Knowledge-Based Systems 31 (2012) 1–7

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Knowledge-Based Systems

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Design of an assistive anaesthesia drug delivery control using knowledgebased systems

Divya Agrawal a,⇑, Sanjeev Kumar a, Amod Kumar a, Satinder Gombar b, Anjan Trikha c, Sneh Anand d

a Central Scientific Instruments Organisation, Sector 30-C, Chandigarh 160 030, Indiab Government Medical College and Hospital, Sector 32, Chandigarh 160 030, Indiac All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110 029, Indiad Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110 016, India

a r t i c l e i n f o

Article history:Received 14 June 2011Received in revised form 12 January 2012Accepted 12 January 2012Available online 25 January 2012

Keywords:Anaesthesia drug deliveryBispectral indexFuzzy logicFuzzy proportional-integral-derivativecontrollerDepth of Hypnosis

0950-7051/$ - see front matter � 2012 Elsevier B.V. Adoi:10.1016/j.knosys.2012.01.012

⇑ Corresponding author. Tel.: +91 07122637492; faE-mail addresses: divyaagrawal11@gmail.com (D

mail.com (S. Kumar), csioamod@yahoo.com (A. Kumcom (S. Gombar), anjantrikha@hotmail.com (A. Trikha(S. Anand).

a b s t r a c t

Manual methods used during anaesthesia, to decide and deliver the quantity of drug, required significanteffort from the clinical standpoint not guaranteeing an optimal performance. Delivering adequate anaes-thesia requires precise automation in anaesthesia drug delivery system which will improve the patientsafety, reduce the cost due to minimal consumption of drug and will help in early post-operative recov-ery. The present study discusses a fuzzy proportional-integral-derivative (fuzzy PID) based controller tosuggest the change in quantity of isoflurane to be delivered to the patient, for the maintenance of thedesired anaesthetic depth, as targeted by the anaesthesiologist. Depth of Hypnosis (DoH) of the patientis measured using BIS™ index and is used as the measured variable in the controller designed for themaintenance of anaesthesia during surgery. The fuzzy PID controller efficiently deals with the nonlinear-ity of physiological systems.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

Anaesthesia has been defined as ‘that state which ensures thesuppression of the somatic and visceral sensory components andthus the perception of pain’ [1]. In case of balanced anaesthesia,the four major goals to achieve are: (1) Hypnosis, (2) Analgesia,(3) Amnesia and (4) Neuromuscular Blockade [2–4]. It has been amajor issue of research for past many decades to achieve an opti-mal control over all four components. Traditionally, an anaesthesi-ologist assesses the state of the patient on the basis of experienceand decides the quantity of the drug (bolus) and changes requiredin the drug dose during surgery. With the advent of several newDepth of Anaesthesia (DoA) indices, efforts have been directed to-wards designing of an automatic control for anaesthetic drug deliv-ery system [5].

Commonly known methods for controlling anaesthetic drugadministration are: open and closed-loop control. Automatic con-trol of anaesthetic drug with closed-loop control system is a verycritical and demanding task. This leads to the need of identifyingsuch a control mechanism which can adjust to the critical condi-tions surfacing while the course of surgery judiciously. Different

ll rights reserved.

x: +91 07122657614.. Agrawal), virdi205@rediff-ar), dr_sgombar@rediffmail.-), snehanand@rediffmail.com

knowledge-based-systems have been reported in literature andwere found robust enough to deal with a variety of medicinalemergencies efficiently [6,7]. In this study, we also report a hybridknowledge based control scheme for controlling the anaestheticdrug delivery.

The complexity of the human body makes it difficult to achieveall the goals of anaesthesia. Thus, to develop such a system, the ba-sic components required are: (1) an adequate measured variable toreflect the drug effect; (2) an accurate set-point for this variable,which is the chosen target value specified by the anaesthesiologist(in our case); (3) a stable controller for the actuator; (4) the controlactuator and (5) a system, which in case of anaesthesia drug deliv-ery system is a patient [8].

In the present study, these components are: (1) measured var-iable – Bispectral Index; (2) set point – anaesthesiologist is to en-ter the target value, i.e., set BIS (SBIS), the desired region wherethe anaesthesiologist wants the patient to reside during surgery;(3) controller – Fuzzy Proportional-Integral-Derivative (FPID)based controller designed in the study. The fuzzy-PID based con-troller use: (1) error ‘e’ between the set BIS (SBIS) value and theactual BIS (ABIS) value corresponding to the current state of thepatient and (2) the rate of change of error ‘de/dt’, as its two inputvariables to predict the need of change in the drug dose requiredto maintain the desired anaesthetic depth of the patient. The con-troller developed at this stage acts as an assistive drug deliverysystem.

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2. Materials and methods

2.1. Patients

After proper institutional ethical clearance and written in-formed consent from the patients, the controller trials were donein collaboration with the Government Medical College and Hospi-tal, Chandigarh and All India Institute of Medical Sciences, NewDelhi. The system trial was done on 25 patients (15 female and10 male), in the age group of 18–60 years (38 ± 10.88), havingweight between 48 and 93 kg (62 ± 14), of ASA physical status I/II (demographic data given in Table 1) who were scheduled to un-dergo non thoracic, non vascular and non neurosurgical proceduresrequiring general anaesthesia (GA). Patients taking psychoactivedrugs including alcohol were excluded from the study.

2.2. Experiment

The selected patients were kept on fasting 6 hours prior to sur-gery and premedicated with tablet alprazolam 0.25 mg or equiva-lent in the morning before surgery. Once the patient is in theoperating room, an intravenous (IV) line was thereafter started. Pa-tients were pre-oxygenated for 3 min and then general anaesthesiawas induced using IV Propofol 2–3 mg kg�1, and Fentanyl2 lg kg�1. Vecuronium 0.1 mg kg�1 was used to facilitate endotra-cheal intubation using appropriate size cuffed endotracheal tube.Anaesthesia was maintained with O2/Air [40:60] and isoflurane[MAC of 0.5–1] with a fresh gas flow of 2–3 L per minute to main-tain an end tidal CO2 of 35 mmHg using closed circuit with circleabsorber. After the completion of surgical procedure, the residualneuromuscular blockade was antagonized with IV Glycopyrrolate0.01 mg kg�1 and Neostigmine 0.04 mg kg�1. Patients were thenextubated and shifted to Post Anaesthetic Care Unit (PACU) for fur-ther monitoring and care.

The general anaesthesia process is a combination of three dis-tinct phases which are induction followed by intubation, mainte-nance of anaesthesia during the surgery and recovery. Theamount of intravenous drug (Propofol) to be administered is theinduction drug dose which was estimated using a fuzzy logic basedestimator. The maintenance of anaesthesia can be done with the

Table 1Demographic data of the patients.

Patient ID No. Age Sex Weight (in kg) Height (in cm)

1 24 F 40 152.42 33 M 86 172.73 39 F 57 152.44 53 F 65 160.025 53 F 80 165.16 37 F 58 157.487 30 F 50 147.328 40 F 65 162.569 36 F 57 154.94

10 35 F 50 154.9411 24 M 50 177.812 33 F 50 157.4813 40 F 48 152.414 55 M 49 162.5615 34 F 81 160.0216 50 F 93 162.5617 40 F 67 167.6418 35 M 60 152.419 35 M 90 165.120 29 M 55 165.121 20 M 56 165.122 18 M 55 152.123 60 M 60 177.824 26 M 65 165.125 35 F 58 160.02

help of the designed controller by administering the inhalationalagent (Isoflurane).

2.3. Induction drug dose estimation

The general anaesthesia was induced using Propofol and afterinduction the anaesthesia was maintained using Isoflurane. Theamount of drug required to induce anaesthesia is the inductiondrug dose which was estimated by using a fuzzy model. The anaes-thetic dose bears a relationship with the body surface area, age andgender of the patient. Body surface area (BSA) is given by [9]:

BSA ¼ 0:007184� ðweightÞ0:425 � ðheightÞ0:725 ð1ÞHere, height is in centimeters and weight is in kilograms. The fuzzymodel uses the age and BSA as the input variable and gives the ini-tial dose as output [10].

2.4. Maintenance of anaesthesia

Once the bolus amount of propofol is delivered, the amount ofisoflurane required to maintain the desired anaesthetic depth ofthe patient can be estimated by the fuzzy-PID controller. The BIS™monitor from Aspect Medical Systems was used to get the BIS in-dex values throughout the surgery. The BIS electrode was posi-tioned on the forehead of the patient, i.e., the frontal polar regionof the brain (as specified by the manufacturer). The BIS valuewas then continuously taken via serial port and was fed to the con-troller. The anaesthesiologist was asked to enter a set-point, i.e.,the target BIS (SBIS) index around which the patient is most likelyto be in ‘deep sleep’ state during the surgery.

In fuzzy-PID based controller: error ‘e’ between the target BIS(SBIS) value and the actual BIS (ABIS) value corresponding to thecurrent state of the patient and the rate of change of error ‘de/dt’,are fed as the two input variables to a fuzzy inference system, whichgenerate the values for the three PID coefficients Kp, Ki and Kd. Thesecoefficients are then given to the PID algorithm to predict the re-quired increment/decrement in the current drug dose.

The drug delivery is not controlled directly by the controller;the system only suggests the anaesthesiologist to make a suitablechange in the amount of isoflurane to be delivered to the patientfor maintenance of desired anaesthetic depth. The anaesthesiolo-gist makes the final decision about the drug dose delivered.

3. Theory

The primary goal of control engineering is to distil and applyknowledge about how to control a process so that the resultingcontrol system will reliably and safely achieve high-performanceoperation.

The key characteristic of control is to interfere, to influence or tomodify the process. This control function or the interference to theprocess is introduced by an organization of parts that, when con-nected together is called the Control System. Control systems are alsobroadly classified as: Open loop systems and Closed Loop systems.

Systems that utilize feedback are called closed-loop control sys-tems. There are two common classes of closed loop control sys-tems, with many variations and combinations: logic or sequentialcontrols, and feedback or linear controls. There is also fuzzy logic,which attempts to combine some of the design simplicity of logicwith the utility of linear control.

3.1. Proportional integral derivative (PID) control

As the name suggests, the PID algorithm consists of three basicmodes, the Proportional mode, the Integral and the Derivativemodes.

D. Agrawal et al. / Knowledge-Based Systems 31 (2012) 1–7 3

� Proportional mode (P): responds quickly to changes in errordeviation.� Integral mode (I): is slower but removes offsets between the sys-

tem’s output and the reference.� Derivative mode (D): Derivative action (also called rate or pre-

act) anticipates where the process is heading by looking at thetime rate of change of the controlled variable. TL is the ‘ratetime’ and this characterises the derivative action. It speeds upthe system response by adding in control action proportionalto the rate of change of the feedback error. Derivative actiondepends on the slope of the error, unlike P and I. If the erroris constant derivative action has no effect.

‘‘PID control’’ is the method of feedback control that uses thePID controller as the main tool. The controller output is equal tothe proportional gain (Kp) times the magnitude of the error plus

Table 2Effects of increasing each of the controller parameters Kp, Ki and Kd on PIDcharacteristics.

Coefficients Response

Rise time Overshoot Settling time Steady state error

Kp Decrease Increase No trend DecreaseKi Decrease Increase Increase EliminateKd No trend Decrease Decrease No trend

Fig. 1. Basic components of a Fuzzy Controller: (1) ‘‘rule-base’’, (2) inferencemechanism, (3) fuzzification interface, and, (4) defuzzification interface.

Fig. 2. Fuzzy-PID based controller. The controller uses error ‘e’, between the target BIS (Spatient and the rate of change in this error e, as the two input variables for a fuzzy inferThese coefficients are then fed to the PID algorithm to predict the required increment/dderivative of error).

the integral gain (Ki) times the integral of the error plus the deriv-ative gain (Kd) times the derivative of the error.

u ¼ Kp eðtÞ þ Ki

ZeðtÞdt þ Kd deðtÞ=dt ð2Þ

When dealing with the PID, the four major characteristics of theclosed-loop step response to be considered are:

(a) Rise time: the time it takes for the system output to risebeyond 90% of the desired level for the first time.

(b) Overshoot: how much the peak level is higher than thesteady state, normalized against the steady state.

(c) Settling time: the time it takes for the system to converge toits steady state.

(d) Steady-state error: the difference between the steady-stateoutput and the desired output.

The effects of increasing each of the controller parameters Kp, Ki

and Kd are as summarized in Table 2.Thus, the PID controller can be understood as a controller that

takes the present, the past, and the future of the error into consid-eration and gives it an edge over other controllers (like P, PI con-trol). Due to its simplicity and excellent if not optimalperformance in many applications, PID controllers are used inmore than 95% of closed-loop processes.

3.2. Fuzzy logic control

Fuzzy control provides a formal methodology for representing,manipulating, and implementing a human’s heuristic knowledgeabout how to control a system. Such knowledge based systemshave been reported earlier for application like computer-aided-drug-design and were found to be remarkably efficient in utilizingexpert’s knowledge for controlling the process [11].

Fuzzy logic can be used efficiently for controlling the non-linearsystems, hard to model, as it does not need a model for its func-tionality. In fuzzy logic control, the main focus is on the intuitiveunderstanding of the control process and this information is di-rectly loaded to fuzzy by means of membership functions andrules. The fuzzy logic operates on linguistic variables and ‘‘If–Then’’rules.

The fuzzy controller (as shown in Fig. 1) has four main compo-nents: (1) the ‘‘rule-base’’ holds the knowledge, in the form of aset of rules, of how best to control the system, (2) The inference

BIS) value and the actual BIS (ABIS) value corresponding to the current state of theence system, which generates the values for the three PID coefficients Kp, Ki and Kd.ecrement in the current drug dose (e = error, e = derivative of error and ë = double

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mechanism evaluates which control rules are relevant at the currenttime and then decides what the input to the system should be, (3)The fuzzification interface simply modifies the inputs so that theycan be interpreted and compared to the rules in the rule-base,and, (4) the defuzzification interface converts the conclusionsreached by the inference mechanism into the inputs to the system.

The fuzzy controller takes inputs in the form of fuzzy sets. Fuzzysets are used to quantify the information in the rule-base, and theinference mechanism operates on fuzzy inputs to produce fuzzysets; hence, we must specify how the fuzzy system will convertits numeric inputs into fuzzy sets (a process called ‘‘fuzzification’’)so that they can be used by the fuzzy system.

The inference process generally involves two steps:

(a) The premises of all the rules are compared to the controllerinputs to determine which rules apply to the current situa-tion. This ‘‘matching’’ process involves determining the cer-tainty that each rule applies, and typically, we will morestrongly take into account the recommendations of rulesthat we are more certain apply to the current situation.

(b) The conclusions (what control actions to take) are deter-mined using the rules that have been determined to applyat the current time. The conclusions are characterized witha fuzzy set (or sets) that represent the certainty that theinput to the system should take on various values.

Defuzzification operates on the implied fuzzy sets produced bythe inference mechanism and combines their effects to provide the‘‘most certain’’ controller output (system input). There are manydifferent approaches available for defuzzification.

The fuzzy control design process is nothing more than a heuris-tic technique for the synthesis of nonlinear controllers. The shapeof this nonlinearity is what determines the behaviour of the

Fig. 3. Membership functions of input variables: error and rate of change of error;used in the FPID fuzzy inference system.

closed-loop system, and it is the task of the designer to get theproper control knowledge into the rule-base so that this nonlinear-ity is properly shaped [12].

4. Discussion

There is still no universally acclaimed sensor able to measurechanges in depth of anaesthesia. One point which is agreed uponby most of the researchers is that alteration in consciousness isat least a required component of anaesthesia. Although conscious-ness itself may be difficult to define, it has been shown that deriv-atives of the electroencephalogram can correlate with changes inconsciousness [13–16]. Thus sensors now exist to measure at leastone component of the anaesthesia, making it amenable to closed-loop control. This has been a major break-through in the field ofdeveloping clinically acceptable closed-loop anaesthesia drugdelivery system. However, improvements in sensor technology

Fig. 4. Membership functions of PID coefficients to be provided as output of FPIDfuzzy inference system.

D. Agrawal et al. / Knowledge-Based Systems 31 (2012) 1–7 5

and artifact detection and elimination remain challenges for theirroutine use in closed-loop anaesthesia [17].

Many closed loop drug delivery control systems have been pro-posed by various researchers following different approaches tilldate. The major approaches used for the development of automaticdrug delivery systems are: (i) open-loop control with the help oftarget-controlled infusion systems (TCI) [18,19]; (ii) closed-loopcontrol with a TCI-system as the control actuator [20,21]; and(iii) closed-loop control without TCI-systems and direct calculationof the drug infusion rate [22–27].

4.1. Fuzzy-PID controller

Human physiological behavior being highly non-linear makesthe use of PID control for drug delivery purpose quite undesirable.Since the PID controllers are found to be quite ignorant about therate of drug metabolism, they are in some cases slow to establishcontrol and might prove dangerous also because of the oscillationsoccurring before the steady state is achieved. To overcome theseshort comings as well as to design a controller which will not berequiring an analytical model of the body, knowledge based systemlike fuzzy logic was used with the conventional PID. Thus, the com-bined strength of fuzzy logic and PID controllers is used to dealwith the problem of automation of anaesthesia.

The basic block diagram for the fuzzy-PID controller is as shownin the Fig. 2. The controller comprises of two basic blocks: a fuzzyinference system for calculation of PID coefficients and a PID con-trol algorithm to determine the inhalational drug amount variationusing the coefficients provided by the fuzzy inference system.

The fuzzy inference system receives two inputs and providesthree outputs. ‘Mamdani’ fuzzy inference system was used fordefining membership functions and rule base. Every rule consistedof two parts – Premise part of the rule operated in the fuzzy sub-space of inputs while the Consequent part described the outputwithin the fuzzy subspace of output. Defuzzification was doneusing ‘Centroid rule’. The inputs to the fuzzy are: the error e (be-tween the target BIS and the current BIS) and the rate of changeof error (de/dt). Both the inputs were in the range of (�100, 100)and over each input seven membership functions were defined

Fig. 5. GUI demonstrating the wo

(Fig. 3). There is no fixed rule for deciding the shape of the mem-bership functions, thus various membership function shapes weretried and the configuration resulting in most optimal performancewas selected.

Since seven membership functions were associated with eachinput, the input space was partitioned in 72 = 49 fuzzy subspaceseach of which was governed by a fuzzy if-then rule.

In this study, the fuzzy control rules design is based on therequirement that the DoA index of the patient approach the targetquickly and in a stable way. For different |e| and |de/dt|, parametersKp, Ki and Kd are adjusted by the self-tuning control process. Forexample, when |e| is smaller, in order to have good steady perfor-mance, one should increase Kp and Ki; simultaneously in order toavoid the system round settings point appear oscillation phenom-enon. The basic principle is that when |de/dt| is smaller, Kd is larger,usually the medium-sized, and when |de/dt| is larger, one shouldtake Kd smaller.

The fuzzy set values for initial dose determination used follow-ing linguistics:

rki

NS: negative small

ng of fuzzy-PID controller.

ZO: zero

PS: positive medium NM: negative medium PM: positive small NB: negative big PB: positive big

Output of the model provided three coefficients Kp (�3, 3), Ki

(�0.06, 0.06) and Kd (�0.5, 0.5) which also had seven membershipfunctions each (Fig. 4). These coefficients were then used in the PIDcontrol algorithm.

The output variable ‘u’ of the PID controller is calculated as:

u ¼ Kp yð1Þ þ K i yð2Þ þ Kd yð3Þ ð3Þ

where Kp, Ki and Kd are the coefficients obtained through the fuzzylogic, y(1) = derivative of error, y(2) = error, y(3) = double derivativeof error.

This calculated control variable ‘u’ gives an indication regardingthe amount of drug need to be increased or decreased and at whichrate the increment and decrement is to be done in the drug dose sothat the desired anaesthetic depth could be achieved, i.e., if the

Fig. 6. Set up established inside the operation theatre.

Fig. 7. Graph depicting the response of the FPID control system to regulate the drugdose to keep the patient in desired anaesthetic depth. The figure illustrates thatwhen the ABIS goes below the SBIS value the drug dose being delivered to thepatient at the moment needs to be decreased and when ABIS surpasses SBIS thedrug dose is to be increased, when ABIS equals SBIS then the drug dose is to bemaintained constant.

6 D. Agrawal et al. / Knowledge-Based Systems 31 (2012) 1–7

patient is in desired sleep state and there is a steep drop in the BISvalue of the patient signifying a rapid shift of the patient state fromdesired sleep state to very deep sleep state, the amount of drug willbe reduced and that too at a higher rate. Similarly, if there is a grad-ual drop then the drug will be reduced correspondingly at a slowerpace.

The system is designed on the MATLAB™ platform and the trialswere conducted using the developed GUI shown in the Fig. 5. Theset up established for trial in the operation theatre is shown inFig. 6.

5. Results

The controller designed was precisely able to predict the drugincrement/decrement depending on the current anaesthetic depthof the patient. The predicted change in drug dose was in agreementwith that decided by the anaesthesiologist in charge. The adjust-ment of rate of drug dose increment/decrement was also approvedby the anaesthesiologist monitoring the state of the patient. Thecontroller response is shown in the Fig. 7.

The figure consists of two simultaneously plotted graphs. Thefirst graph shows the Set-BIS (SBIS) and the Actual-BIS (ABIS) plot-ted with respect to the time. The blue line is for the SBIS and thered line is for the ABIS. The second graph indicates the drug dosewith respect to time. The blue dotted lines mark the crossing overpoints (where ABIS undershoot/overshoot the SBIS). The figureillustrates that when the ABIS goes below the SBIS value the drugdose being delivered to the patient at the moment needs to be de-creased and when ABIS surpasses SBIS the drug dose is to be in-creased, when ABIS equals SBIS then the drug dose is to bemaintained constant. The drug dose is estimated after receivingthe ABIS value from the BIS monitor so there is a lapse of 0.329 sec-onds in a single iteration so the time axes are not same; the differ-ence is due to the calculation time lapse.

The controllers are also provided with a feature of setting thealarm range (higher fixed point and lower fixed point) of the BIS in-dex, where the anaesthesiologist will like the patient to lie duringsurgery. If the patient’s present BIS index lies outside this range acontinuous beep alarm will ring until the present BIS index fallsin the predefined range.

The nonlinearity of the human system is a demanding issuewhich can be easily solved by making use of the fuzzy-PID whichtakes into consideration all the present, past and future

adjustments that may be required for the proper functioning ofthe control system.

6. Conclusion

The fuzzy-PID controller was tested and the controller’s perfor-mance was evaluated in unison with the anaesthesiologists. Thecontroller gave a reliable indication for increment/decrement ofthe drug dose. The fuzzy PID controller overcame the problemsbeing faced with the controllers solely using PID design approach.The fuzzy PID controller was rapid to establish control and is notrisky to use because there are no oscillations occurring in the re-sponse while reaching the desired plane. This also strengthensthe point that the use of conventional PID approach in sync withthe Knowledge based systems help in controlling the non-linearsystems efficiently.

Anaesthesiologists were satisfied by the performance of thecontroller in terms of accuracy of predicting the required incre-ment of drug dose as well as its automatic adjustment of the rateof drug delivery. In approximately 90% cases the anaesthetistswere in agreement with the drug dose variation predicted by thecontroller, however, in some exceptional cases wherein the anaes-thetists were facing some complications or they were using localanaesthetic agents also, their decision was different from the con-troller’s prediction.

The system designed can be easily integrated with the existinganaesthesia drug delivery machines, thereby, not increasing anymore machines in the already overcrowded operation theatre.The anaesthesia drug delivery machines already consist of multi-parameter monitor, vaporizer etc, and the controller can be incor-porated with the machine to regulate the concentration of theinhalational anaesthetic agent being delivered to the patient.

The proposed controller behaves as an assistive device in thecurrent scenario. The development of fully automated anaesthesiadrug delivery system will need much more refinements and efforts

D. Agrawal et al. / Knowledge-Based Systems 31 (2012) 1–7 7

are directed in this direction. In the future, the authors aim to de-velop a drug delivery system which may control all the compo-nents of balanced anaesthesia (i.e., hypnosis, analgesia andneuromuscular blockade) simultaneously. Until such control sys-tem can be designed and tested successfully, removing an anaes-thesiologist completely from the scene seems a distant dream.Although, the field of automation of anaesthesia still requires sig-nificant efforts, the proposed fuzzy-PID controller is a promisingapproach to achieve the goal.

Acknowledgment

We sincerely thank Director, Central Scientific InstrumentsOrganization (CSIR-CSIO, Chandigarh) for giving us the opportunityto work upon this project. We are also grateful to the physiciansand technical staff of Government Medical College and Hospital,Chandigarh and All India Institute of Medical Sciences, New Delhifor their kind support and cooperation.

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