Review: Intelligent Modeling and Control in Anesthesia · Journal of Medical and Biological...

Journal of Medical and Biological Engineering, 32(5): 293-308 293

Review: Intelligent Modeling and Control in Anesthesia

Jheng-Yan Lan1,2 Maysam F. Abbod3 Rong-Guan Yeh2

Shou-Zen Fan4 Jiann-Shing Shieh2,5,*

1Department of Anesthesiology, National Taiwan University Hospital in Yuan Lin Branch, Yuanlin 640, Taiwan, ROC 2Department of Mechanical Engineering, Yuan Ze University, Chungli 320, Taiwan, ROC

3School of Engineering and Design, Brunel University, London UB8 3PH, United Kingdom 4Department of Anesthesiology, College of Medicine, National Taiwan University, Taipei 106, Taiwan, ROC

5Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chungli 320, Taiwan, ROC

Received 5 Sep 2011; Accepted 17 Feb 2012; doi: 10.5405/jmbe.1014

Abstract

This paper provides a detailed review of the clinical aspect and engineering view of how to measure, interpret,

model, and control general anesthesia. The mechanisms of anesthesia in terms of unconsciousness, amnesia, analgesia,

and akinesia in modern balanced anesthesia are reviewed. The assessment and interpretation of anesthesia according to

clinical signs (i.e., lacrimation, sweating, papillary dilatation), physiological monitors (i.e., electromyography,

electrocardiography, blood pressure, electroencephalography, oxyhemoglobin saturation), and evaluation indices (i.e.,

bispectral index scale, entropy, auditory evoked potentials, and surgical stress index) are reviewed in order to define the

objectives of general anesthesia. Finally, the intelligent modeling and control of anesthesia are thoroughly reviewed.

Modern general anesthesia is moving towards the monitoring, interpretation, modeling, and control of multi-inputs

from quantitative and qualitative nonlinear physiological signals and multi-outputs for drug control of unconsciousness,

amnesia, analgesia, and akinesia. A multistage hierarchical system should thus be developed for the modeling and

control of general anesthesia in the future.

Keywords: General anesthesia, Unconsciousness, Amnesia, Analgesia, Akinesia, Intelligent systems, Modeling and

control

1. Introduction

General anesthesia is a reversible change that includes

unconsciousness, amnesia, analgesia, and akinesia, with

concomitant stability of the cardiorespiratory and autonomic

systems [1-5]. In order to reach the proper anesthetic state for

surgery, hypnotic and analgesic effects are considered as major

fundamental pharmacologic components. Under general

anesthesia, memory and awareness are critical components of

the depth of anesthesia (DOA). However, anesthesiologists

have multiple inconsistent definitions of the anesthetic state

and have no standard measurement to assess it. Analgesia is an

important aspect of balanced anesthesia, but there is no

commercially available monitor for its evaluation. Therefore, in

clinical practice, anesthesiologists must provide specific care

during surgical procedures, including neuromuscular relaxation

or paralysis, adequate DOA, and analgesia. These interacting

requirements necessitate a balanced administration of suitable

drugs.

* Corresponding author: Jiann-Shing Shieh

Tel: +886-3-4638800 ext. 2470; Fax: +886-3-4558013

E-mail: [email protected]

Direct measurements of blood pressure (BP), respiratory

parameters, temperature, blood oxygen saturation, and other

related vital signs are feasible. The patient can be controlled by

manipulating the monitored values, but the response is often

delayed. For example, when tachycardia and hypotension

occur, the total amount of blood loss is large and the

hypoperfusion continues for a period of time. Furthermore,

direct measurements cannot provide sufficient information of

the autonomic nervous system (ANS) and central nervous

system (CNS), which are related to the DOA, level of surgical

stress, and nociceptive changes. Biological systems are

spatially and temporally complex systems connected by

dynamic interconnected feedback loops. Thus, although direct

measurements of vital signs are available, precise modeling and

anesthesia control are very challenging for anesthesiologists.

Quantitative modeling approaches are being replaced by

qualitative techniques, especially in the artificial intelligence

field. Hybrid models (quantitative/qualitative) and hybrid

intelligent algorithms (neural networks, fuzzy logic,

evolutionary computing) have been applied to anesthesia [6].

This review is divided into two sections. The first section

reviews the mechanisms and effects of anesthetics and how to

assess the anesthesia. The second section reviews intelligent

modeling and control systems for anesthesia.

J. Med. Biol. Eng., Vol. 32 No. 5 2012 294

2. Mechanisms of anesthesia

The most commonly used inhaled anesthetics in modern

anesthesia are a single gas (i.e., nitrous oxide) and volatile

liquids (i.e., halothane, enflurane, isoflurane, desflurane, and

sevoflurane). The immobilizing and hypnosis/amnesia effect of

inhaled anesthetics involves sites of action in the spinal cord and

supraspinal mechanism, respectively. Each agent has a

complicated molecular mechanism and multiple targets

contribute to the effects. Nonopioid intravenous anesthetics such

as barbiturates (i.e., thiopental, methohexital), benzodiazepines

(i.e., midazolam), propofol, ketamine, and etomidate, have an

important role in modern anesthesia practice. They are widely

used to facilitate the rapid induction of anesthesia or to provide

sedation during monitored anesthesia care. With the introduction

of propofol, intravenous anesthesia has become more popular as

a component of anesthesia maintenance. However, currently

available intravenous anesthetics are not ideal anesthetic drugs

in the sense of producing all and only desired effects (hypnosis,

amnesia, analgesia, immobility). Therefore, balanced anesthesia

with multiple drugs (neuromuscular-blocking drugs, inhaled

anesthetics, and opioids) is generally used.

2.1 Mechanisms of unconsciousness

Most anesthetics induce unconsciousness by changing

neurotransmission, either increasing the primary inhibitory

neurotransmitter gamma-aminobutyric acid (GABA) or

decreasing the activation of the primary excitatory N-methyl-

D-aspartate (NMDA) receptors [7,8] at multiple sites in the

cerebral cortex [9-11], thalamus, and brain stem [12]. A

positron-emission tomographic study showed that cortical

metabolic activity is decreased after general anesthesia [13],

and functional magnetic resonance imaging has shown

evidence of unconsciousness in the cerebral cortex [10].

Hypnotic drugs administered during induction rapidly reach

brain-stem arousal centers and contribute to unconsciousness.

Furthermore, the drugs act on several nuclei in the area,

resulting in impaired brain-stem function which is manifested

as apnea [14], atonia [15], and loss of normal reflexes, such as

oculocephalic and corneal reflexes [16]. In the central

thalamus, anesthetics control the normal arousal system [17].

Most anesthetics interact with various molecular sites of

neuronal action via ligand-gated or voltage-gated channels on

the main targets and cause hyperpolarization of neurons in

thalamocortical loops, disrupting the connectivity in the cortex

[18]. In the mammalian CNS, GABA is the main inhibitory

neurotransmitter and its fast inhibitory effects are controlled

through GABAA receptors. Barbiturates, etomidate, propofol,

sevoflurane, enflurane, desflurane, and isoflurane are GABA-

agonist agents [9], which increase the sensitivity of receptors to

GABA, prolong the inhibitory post-synaptic effect after a

GABA release, and increase the inhibition of postsynaptic

neuronal excitability [18] at clinically effective concentrations.

Glutamate is the major excitatory neurotransmitter in the

CNS. Ketamine, nitrous oxide, and xenon can inhibit glutamate

effects through the NMDA receptor [18-20]. Most hypnotic

agents produce low electroencephalography (EEG) patterns

under unconsciousness conditions, whereas NMDA antagonists,

such as ketamine, are associated with active EEG patterns.

When ketamine is used with propofol, an additive anesthetic

interaction occurs that is not reflected in the bispectral index

scale (BIS) [21]. Thus, the addition of ketamine to an anesthetic

complicates the interpretation of the processed EEG, again

emphasizing the need to consider the clinical circumstances

and drugs used when interpreting a DOA index.

2.2 Mechanisms of analgesia induced by general anesthesia

Intravenous or inhalational anesthetics are used for

hypnotic effects, but are ineffective at eliminating the

hemodynamic response to high-stress stimuli even with large

doses [22,23]. Opioids are typically combined with hyponotic

drugs to create a state of non-responsiveness. Opioids are unique

in producing analgesia without loss of touch, proprioception, or

consciousness. Opioids suppress pain by their action in the

brain, spinal cord, and peripheral nervous system through

agonists, partial agonists, and mixed agonists- antagonists, with

opioid receptors (μ-,δ-, κ-opioid receptors). The principal effect

of opioid receptor activation is a decrease in neurotransmission,

which results mostly from presynaptic inhibition of

neurotransmitter release (e.g., acetylcholine, dopamine,

norepnephrine, substance P) [24] and increased potassium

conductance, leading to hyperpolarization of cellular

membranes. Opioids produce analgesic effects by directly

inhibiting the ascending transmission of nociceptive stimulation

from the spinal cord dorsal horn and activate pain control

circuits via the descending transmission of the rostral

ventromedial medulla.

Opioids affect many organ systems, including the

respiratory and cardiovascular systems, and can cause a variety

of adverse effects. The pharmacokinetic and pharmacodynamic

properties of opioids are affected by a variety of factors, such

as age, body weight, organ failure, and shock. Proper dosing

and monitoring allow the adverse effects to be minimized.

Increasing the concentration of inhaled anesthetics produces a

continuum of changes in the EEG and eventually results in

burst suppression and a flat EEG. In contrast, a ceiling is

reached with opioids. Once the opioid dosage is increased to a

critical level, further increases will not affect the EEG [25].

Problems with signal processing need to be resolved before

analysis of the EEG can be used as a routine parameter of

DOA.

2.3 Mechanisms of akinesia induced by general anesthesia

Neuromuscular blockers are used to facilitate endotracheal

intubation and maintain neuromuscular blockade during

surgical procedures. Neuromuscular-blocking drugs interrupt

the transmission of nerve impulses at the neuromuscular

junction and thereby produce paresis or paralysis of skeletal

muscles. Non-depolarizing muscle relaxants produce a

neuromuscular blockade by competing with acetylcholine for

postsynaptic α-subunits. In contrast, succinylcholine, a

depolarizing neuromuscular blocker, produces prolonged

depolarization that results in decreased sensitivity of the

Intelligent Modeling and Control in Anesthesia 295

Table 1. Clinical sign features, power spectra, and entropy analysis evaluation indices for inadequate general anesthesia conditions (EMG:

electromyography; EEG: electroencephalography; ECG: electrocardiography; SpO2: oxyhemoglobin saturation; BP: blood pressure; HRV:

heart rate variability; RRI: R peak interval; FFT: fast Fourier transform; LFP: low-frequency power; HFP: high-frequency power; PPGA:

plethysmograph amplitude; BPV: blood pressure variability; BIS: bispectral index; AEP: auditory evoked potentials; CPC: cardiopulmonary

coupling; LHR: low/high frequency ratio; DFA: detrended fluctuation algorithm; MSE: multi-scale entropy; CI: complex index; SSI: surgical

stress index; and PVI: pleth variability index).

Clinical signs of inadequate general

anesthesia

Monitors Signal

processing

Evaluation

indices

Somatic responses

Motor Movement

Withdrawal

EMG,

Observation;

Power spectral

analysis, BIS, Entropy

BIS,

Entropy AEP Consciousness

Nociception

Awareness,

Pain

EEG,

Subjective

experience;

Autonomic responses

Breathing Breathing pattern

change

Observation;

Respiration rate, volume

Coherence and

Cross-power Spectrum

CPC

(combined with ECG)

Hemodynamic Tachycardia ECG HRV, RRI, FFT,

LFP, HFP

LHR,DFA

(α index), MSE (CI),

SSI, PVI Volume SpO2 PPGA,

Hypertension BP BPV

Sudomotor Sweating Skin

conductivity

Papillary

dilatation

Pupillometry

Lacrimation Observation

Hormonal Catecholamines,

Corticosteroids

Blood drawing and

lab analysis

postsynaptic nicotinic acetylcholine receptor and inactivation

of sodium channels, inhibiting propagation of the action

potential across the muscle membrane.

Doctors often use a peripheral nerve stimulator to titrate

the dose to produce the desired pharmacologic effect. At the

conclusion of surgery, the responses evoked by the nerve

stimulator are used to judge spontaneous recovery from

neuromuscular blockade, which is facilitated by the

administration of anticholinesterase drugs. Muscle relaxants

may confound the accuracy of devices in relation to the DOA.

Fluctuating muscle tension and tonic input from muscle

spindles contribute to CNS arousal. As a result, muscle

relaxants may deepen anesthesia [26-29] by reducing neural

traffic [30]. The reversal of muscle relaxation can also change

the amount of electromyography (EMG) activity and influence

the anesthesia monitor [31].

3. Assessment and interpretation of anesthesia

Inadequate general anesthesia and pain control may cause

side effects and lead to increased morbidity. Although the

assessment of awareness and pain is always based on patients’

subjective reports, there are clinical signs (i.e., lacrimation,

sweating, papillary dilatation), physiological monitors (i.e.,

EMG, electrocardiography (ECG), BP, EEG, oxyhemoglobin

saturation (SpO2)), and evaluation indices (i.e., BIS, entropy,

auditory evoked potential (AEP), surgical stress index (SSI))

that can help give objective reports of general anesthesia.

Table 1 shows clinical sign features and their correlation to

power spectral and entropy analysis evaluation indices (e.g.,

BIS and AEP) for inadequate general anesthesia conditions.

The assessment and interpretation of general anesthesia are

introduced in the following subsections.

3.1 Clinical signs

There are three periods of general anesthesia: induction,

maintenance, and recovery. Different clinical signs can be

observed in the individual stages. When a small dose of a

hypnotic drug such as propofol or a barbiturate is administered,

the patient is in the sedation state and easily arousable. As the

dose is increased, a state of paradoxical excitation is observed,

which is characterized by incoherent speech or purposeless

movements. As the concentration is further increased, an

irregular respiratory pattern develops, which may progress to

apnea. At the same time, muscle tone and response to oral

commands obviously decrease. The corneal and eyelash

reflexes are lost. Tracheal intubation is usually performed at

this state.

A combination of inhalational agents, hypnotic agents,

opioids, and muscle relaxants is used to maintain anesthesia. In

the maintenance period, anesthesiologists use heart rate (HR)

and BP to monitor the level of anesthesia because the clinical

signs are difficult to access. When the HR and BP increase

dramatically, it means that the state of anesthesia is inadequate

for the patient. Other clinical signs that indicate inadequate

general anesthesia are sweating, papillary dilatation, the return

of muscle tone, movement, lacrimation [32], and the hormonal

response [33,34] (i.e., catecholamines, corticosteroids). PRST

(blood pressure, heart rate, sweat, and tear formation) is used to

assess responsiveness. It is used as a standard measure of

autonomic reactions in clinical practice [32].

The return of spontaneous respiration and muscle tone,


HR and BP increases, salivation and tearing, and grimacing,

swallowing, coughing, and defensive movements are the

behavioral and physiological signs of recovery from general

anesthesia. At this point, there is sufficient return of brain-stem

reflexes to maintain spontaneous respiration and airway

protection, and thus the anesthesiologist performs extubation.

3.2 Electrophysiologic monitoring

The EEG represents electrical activity of the cerebral

cortex derived from summated inhibitory and excitatory

post-synaptic activity, which in turn is the result of subcortical

events, including those that pass through subcortical thalamic

nuclei. Anesthetic drugs affect cerebral blood flow, metabolism,

and EEG patterns, which are related to the degree of EEG

activity [30]. For an awake state with closed eyes, EEG is

active and normally shows prominent alpha activity (10 Hz).

After the administration of a hypnotic drug, the patient enters

the sedation state and the EEG shows an increase in beta

activity (13-25 Hz) [5]. In the maintenance period, there are

four phases of EEG. Phase 1 is a light state of general

anesthesia with decreased EEG beta activity (13-30 Hz), and

increased alpha activity (8-12 Hz) and delta activity (0-4 Hz)

[31]. In phase 2, an intermediate state, beta activity decreases

and alpha and delta activities increase [31,32]. During phase 3,

a deeper state, the EEG is characterized by burst suppression

[33]. Surgery is often performed in phases 2 and 3. Phase 4 is

the most profound state, during which the EEG is isoelectric.

EEG patterns were initially used as a measure of the DOA

to define the anesthetic state but a discrepancy seemed to exist

between the clinical signs and EEG patterns, especially during

induction and recovery [35]. Because the effects of the

interactions of several administered anesthetic drugs on EEG

patterns are not known, there is no standard method for

choosing an optimal EEG parameter, and there is no clear

definition for the assessment of clinical DOA, it is difficult to

apply EEG to measure clinical DOA.

3.3 Quantitative EEG-derived indices and monitors

Power spectral analysis, a frequency analysis method that

decomposes an EEG signal into a series of sine waves, is a

traditional method for the quantification of EEG and reflects the

amount of activity in the frequency bands of the EEG signal.

Bispectral analysis was applied to EEG signals to describe the

states of awake and sleeping subjects by Barnett et al. [36] and

Dumermuth et al. [37]. Numerous methods for processing EEG

signals, such as entropy, the patient state index, narcotrend, and

evoked responses, have been evaluated for monitoring DOA.

3.3.1 Bispectral index scale

The BIS value is calculated from a multivariate logistic

regression analysis from a collected database of EEG recordings

from adult volunteers with hypnotic drug concentrations,

behavioral assessments, and clinically important endpoints

[38,39]. The BIS, scaled from 0 to 100, creates a dimensionless

number. Zero represents complete electrical silence of the

cerebral cortex, and 100 represents an alert state. A BIS value of

less than 55 is acceptable during general anesthesia, and that

between 50-60 is associated with low probability of response to

verbal command.

In a prospective cohort study [40], 4945 surgical patients

monitored with the BIS were compared with 7826 similar cases

in a previous study where no cerebral monitoring was used. In

the BIS-monitored group, 0.04% had explicit recall compared

with 0.18% in the control group (p < 0.038). BIS values

between 40 and 60 have been recommended. Hence, this cohort

study and similarly other studies have shown that the incidence

of awareness decreasing from 0.18% to 0.04% is associated with

the use of BIS monitoring [41,42]. However, in some studies,

there was no difference in the incidence of awareness and

volatile anesthetic consumption between BIS-monitored and

routine care patients [43]. In addition, the BIS has been applied

to pediatric anesthesia [44,45], the detection of brain death [46],

the prediction of neurological prognosis after trauma and

cerebral ischemia [47], and as a monitor of the sedation level in

intensive care units [48].

The BIS is used as a DOA monitor during propofol

anesthesia, but it has some limitations for sevolfurane and

enflurane anesthesia. During ketamine, dexmedetomidine, N2O,

and xenon anesthesia, the BIS does not perform well [49].

3.3.2 Entropy

Approximate entropy, which measures the logarithmic

likelihood of patterns, has been used to analyze physiological

signals and EEG for short time series [50-53]. Although there

are multiple entropy algorithms, only spectral entropy has been

used in a commercially available device, the GE Healthcare

Entropy Module (formerly Datex-Ohmeda M-Entropy), which

was commercially released in 2003. The algorithm uses the

time-domain and frequency-domain data from Fourier analysis

and then applies the Shannon function to the frequency data.

Spectral entropy, termed time-frequency balanced spectral

entropy, depends on the sampling frequency and windowing

[54], but is independent of the frequency components of the

signal [55]. Both approximate and spectral entropy decrease

during general anesthesia [54].

Facial EMG has a frequency range that overlaps with

traditional EEG (0.8-32 Hz), thereby confounding the analysis

of cortical activity. Facial EMG changes with the level of

consciousness and the use of neuromuscular-blocking drugs.

When original EEG signals are analyzed, the response entropy

(RE) and state entropy (SE) indices are obtained. SE is

computed from the EEG across the 0.8 to 32 Hz range and

should encompass mainly the hypnotic elements of the EEG,

whereas RE is computed from 0.8 to 47 Hz, which includes a

significant amount of facial EMG [56]. The displayed SE range

is 0 (isoelectric EEG) to 91 (fully awake), and the RE range is

0 to 100. The anesthetic range is 40 to 60. The manufacturer

recommends that SE values outside this range may require a

change in hyponotic dosing. If the SE value is in this range but

the RE is more than 10 above the SE, more analgesic may be

required.

3.3.3 Evoked potentials

Sensory, including visual, somatosensory, and auditory,

stimulation produces an evoked response between the sensory


receptor and neural generator within the functional integrity

pathway in the CNS. The evoked response can be separated

from the underlying, spontaneous EEG by signal-averaging

techniques. Because auditory evoked responses are sensitive to

anesthetic drugs and their signal represents the conduction of

the peripheral nerve to the brain stem, which is the major

control system of the ANS, AEP has been used as a measure of

the anesthetic effect and DOA [57].

Many investigations associated with evoked potentials

have focused on mid-latency auditory evoked potentials

(MLAEPs) [57-59]. Hypnotic anesthetic drugs increase the

latencies and decrease the amplitudes of waves Pa and Nb. In

contrast, opioids produce minimal changes in MLAEPs [60] in a

clinically effective concentration. There are some limitations of

MLAEPs, such as complex setup, need for intact hearing, and

considerable time needed to produce a response. Therefore, the

AEP index, based on a proprietary algorithm, was introduced

[61,62]. The AEP index simplifies the interpretation of MLAEP

waveforms, but it still requires an adaptive technique for

extracting the MLAEP from the original EEG signal. The

process involves an autoregressive method and an exogenous

input (ARX) to allow the extraction of the AEP signal within

15-25 sweeps of 110-ms duration [63]. This concept has been

incorporated into a commercial device that calculates the A-Line

ARX Index (AAI), which was derived from the fast-extracted

MLAEP waveform analysis and ranges from 0 (deep hypnotic

effect) to 100 (awake). The recommended surgical anesthesia

range is 15-25. In clinical studies, the A-Line performed

comparably to the BIS and other EEG-based monitors but did

not demonstrate advantages that would outweigh the nuisance of

ear plugs and continuous high-frequency clicking sounds in

patients’ ears [64-66].

3.3.4 Clinical interpretation and effects of drug effects in EEG

Studies of all currently available devices show a good

relationship with the Modified Observer’s Assessment of

Alertness/Sedation (MOAA/S) scale for commonly used

hypnotics such as midazolam [67], propofol [66,68-72],

isoflurane, sevoflurane [64,73], and desflurane. The relationship

between sedation and the derived electrophysiologic index does

not seem to be influenced by age in adults [74]. Although the

average index tracks the sedation scale well at the steady state,

there is huge inter-individual variation, particularly during

non-steady-state conditions such as induction and recovery [75].

Thus, inter-subject variability in the EEG requires the

assessment of the awake values before titration of a hypnotic to

a given target EEG index.

The dissociative anesthetic ketamine, an NMDA antagonist,

produces excitatory effects in the EEG. Ketamine doses that

create unresponsiveness do not change the BIS [76]. When

ketamine is used with propofol, an additive anesthetic

interaction occurs [77] that is not reflected in the BIS [21]. Thus,

the addition of ketamine to an anesthetic complicates the

interpretation of the processed EEG.

Nitrous oxide (N2O), an NMDA antagonist, has a

sympatholytic effect. Most studies suggest that nitrous oxide

given in concentrations of up to 70% has minimal effect on the

BIS [78,79] and have found that the effect of nitrous oxide

appears to depend on the monitor used.

3.4 Neuromuscular monitoring

Neuromuscular function is monitored by evaluating the

muscular response to supramaximal stimulation of a peripheral

motor nerve [80,81]. After the administration of a

neuromuscular-blocking drug, the decrease of muscle response

reflects the degree of fiber blockade [82,83]. There are several

patterns of nerve stimulation. The most commonly used patterns

of electrical nerve stimulation are single-twitch, train of four

(TOF), titanic count, post-tetanic count (PTC), and double-burst

stimulation (DBS). Moreover, it has been possible to measure

movement (i.e., degree of muscle relaxation) since the

commercial product the Datex EMG machine (Datex NMT-221

monitor, Datex Instrumentarium, Helsinki, Finland) launched in

the market in 1980. Therefore, it is easy to monitor

neuromuscular function in general anesthesia.

3.5 Measurements of analgesia during general anesthesia

Pain is a subjective conscious experience derived from

internal or external stimuli. During general anesthesia,

consciousness vanishes and the experience of pain disappears.

The term nociception is used to describe the consequences of

surgical stimulus on system function. Several indicators,

including heart rate variability (HRV) [32], changes of the

photoplethysmographic pulse wave amplitude [84,85], facial

muscle activity [86], changes in skin conductivity [87],

pupillometry [88], ocular microtremors [89], and EEG-based

monitoring [86], have been proposed to assess the degree of

nociception.

Photoplethysmography (PPG) gives the concentration of

oxyhemoglobin in the blood and its waveform can be used to

estimate volume changes. Changes in PPG amplitude (PPGA)

correlate with perfusion and reflect the relation of the left

ventricular volume and the peripheral vascular bed [90,91].

Under general anesthesia, increased PPGA response [92] and

skin vasomotor response [93] have been related to nociception.

However, several factors may easily cause noise in PPGA

measurements, such as the movement of the detector,

physiological factors, and pharmacological factors.

Huiku et al. [94] proposed the SSI, which is a simple

numerical measure of the surgical stress level under anesthesia.

Two continuous variables, the interval between successive

hearts beats (HBI) and PPGA, are used to calculate SSI: 100-

(0.7 PPGAnorm + 0.3 HBInorm), where PPGAnorm is the

normalized PPGA and HBInorm is the normalized HBI. The

SSI value indicates the balance between the intensity of

surgical stimulus and the level of antinociception. An SSI value

near 100 corresponds to a high stress level, and that near zero

corresponds to a low stress level.

4. Intelligent modeling and control in anesthesia

In general anesthesia, the clinical anesthesiologists’ major

challenge is to maintain the patient anesthesia via drug-induced

unconsciousness, muscle relaxation, and analgesia (i.e., pain

relief). The former two take place in the operating room,


whereas the latter is mainly related to postoperative conditions.

In recent years, automated drug infusion has been implemented

with feedback strategies to control the patient’s anesthesia.

However, measurement of anesthesia is the difficult part in

designing such systems. Feedback control can be used to keep

the patient in a desired anesthesia state with the advantages of

reduced induction time and minimum amount of drugs

delivered. Feedback control has been used for BP regulation,

inhalation drug concentration control, muscle relaxation control,

and ventilation control [95]. The first feedback controller used

in anesthesia was designed to control the delivery of

intravenous (IV) drug [96]. Feedback control has become

increasingly used in clinical medicine. Most investigators have

used feedback control with a classical proportional, integral,

and derivative (PID) control scheme. However, the human

body is a highly nonlinear system. The human body during

anesthesia might experience different situations, such as neural

muscular blockade, BP changes, and HRV. Therefore it is quite

difficult to use a traditional PID controller for monitoring

anesthesia. Neural network and fuzzy logic controllers are

suitable for controlling nonlinear systems [6,96,97]. Adaptive

intelligent control can be used to achieve balanced anesthesia.

The modeling and control of muscle relaxation, DOA, and

analgesia are reviewed below.

4.1 Modeling and control of muscle relaxation

4.1.1 Fuzzy logic

Anesthesiologists are familiar with administering bolus

injections of non-depolarizing muscle relaxants for controlling

the muscle relaxation at more or less regular intervals [98,99].

If the muscle relaxation is inadequate, anesthesiologist

promptly administer a bolus injection. This control method is

undesirable for eye or brain surgery because the slightest

movement could be dangerous. The patient is always given

more muscle relaxant than required, which may prolong the

effect. In order to obtain the optimal drug delivery rates, on-line

drug administration strategies should be developed [100].

Numerous computer control strategies for muscle relaxants

have been reported [101-106]. A major problem in designing

adequate feedback controllers for physiological systems is the

variation in the response to external stimuli among individuals.

Adaptive control schemes are considered suitable candidates to

solve this clinical problem [107].

A precise model of biological systems may not exist or it

may be too difficult to obtain. Knowledge-based systems

(KBSs) and intelligent computing systems are used for

modeling and control in medical diagnosis and treatment. A

KBS uses model-based reasoning (MBR), rule-based reasoning

(RBR), or case-based reasoning (CBR). Intelligent computing

methods (ICMs) include fuzzy logic (FL), genetic algorithms

(GAs), and artificial neural networks (ANNs). Pandey et al.

conducted a survey of knowledge-based and intelligent

computing systems applied to medicine, reviewing techniques

such as KBS, ANN, FL, and GAs [108]. Linkens et al.

implemented self-organizing fuzzy logic control (SOFLC) for

automated drug delivery during muscle relaxant anesthesia

[109]. The self-organization of the rule base has proven to

provide a robust controller for conditions such as model

uncertainty, noise contamination, and parameter changes [109].

For example, traditional type-1 fuzzy-based systems can handle

slight uncertainties within a short term, such as the imprecision

and slight noise associated with the inputs and outputs of fuzzy

logic controllers (FLCs). However, the effectiveness of type-

1-based agents deteriorates due to long-term uncertainties, such

as those related to heart transplants. Type-2 fuzzy sets can be

used to represent the inputs and outputs of the FLC to handle

short- and long-term uncertainties [110,111]. FLCs account for

the uncertainty in measured signals and can match the

significantly nonlinear and variable processes commonly

encountered in biomedical applications [100,112,113].

4.1.2 Neural networks

Previous studies have used the pharmacokinetic/

pharmacodynamic (PK/PD) model for determining the infusion

scheme (e.g., for mivacurium [114], atracurium [115], and

pancuronium [116]). The main drawback of this approach is the

need to find proper values for the model parameters. Lendl et al.

applied ANNs to adjust specific parameters without the

knowledge of the inner pharmacodynamic processes [117].

Good control systems can inexpensively be designed using

ANNs [118,119], which can easily be configured to

approximate the desired input/output mapping when pairs of

input and output values (samples) are available. Lendl et al.

[117] developed a novel control system which was tested

clinically on 35 patients. The system utilizes the EMG module

within the Datex AS/3 monitor for monitoring the muscle

blockade and a Graseby 3500 infusion pump for IV

administration of mivacurium. The monitor measures the ratio

of the first twitch intensity to the baseline; it decreases with

increasing neuromuscular blockade. The performance of this

feedback system compares favorably with that of closed-loop

controllers [109]. Mendonca et al. developed an automatic

system (Hipocrates) that controls the neuromuscular blockade

of anesthesia by the continuous infusion of non-depolarizing

muscle relaxants [120]. Hipocrates is based on adaptive, robust,

and classical control strategies as well as a wide range of noise

elimination techniques.

4.1.3 Hybrid and other systems

The conventional method of muscle-relaxant

administration is for the anesthesiologist to administer a bolus

dose, the size of which is determined from experience.

Supplements are given when necessary. Linkens et al. proposed

a system that automatically controls the level of muscle relaxant

in anaesthetized patients undergoing surgery [121]. A linearized

model of the patient was identified based on the

pharmacokinetic and pharmacodynamic parameters. The model

was utilized to develop a Smith predictor feedback controller.

Chuang et al. [122] developed a non-depolarizing

neuromuscular block controller which has two rule bases to

control the administration of cisatracurium. The first rule base is

developed using a fuzzy modeling algorithm (FMA), while the

second is developed based on experts’ clinical experience. The

performance of the two rule bases was tested using disturbances

such as set-point change, time delay change, noise


contamination, as well as sampling time changes. Results show

that the FMA-based rule-base controller had better performance.

4.2 Modeling and control of depth of anesthesia

4.2.1 Fuzzy logic

Many techniques have been utilized for monitoring DOA

based on clinical measurements such as EEG signals, BP,

plasma concentration of propofol, and AEP. Other non-

numerical clinical signs, such as lacrimation, pupil response,

and sweating, are also used for determining DOA. Linkens et al.

used a hierarchical structure based on fuzzy modeling to

monitor DOA using many clinical signs in the operating theatre

[123]. The first level uses numerical clinical signs, such as HR

and systolic arterial pressure (SAP), via a self-organizing

learning algorithm or a rule base derived from anesthesiologists’

experience to interpret primary depth of anesthesia (PDOA).

The second level uses non-numerical clinical signs, such as

lacrimation, pupil response, and sweating, which are combined

with the first level of PDOA to decide DOA. Linkens et al. also

proposed a self-organizing fuzzy model (SOFM) algorithm for

simulating anesthetic procedures during operation from the

induction to maintenance stages.

Schaublin et al. studied the fuzzy logic control of

mechanical ventilation during anesthesia [124]. This control

system automatically adjusts ventilatory frequency and tidal

volume to achieve and maintain the end-tidal carbon dioxide

fraction at a desired level (set-point). The system can achieve

and maintain a desired end-tidal carbon dioxide fraction during

routine anesthesia by controlling the mechanical ventilation

reliably and safely. Elkfafi et al. developed an intelligent signal

processing method for evoked potentials for anesthesia

monitoring and control [125]. The system was validated via 21

clinical trials and was found to reliably assess DOA during

anesthesia using propofol. Elkfafi et al. used a SOFM to

substitute an early auto-regressive technique with an exogenous

input model to maintain the drug propofol for anesthesia using

off-line analysis [126]. The results were sufficiently

encouraging to perform on-line clinical trials using fuzzy-logic-

based control and monitoring in an operating theatre.

Shieh et al. developed SOFLC and a hierarchical rule base

for DOA [127], a hierarchical system of on-line advisory for

controlling and monitoring DOA using self-organizing fuzzy

logic [128], and genetic fuzzy modeling and control of the BIS

for general intravenous anesthesia [129]. The first study proved

the suitability of the SOFLC system and hierarchical rule base

in clinical trials as an intelligent adviser for DOA management.

After extensive validation in the second study, the system was

implemented in on-line mode and was found to reduce the

recovery time. In the third study, a genetic fuzzy logic

controller (GFLC) and a genetic proportional integral

derivative controller (GPIDC) were simulated and compared

using a patient model based on the BIS value as a controlled

variable. The results indicated that both GFLC and GPIDC

control the BIS target better than manual control with similar

drug consumption.

Simanski et al. studied automatic drug delivery in

anesthesia [130]. A survey was conducted on the methods of

modeling, measurement, and general progress of closed-loop

control systems in anesthesia. The Rostock assistant system for

anesthesia control (RAN) was discussed, which provides

multiple-input/multiple-output (MIMO) control of the depth of

hypnosis. The effect of the system is similar to that of

neuromuscular blockade since the system controls arterial

hypotension. The results of 22 patients were presented.

Chou et al. developed a multivariable fuzzy logic/self-

organizing method for anesthesia control [131]. In operating

theatres, anesthesiologists usually adopt a specific regime to

administer anesthetic drugs during the different stages of the

operation. Therefore, an automatic closed-loop control system

cannot be realized using a fixed control system. A multi-stage

controller that can change from fixed to adaptive regimes is an

attractive solution. Studies have applied SOFLC to biomedical

systems for muscle relaxation [109,132,133], DOA [127,128],

and patient analgesia control [134]. Previous studies [135] have

simulated controlling anesthesia in operating theaters using a

multivariable SOFLC structure. The simulation results are

sufficiently encouraging for performing clinical trials.

Abdulla and Wen developed robust internal model control

for DOA [136]. The system includes a robust internal model

controller (RIMC) based on the BIS. Patient dose-response

models were developed to provide an adequate drug

administration regimen to avoid under- or over-dosing patients.

Simulation results showed that the RIMC outperformed the

traditional PID controller. The controller robustness was tested

by varying the patient model parameters.


No anesthesia monitoring system is considered to be

uniformly applicable to anesthesia. Robert et al. [137] surveyed

studies related to the monitoring of anesthesia using ANNs. It

was found that neural networks have been successfully utilized

to assess DOA indicators such as those based on hemodynamics,

pupillary reflex, anesthetic concentration, spontaneous EEG, and

AEPs.

Ortolani et al. [138] used ANNs to obtain a non-

proprietary index of the DOA from processed EEG data. Two

hundred patients who had undergone general abdominal

surgery were recruited for the trial. The results indicated that

the ANN was successfully trained to predict an anesthesia

depth index, ranging from 0 to 100, which correlates very well

with the BIS during total IV anesthesia with propofol.

Shieh et al. [139] used ANNs to simulate an entire surgical

operation during inhalational anesthesia. The patient model

includes four inputs (the patient’s age, weight, gender, and

anesthetic agent concentration) and four outputs (HR, SAP,

end-tidal anesthetic agents (ETAA), and EEG signals (i.e.,

BIS)). The performance of the patient model was based on the

minimum cost function and pharmacological reasoning. The

results showed that the approach can provide a more robust

model despite the considerable individual variation in

inhalational anesthesia among patients.


Allen and Smith [140] studied the neuro-fuzzy closed-loop

control of DOA. AEP measurements were utilized as a


feedback signal for the automatic closed-loop control of

general anesthesia using fuzzy logic and neural networks. AEP

is a signal derived from the EEG in response to auditory

stimulation, which may be useful as an index of the DOA. A

satisfactory input to a fuzzy logic infusion controller for the

administration of anesthetic drugs can be provided through a

simple back-propagation neural network for learning the AEP

signal index.

Single-input/single-output (SISO) pharmacokinetic-

pharmacodynamic compartment models are the standard

modeling paradigm used to describe the relationship between

input anesthetic agents and output patient endpoint variables.

Lin et al. [141] used hybrid multivariable models for predicting

human response to anesthesia. Model classes such as standard

linear time invariant (LTI) multivariable models, hybrid

multivariable models, nonlinear SISO PK-PD models, and

uncertain systems models have been introduced [141]. In

addition, hybrid multivariable models have been used to

describe the relations between inputs that include anesthesia,

disturbances, and surgical stimuli to a variety of patient output

variables. The focus of Lin et al.’s [141] work was a

comparison of hybrid multivariable models constructed using

subspace identification techniques to the more commonly used

SISO PK-PD models. More specifically, the hybrid models

were considered as switching system models, where the

underlying systems are MlMO linear state-space models over

which the patients’ responses switch based on their sedative

state.

Nunes et al. [142] used neural-fuzzy paradigms for

modeling and multivariable control in anesthesia. The

modeling of patient’s vital signs and the classification of DOA

were discussed. First, a hybrid patient model using Takagi-

Sugeno-Kang fuzzy models was developed. Second, a fuzzy

relational classifier was developed to classify a set of wavelet-

extracted features from the AEP into different levels of DOA. A

comprehensive patient model that predicts the effects of the

drugs propofol and remifentanil on DOA while monitoring

several vital signs was obtained. The patient’s model is the

basis for optimal multivariable control for administering these

two drugs simultaneously.

Mahfouf et al. [143] explored the dichotomy associated

with monitoring and control of DOA. The paper starts with a

rather conservative (simplistic) method to closed-loop control

of DOA via the monitoring of arterial BP using the clinical gold

standard (CGS) method followed by feedback control

techniques. The merits/limitations of each technique were

identified and a list of feasible techniques for future research in

monitoring and control of DOA was given.

Tan et al. [144] developed decision-oriented multi-

outcome modeling for anesthesia patients. The work addressed

the problem of real-time modeling for monitoring, predicting,

and diagnosing multiple outcomes of anesthesia patients. They

showed that the consideration of multiple outcomes is

necessary and beneficial for anesthesia management. It was

observed that each patient has very different dynamic responses

to drugs. Even for a given patient, responses to a given drug

change with time, patient conditions, and surgical conditions.

As a result, it is necessary to establish real-time MIMO models

for individual patients. The SISO model structure contains only

a few parameters and their values can be easily estimated in

real-time. The MIMO model is simplified significantly by

being split into multiple SISO models.

Dua et al. [145] studied modeling and multi-parametric

control for the delivery of anesthetic agents. They proposed

model predictive controllers (MPCs) and multi-parametric

model-based controllers for the delivery of anesthetic agents.

An MPC considers the patient’s state and the drug delivery rate

for solving the optimal control signal, which is performed at

regular time periods. The controller features simultaneous

control of cardiac output, mean arterial pressure (MAP), and

unconsciousness.

4.3 Modeling and control of analgesia

4.3.1 Fuzzy logic

Conventional patient-controlled analgesia (PCA) can

reduce drug consumption and improve pain control. Shieh et al.

studied a pain model and fuzzy logic patient-controlled

analgesia in extracorporeal shock-wave lithotripsy (ESWL)

[146]. A PCA based on a fuzzy logic system was developed for

controlling alfentanil administration, where the infusion rate is

adjusted using a look-up table. The look-up table is based on a

history of the drug demand. The results show that a

combination of PCA and fuzzy logic control outperforms

conventional PCA control.

Further work on pain intensity was published by Shieh et

al. [134,147], who used ESWL. A hierarchical controller whose

basic level controls analgesia and whose second level is an

adaptive self-learning level that improves the lower-level

control rules was proposed. Results show that most patients

experience a moderate level of pain when using the system

[134,147]. It was found that pain relief using the hierarchical

system is better than that using a conventional method. The

system was further enhanced by incorporating a fuzzy logic

model of the pain index that is based on the interpretation of

the self-titration of the drug delivery. Such system can be used

online to log the patient’s BP, temperature, pulse rate, and

respiration rate (RR).

Schubert et al. [148] developed a fuzzy system for the

regulation of the analgesic remifentanil during general

anesthesia based on the expert knowledge of anesthesiologists.

The anesthesiologists monitor vital parameters such as HR and

arterial BP to apply the analgesic drug. The HRV was

considered as an additional input to the fuzzy system. The final

controller structure is based on two controllers. The first

controller is used to regulate the remifentanil administration

using the HR and MAP as feedback signals. When the HR goes

higher than 90 bpm, the second controller administers a bolus

of remifenanil based on the measured HR. Both fuzzy

controllers are Mamdani inference systems.


Melzack discussed the concept of pain [149]. He found

that pain is not directly produced by inflammation, injury, or

diseases, but instead is combined and outputted from a widely


distributed neural network of the brain. Pain has been proposed

as the fifth vital sign to be entered into a patient’s chart along

with temperature, BP, HR, and RR. However, there is still no

objective measurement for the experience of pain. Hence, an

objective and reliable interpretation of the pain pattern is

important for pain measurement. Shieh et al. [150] proposed an

ensembled artificial neural network (EANN) with 10 inputs and

one output for the prediction of a visual analog scale (VAS) of

pain intensity. The system was tested on 323 patients receiving

intravenous PCA after Caesarean section. The prediction

performance of the EANN model was satisfactory based on the

root mean square error (RMSE) of training (1.6681) and testing

(1.9900) against the linear regression (LR) model of training

(1.9406) and testing (2.1617) for a VAS in the range of 0 to 10.

The results suggest that the EANN model can provide a useful

reference for clinical practice in acute pain service.


Jacobs et al. [151] investigated modeling and estimation for

patient-controlled analgesia of chronic pain. They derived a

mathematical model of PCA for the real-time prediction of the

continuous infusion of an analgesic drug (morphine-6β-

glucuronide). Webb et al. [152] investigated the use of a

computerized cold pressure test to measure analgesia. Yeh et al.

[153] investigated the pain relief demand index for

postoperative pain. Although they used a complicated bio-signal

processing algorithm (i.e., empirical mode decomposition) to

decompose the pain relief pattern of patients, the obtained

results were not close to those of a traditional clinical evaluation

of pain score (i.e., VAS).

5. Discussion

The main job of anesthesiologists is to maintain the

adequacy of anesthesia. Muscle relaxation, unconsciousness,

and analgesia characterize modern balanced anesthesia. The

responses of clinical signs to inadequate general anesthesia can

be divided into somatic and autonomic responses, as shown in

Table 1. Somatic responses can be further divided into sensory

motor responses (withdrawal and movement), and sensory

consciousness and nociception (awareness and pain). Although

movement (i.e., degree of muscle relaxation) can be measured,

the evaluation indices of awareness (e.g., BIS, AEP, entropy)

cannot reliably predict awareness during general anesthesia.

Several EEG-based monitors have been associated with a

decreased incidence of consciousness [40,154], reduced

administration of DOA drugs [41,42], and fast recovery from

anesthesia [155,156]. A study found that patients can become

aware even when BIS values are within the target range (i.e., 40

to 60), and thus concluded that the BIS monitoring should not

be used as part of standard practice of anesthesia [43]. Another

study has shown that the Narcotrend DOA monitor cannot

reliably predict awareness during general anesthesia via the

isolated forearm technique [157]. Hence, monitoring awareness

is still a challenging area. The assessment of pain (i.e.,

analgesia) is one of the main components of balanced general

anesthesia. However, pain is a very subjective experience

evoked by internal or external stimuli and it is very difficult to

measure via vital signs [158,159]. Currently, no commercial

monitoring method is available for evaluating analgesia during

general anesthesia.

Autonomic responses, which are more complicated, can be

classified into four types depending on the system: breathing,

hemodynamic, sudomotor, and hormonal. For the breathing

system, the breathing pattern can be monitored by RR. For the

hemodynamic system, the tachycardia, blood volume change,

and hypertension can be monitored by ECG, SpO2, and BP.

ECG and BP have been used to analyze the HRV and blood

pressure variability (BPV) in terms of sympathetic and

parasympathetic nerves using the fast Fourier transform (FFT)

to calculate the low/high frequency power ratio (LHR), which

is strongly related to the ANS. Several studies [160-164] have

used nonlinear algorithms (e.g., detrended fluctuation algorithm

(DFA) based on the α index and multi-scale entropy (MSE)

based on the complex index (CI)) to analyze RR intervals of

ECG signals in terms of fractal characteristics and the

complexity index of subjects. Although these studies focused

on cardiovascular diseases (e.g., congestive heart failure (CHF)

and arterial fibrillation (AF)), the DFA and MSE algorithms

can be applied to general anesthesia. Studies [165,166] have

calculated the cardiopulmonary coupling (CPC) via the product

of the coherence and cross-power of the ECG interval and RR

signals that are applied for the measurement of sleep quality.

Moreover, Huiku et al. [94] developed the SSI, which may be

applied to the measurement of analgesia. SSI has been

clinically validated [167,168], and will become commercially

available in the near future.

The third type, namely sudomotor innervations, may be

related to the cholinergic innervations of the ANS (i.e.,

sympathetic nerve) prominent in sweat glands, which causes

perspiration via the activation of muscarinic acetylcholine

receptors. Linkens et al. and Shieh et al. [123,127,128]

investigated the second level of the DOA via fuzzy logic to

combine the evaluation of sweat, lacrimation, and pupil

responses to anesthetic light. They combined this with PDOA,

which uses the HR and BP, to determine a confidence of the

depth of anesthesia. The final type, namely hormonal responses

(i.e., catecholamines, corticosteroids) are affected by the DOA

[169,170]. Hormonal responses can be accurately detected by

analyzing a blood sample via high-performance liquid

chromatography; however, they are not strongly related to the

DOA. In addition, hormonal measurements are expensive,

invasive, and time-consuming; therefore, the measurements

cannot be made in real-time and are not considered a major

parameter for determining adequate general anesthesia. Table 1

summarizes the use of clinical signs for adequate general

anesthesia. Multiple parameters should be considered for

deciding adequate general anesthesia.

Because it is difficult to interpret adequate general

anesthesia and the physiological signals and responses are

time-variant, the precise modeling and control of general


Table 2. Intelligent modeling and control of general anesthesia studies classified based on types of anesthesia parameter (i.e., muscle relaxation, depth

of anesthesia, and analgesia).

Types of anesthesia parameter

Related studies ([Reference]/Year)

Muscle relaxation

[6]/2001, [98]/1991, [99]/2002, [100]/1994, [101]/1980,

[102]/1987, [103]/1987, [104]/1988, [105]/1991, [106]/1991, [107]/1990, [108]/2009, [109]/1991, [114]/1993, [115]/1991,

[117]/1999, [120]/2004, [121]/1982, [122]/2009, [133]/1997

Depth of anesthesia

[95]/1992, [96]/1992, [97]/1997, [123]/1996, [124]/1996,

[125]/1997, [126]/1998, [127]/1999, [128]/2005, [129]/2006, [130]/2009, [131]/2010, [136]/2011, [137]/2002, [138]/2002,

[139]/2002, [140]/2001, [141]/2002, [142]/2005, [143]/2009,

[144]/2010, [145]/2010, [173]/2010

Analgesia [134]/2007, [146]/2002, [147]/2007, [148]/2008, [149]/1999, [150]/2010, [151]/1995, [152]/2001, [153]/2011

Table 3. Intelligent modeling and control of general anesthesia studies classified by type of intelligent algorithm.

Intelligent

algorithm type Related studies ([Reference]/Year)

Fuzzy logic

[6]/2001, [96]/1992, [99]/2002, [100]/1994, [107]/1990, [108]/2009, [109]/1991, [110]/2007, [111]/2010, [113]/1997,

[123]/1996, [124]/1996, [125]/1997, [126]/1998, [127]/1999,

[128]/2005, [129]/2006, [130]/2009, [131]/2010, [133]/1997, [134]/2007, [136]/2011, [146]/2002, [147]/2007, [148]/2008

Neural network [117]/1999, [118]/1993, [119]/1995, [120]/2004, [137]/2002, [138]/2002, [139]/2002, [149]/1999, [150]/2010

Hybrid and others

[97]/1997, [121]/1982, [122]/2009, [140]/2001, [141]/2002,

[142]/2005, [143]/2009, [144]/2010, [145]/2010, [151]/1995, [152]/2001, [153]/2011, [173]/2010

anesthesia is challenging. Quantitative approaches were initially

used but they have been found to be too complicated for

interpreting general anesthesia. Qualitative techniques,

particularly artificial intelligent approaches, have thus become

commonly used. Hybrid models (quantitative and qualitative

approaches) and hybrid intelligent algorithms (fuzzy logic,

neural networks, and genetic algorithms) have been applied to

general anesthesia. The intelligent modeling and control of

general anesthesia has been applied to different types of

anesthesia parameter (i.e., muscle relaxation, DOA, and

analgesia), as shown in Table 2. Most studies have focused on

how to interpret the DOA using expert systems, intelligent

systems (fuzzy logic and neural networks), or hybrid models

(genetic-fuzzy and neural-fuzzy paradigms). Muscle relaxation

and DOA can be measured directly and fed back for regulation

purposes, whereas analgesia cannot be directly measured.

Because of the difficulty of measuring pain, studies have

evaluated pain using PCA for ESWL and post-operation.

Patients in an ESWL operation are conscious, so they can feel

their pain intensity. The patients push a button when they require

pain relief. Similarly, patients start to feel pain after an operation.

It is thus convenient to use PCA for post-operation patients.

Table 3 lists research work related to anesthesia published

in the last two decades classified based on the type of intelligent

system algorithm. Most studies used fuzzy logic because it can

be applied to anesthesiologists’ decision-making. However,

complicated and high-dimensional MIMO systems are not easy

to model using fuzzy logic. The imprecision and instability of

fuzzy logic are disadvantages for general anesthesia. Hence,

ANNs have become used to solve imprecise problems if

learning data are sufficient. However, ANNs use hidden layers,

which hide a lot of information. Neuro-fuzzy modeling

[171-173] is an alternate form of artificial intelligence that

combines the transparency of fuzzy logic and the learning

ability of ANNs, which allow the understanding, validation, and

interpretation of the model produced by the rule base. Hybrid

approaches have received increasing attention because many

parameters are difficult to determine during the modeling or

control of general anesthesia. Future development should

consider neuro-fuzzy, genetic-fuzzy, genetic-neuro, and genetic-

neuro-fuzzy approaches for the modeling or control of general

anesthesia.

6. Conclusion and future developments

This paper provided a detailed review of the clinical and

engineering aspects of how to measure, interpret, model, and

control general anesthesia. Determining adequate general

anesthesia is very difficult due to the complicated mechanisms

of anesthesia, namely unconsciousness, amnesia, analgesia, and

akinesia. High-performance parallel computing and embedded

systems have been applied to biomedical engineering

applications, allowing complicated signal processing algorithms

to be implemented for general anesthesia. EMG, ECG, BP, EEG,

and SpO2 are good candidates for representing general

anesthesia. Therefore, a multistage hierarchical system for the

modeling and control of general anesthesia should be developed.


Acknowledgements

This research was financially supported by the National

Science Council (NSC) of Taiwan (NSC 99-2221-E-155-

046-MY3) and the Center for Dynamical Biomarkers and

Translational Medicine, which is sponsored by the NSC (NSC

100-2911-I-008-001).

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