrator equation.

With the nonlinear modification intro-duced in Eq. (13), the stability of the feed-back system must be checked anew. To dothis, the system can be transformed intothe following form:

e e zy G s= +( )~ 1 (14)

( )~e e z= +∆ y 2 (15)

where G(s) incorporates the linear timeinvariant parts of the closed-loop system,and ∆ contains the nonlinear weightingfunctions. Using integral quadratic con-straints (IQC), one can check the stabilityof Eqs. (14) and (15) even whentime-varying or uncertain systems are in-cluded in the ∆-block.

The nonlinear modificationsψ i yei

( ) ofthe output injection have to be chosensuch that the performance of the observerdoes not suffer in the artifact-free case.More precisely, they must be chosen suchthat the normal noise on the signal (eyi

)does not lead to an inactivation of the ob-server corrections.

The artifact-suppression scheme wasextensively tested during clinical valida-tion of the model-based end tidal control-ler, as well as for MAP and BIS control. Inthis latter case, the concepts were adaptedto the particular structure of the cascadedIMC controller. Figure 17 shows typicalcases where artifacts were properly de-tected without performance deterioration.In particular, the lower plot in Fig. 17shows the correct behavior for an artifactlasting approximately 5 min.

Experimental SetupWhen it comes to the routine applica-

tion of automatic controllers to patients,considerable attention must be paid to aproper hardware/software (HW/SW) plat-form. In the early phase of the project, thecontrollers were implemented underModula II on an MS DOS-supported in-


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V

k s/r + u

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+A,B,C,D

A,B,C,D

L

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+ +

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16. Artifact-tolerant control schemes for observer-based state feedback (OBSF) con-trollers. The nonlinear functions ψ and ψ1 exclude residuals that are corrupted byartifacts and that may lead to serious degradation of the controller performance.

dustrial PC with the necessary A/D andD/A conversion boards. This platformsuffered from a number of shortcomings,such as poor support of real-time andmultitasking features, lack of softwarecomponents to design adequate user inter-faces, and compiler limitations.

Currently, a target (VME PowerPC)-host (PC) computer system provides thehardware basis for the experimental plat-form. The operating system XOberon fromthe Robotics Institute of ETH provides therequired real-time and multitask features[58]. The applications are implemented us-ing the object-oriented programming lan-guage Oberon, a member of the Pascal-Modula family [32]. Making use of ob-ject-oriented technology, a framework hasbeen developed that efficiently allows us towrite new control applications.

The platform is depicted in Fig. 2.The compact integration of the com-puter system equipped with a touchscreen on a standard CiceroEM anesthe-sia workstation from Dräger Germanycontributes significantly to the generalacceptance of this prototype system inthe OR. Also, the platform is endowedwith an emergency shut-down buttonthat cuts any active controller off andtransfers the regulation of the breathingsystem to the anesthesiologist for man-ual dosing. Recently, a pump has beenintegrated for the continuous intrave-nous infusion of analgesics.

The design of the MMI is aimed at re-producing closely the standard monitor-ing systems for anesthesia. In this way,anesthesiologists are able to runclosed-loop controllers without any engi-neering support. An example of an operat-ing panel is presented in Fig. 14.

ConclusionsThe first steps toward the development

of an autonomous anesthesia system atour laboratory have been described. Thecontrollers are implemented on areal-time platform and tested on humansto quantify the benefits that may resultfrom their use in routine practice. To date,the staff at the University Hospital in Berncan rely on controllers that regulate sixdifferent patient outputs. Overall, morethan 150 patients were treated withclosed-loop controllers during general an-esthesia. These controllers regulate O2 ,CO2 , and inspired and expired anestheticgas concentrations in the breathing sys-tem, as well as MAP and depth of hypno-sis through BIS.

The controllers for MAP and BIS werealso described. The controllers adopt par-ticular schemes to prevent overdosing inclinical practice, and they guarantee safeoperation in the presence of measurementartifacts. The controllers are also adapt-able to the different operating regimes ofthe breathing system, guaranteeing ade-quate performance. The successful appli-cation of both controllers in clinicalpractice has been demonstrated, indicat-ing higher-quality anesthesia for patientswho were treated with automatic control.

A number of limitations of the actualdesign must be pointed out at this stageof development. The controllers cannotbe used without the anesthesiologist’sclose supervision. For example, thefeedback systems that were shown areSISO, whereas the goal of the anesthesi-ologist is to maintain several physiolog-ical variables in specified ranges. Forinstance, under BIS control, no action istaken by the controller if the MAP is toohigh or too low.

Further, the benefits of multidrug an-esthesia are not yet exploited. At present,we are using Isoflurane alone, and we arenot considering other drugs in our controlschemes. Opiates and muscle relax-ants—if used in combination withhypnotics—can provide beneficial effectsin clinical practice [56, 38].

Another open issue is the administra-tion of analgesics, for which an open-loop infusion policy is still adopted clini-cally. As pointed out in the overview,there are no specific measures of the an-algesic state of the patient. We have theactuator, but we miss the sensor. The an-algesic state is assessed clinically mainlyon the basis of the reaction of thehemodynamic system to surgical stimu-lation. However, this method is of nogreat help, since the expected reaction ofMAP depends on various nonmeasurablevariables such as the intensity and loca-tion of the stimulation, and the patient’sspecific characteristic reaction to pain.These are some of the open issues thatwill be addressed by our research projectin the future.

Andrea Gentilini wasborn in 1972 in Italy. Heobtained his chemicalengineering degree fromPolitecnico di Milano“summa cum laude” in1997, winning the award“Premio Pastonesi” forthe best presented thesis.

He is currently with the Automatic ControlLaboratory at the Swiss Federal Instituteof Technology, where he obtained post-graduate degrees in information technol-ogy and applied statistics. His research


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interests include modeling of physiologi-cal systems and design of feedback con-trollers for anesthesia.

Christian W. Frei re-ceived his first degree inEE from the Engi-neering School (HTL)Brugg-Windisch (Swit-zerland). He obtained anMSEE from Northwest-ern University in 1995and a PhD from the

Swiss Federal Institute of Technology(ETH) in 2000. He has been working forLandis & Gyr Corporation, Zug (Switzer-land) since 1994 and is currently withBT&T Asset Management AG, Urdorf(Switzerland). His interests concern mod-eling and control of non-technical systems.

Adolf Hermann Glatt-felder received his Di-ploma in mechanicalengineering in 1964from ETH Zurich/Swit-zerland, and his Ph.D.degree from the sameinstitution in 1969. Hishabilitation for auto-

matic control was accepted by ETH Zu-rich in 1973. From 1976 to 1991 he wasresearch and line manager with SulzerLimited. In 1991 he returned to the Auto-matic Control Laboratory. His main re-search interests are feedback control inanesthesia, design methods adapted to in-dustrial control systems, with input andoutput constraints.

In 1994 Manfred Mor-ari was appointed headof the Automatic Con-trol Laboratory at theSwiss Federal Instituteof Technology (ETH) inZurich. Before that hewas the McCollum-Corcoran Professor and

Executive Officer for Control and Dy-namical Systems at the California Insti-tute of Technology. He obtained thediploma from ETH Zurich and the Ph.D.from the University of Minnesota. His in-terests are in hybrid systems and the con-trol of biomedical systems. In recognitionof his research he received numerousawards, among them the Eckman Awardof the AACC, the Colburn Award, and theProfessional Progress Award of theAIChE, and he was elected to the NationalAcademy of Engineering (US). ProfessorMorari has held appointments with ExxonR & E and ICI and has consulted interna-

t ional ly for a number of majorcorporations.

Thomas J. Sieber re-ceived his M.D. degreein 1985 from the Uni-versity of Zurich and iscurrently working as astaff anesthesiologist atthe University Hospitalin Bern, Switzerland.His interests include

cardiovascular anesthesia, feedback con-trol and general management.

Rolf Wymann was bornin Switzerland, 1958. Heobtained his M.D. fromthe University of Bern in1983. In the period1985-1986 he did re-search in the field of ad-verse drug reactions inthe team of the Compre-

hensive Hospital Drug Monitoring in Bern.In the period 1987-1990 he spent his resi-dency in Internal Medicine in Bern andBiel, Switzerland. Since 1991 he is staffanesthesiologist at the University Hospitalin Bern. His research interests involvepharmacokinetic-pharmacodynamic mod-eling of anesthetic drugs and feedback con-trol in anesthesia.

Thomas W. Schnider re-ceived his M.D. degreefrom the University ofBern, Switzerland, in1984, where he is cur-rently working as a staffanesthesiologist. From1993 to 1995 he workedas a research fellow at

the Department of Anesthesiology of Stan-ford University in clinical pharmacology.His main research interests are inpharmacokinetic/pharmacodynamic mod-eling and feedback control in anesthesia.

Alex M. Zbinden re-ceived his first M.D.from the University ofBern and went throughhis training as a surgeonand anesthesiologist atthe University Hospitalin Basel and obtained hisqualification as anesthe-

siologist from that institution in 1983. Prof.Zbinden’s first experience with feedbackcontrol in anesthesia was obtained in 1985in Basel. He continued to work on this fieldafter becoming the head of the research de-partment in the Institute of Anesthesiology

and Intensive Care in the University Hos-pital of Bern. His main fields of interest arefeedback control of anesthesia, dosage ofinhaled anesthetics, pain perception, andquality assurance in anesthesia.

Address for Correspondence :Manfred Morari, Automatic Control Li-brary, ETH Zentrum, ETH I 29, CH8092, Zurich Switzerland. Tel: +41 1632 7626. Fax: +41 632 1211. E-mail:[email protected].

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Multitasked closed-loop control in anesthesia