Monitoring Depth of Hypnosis under Propofol General Anaesthesia

Granger Causality and Hidden Markov Models

Nicoletta Nicolaou

1,2,3

and Julius Georgiou

1,2,3

Holistic Electronics Research Lab, University of Cyprus, Nicosia, Cyprus

Dept. of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

KIOS Research Centre, University of Cyprus, Nicosia, Cyprus

Keywords:

Brain-Computer Interface, EEG, Anaesthesia, Awareness.

Abstract:

Intra-operative awareness is experienced when a patient regains consciousness during surgery. This work

presents a Brain-Computer Interface system that can be used as part of routine surgery for monitoring the

patient state of hypnosis in order to prevent intra-operative awareness. The underlying state of hypnosis is

estimated using causality-based features extracted from the spontaneous electrical brain activity (EEG) of the

patient and a probabilistic classiﬁcation framework (Hidden Markov Models). The proposed method is applied

to EEG activity from 20 patients under propofol anaesthesia. The mean discrimination performance obtained

was 98% and 85% for wakefulness and anaesthesia respectively, with an overall performance accuracy of 92%.

The use of a probabilistic framework increases the anaesthetist’s conﬁdence on the estimated state of hypnosis

based on the marginal probabilities of the underlying state.

1 INTRODUCTION

Intra-operative awareness occurs in approximately

0.1-0.8 % of surgical patients (Bruhn et al., 2006).

The real incidence of awareness, however, is likely

to be much higher due to the amnesic effect of cer-

tain anaesthetics resulting in some patients having

no recollection of regaining awareness. The impor-

tance of monitoring depth of anaesthesia in order to

prevent intra-operative awareness is apparent consid-

ering that, given the estimated 234.2 million major

surgical procedures undertaken annually worldwide

(Weiser et al., 2008), at least 1,873,600 people are

likely to have experienced intraoperative awareness.

The use of the electrical brain activity (EEG) is

currently the preferred method for monitoring anaes-

thetic depth. Devices that monitor the patient state

of hypnosis during anaesthesia are a form of a Brain-

Computer Interface (BCI) system. Even though the

idea of using the spontaneous patient EEG for mon-

itoring purposes is not new (McEwen et al., 1975),

a relatively small number of devices are currently

commercially available for this purpose. In these de-

vices, the patient’s spontaneous EEG activity is con-

tinuously monitored for changes that signal regaining

of awareness. In incidences of intra-operative aware-

ness the patient cannot communicate this to the anaes-

thetist due to the co-administration of neuromuscular

blockers with the anaesthetic agents. Thus, the patient

is essentially put in a chemically-induced ‘locked-in’

state whereby communication via conventional means

is impaired. Despite the existence of commercial

EEG-based devices for monitoring patient state dur-

ing anaesthesia, they are not considered as part of rou-

tine anaesthetic practice in the majority of hospitals

worldwide. This is mainly attributed to issues relat-

ing to robustness and inter-subject variability (Voss

and Sleigh, 2007). Inter-subject variability in partic-

ular is an issue that cannot be addressed with current

systems, as the systems have a universal state indi-

cator scale (from 0-100; 100: fully conscious, 40-60:

surgical anaesthesia, 0: no activity) that cannot be cal-

ibrated for each individual patient and, thus, does not

take into account patient speciﬁcs.

In this study the anaesthetic-induced EEG changes

are modelled using causality-based features and the

underlying state (wakefulness / anaesthesia) is es-

timated using a probabilistic framework (Hidden

Markov Models). The use of causality features in-

creases the system robustness, as it has been shown

that such features capture general mechanisms of

anaesthetic administration regardless of the particu-

lar anaesthetic protocol (Nicolaou et al., 2012; Barrett

et al., 2012), and with a high discriminative ability

256

Nicolaou N. and Georgiou J..

Monitoring Depth of Hypnosis under Propofol General Anaesthesia - Granger Causality and Hidden Markov Models.

DOI: 10.5220/0004679402560261

In Proceedings of the International Congress on Neurotechnology, Electronics and Informatics (BrainRehab-2013), pages 256-261

ISBN: 978-989-8565-80-8

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

(Nicolaou and Georgiou, 2013). The use of a proba-

bilistic framework in such a system is advantageous,

as the anaesthetist can also assess the reliability of

a state estimation through the marginal state proba-

bilities. Learning an HMM model for each patient

also ensures that the system is calibrated for each pa-

tient and the estimated states are not affected by inter-

subject variability.

2 METHODS

2.1 Dataset

The data is a subset of EEG data collected from pa-

tients during surgery at the Nicosia General Hospi-

tal, Cyprus. In this particular study data from 20

male patients (mean age 41.8 ± 20.6) were analysed.

The study has been approved by the Cyprus National

Bioethics Committee and patients involved gave writ-

ten informed consent for their participation. The ex-

perimental protocol details are described elsewhere

(e.g. see (Nicolaou et al., 2012)). In summary, anaes-

thesia was induced with a propofol bolus and main-

tained with constant intravenous propofol administra-

tion. In most patients this was titrated with an in-

travenous administration of remifentanil hydrochlo-

ride. Lungs were ventilated with an air-oxygen or air-

oxygen-N

O mixture. During surgery boluses of neu-

romuscular blocking agents and other drugs, such as

antibiotics, were administered as required. EEG data

were obtained from 19 electrodes based on the inter-

national 10/20 system, with a sampling rate of 256

Hz. Data recording was performed throughout the en-

tire surgical duration (awake pre-induction, induction,

surgical anaesthesia and recovery of consciousness).

Since the exact point at which loss of consciousness

occurs after patient induction is not known, the point

at which the anaesthetic bolus was administered was

considered instead. Recovery of consciousness was

deﬁned as the point at which the patient responded

(either via voluntary muscular movement or a verbal

response) to verbal commands or tactile stimuli by the

anaesthetist.

2.2 Granger Causality

Granger Causality (GC) is deﬁned by Wiener as fol-

lows: ‘for two simultaneously measured signals, if

one can predict the ﬁrst signal better by incorporat-

ing the past information from the second signal than

using only information from the ﬁrst one, then the

second signal can be called causal to the ﬁrst one’

(Wiener, 1956). Mathematically, causality was de-

ﬁned by Granger through the use of regression: for

two time series, X

, and X

, if X

is inﬂuenced by

, then the addition of past values of X

in the re-

gression of X

will improve its prediction (Granger,

1980). The performance of the prediction can be as-

sessed through the variances of the ﬁtted regression

models. Thus, GC is deﬁned as:

→X

= ln

(1)

where σ

and σ

are the variances of the

regression errors, e

and e

respectively, obtained

from the following (auto)regression models ( j = 1, 2):

(t) =

∑

i=1

(t − i) + e

(t) (2)

(t) =

∑

i=1

(t − i)+

∑

i=1

(t − i)+e

(t)

(3)

The variables a

, a

and b

are the regression

coefﬁcients for models of order P. The main idea is

that if the prediction of X

is improved by using past

values of X

in its prediction, then σ

< σ

and, therefore GC

→X

increases. If, however, the

past of X

does not improve the prediction of X

, then

→X

will be close to zero. Therefore, by deﬁni-

tion, GC

→X

= 0 when there is no causality between

the signals, and GC

→X

> 0 otherwise. Similarly,

we can also deﬁne GC in the opposite direction. A

number of factors must be taken into account when

considering GC-based analysis, such as the choice of

the regression model order, data stationarity and data

ﬁltering (more details can be found in (Bressler and

Seth, 2010)).

2.3 Hidden Markov Models

Hidden Markov Models (HMMs) belong to the family

of Bayesian networks (Rabiner, 1986). They are used

to model systems that are assumed to be a Markov

process with states that are not directly visible to the

observer. The observer can observe only the output of

the system. However, the sequence of outputs gives

some information about the invisible sequence of dis-

crete states, as each state has a probability distribu-

tion associated with it. Transitions between states

are also probabilistically described through the tran-

sition probability matrix. The HMM model makes

two assumptions: (i) the Markov assumption, which

states that the current state is dependent only on the

previous state; and (ii) the independence assumption,

MonitoringDepthofHypnosisunderPropofolGeneralAnaesthesia-GrangerCausalityandHiddenMarkovModels

257

which states that the output observed at a particular

time is independent of past outputs and states and de-

pends only on the current state. An HMM can also

perform learning, whereby the model parameters that

best describe a process can be estimated from a set of

examples from the particular process. Training can be

both supervised or unsupervised. In supervised learn-

ing, the model outputs are equated to the inputs, and

the outputs to the corresponding states. The model pa-

rameters can then be estimated using maximum likeli-

hood estimation. In our particular case, the output ob-

servations correspond to the GC-based features, while

the unobserved underlying states are ‘wakefulness’

and ‘anaesthesia’. The HMM is then used to answer

the question: ”What is the most likely sequence of

wakefulness/anaesthesia states that could have gener-

ated the observed GC values?”.

2.4 Methodology

The ability to separate consciousness and anaesthesia

using the spontaneous changes in the patient’s EEG

activity was investigated. The particular methodology

consists of the following steps:

1. For each patient, the 19 electrodes were split into

the following ﬁve grids: left frontal (LF: elec-

trodes Fp1, F7, F3, T3, C3), right frontal (RF:

Fp2, F8, F4, C4, T4), left posterior (LP: T5, P3,

O1), right posterior (RP: T6, P4, O2), and mid-

line (Z: Fz, Cz, Pz). The average EEG activity

over each of the ﬁve grids was then estimated.

The particular groupings were chosen such that

broad areas corresponding to frontal and poste-

rior activity were obtained, as fronto-posterior

interactions appear to play an important role in

(un)consciousness.

2. For each patient, the continuous averaged EEG

data obtained over each of the ﬁve grids were win-

dowed into 2-s non-overlapping segments.

3. Using the manual markers in the EEG record, the

windows corresponding to wakefulness (class A)

and anaesthesia (class B) were identiﬁed.

4. For each 2-s segment, pairwise fronto-posterior

GC features were estimated, resulting into the fol-

lowing 4-dimensional feature vector:

= [GC

LF→LP

,GC

RF→LP

,GC

LF→RP

,GC

RF→RP

]

where C ∈ {A,B} corresponds to one of the two

classes, and i = 1,...,N

denotes the i

2-s seg-

ment from all the available segments of each class

). The order of the ﬁtted regression models

was set to 6.

5. The ‘Bayes Net Toolbox’ for Matlab is used for

HMM modelling (Murphy, 2001). An HMM

model is trained using 40% of the available data

for each class. The HMM outputs are modelled

as continuous Gaussians. The estimated predic-

tion probabilities are then obtained for all avail-

able data.

The speciﬁc parameters, such as duration of the win-

dows and the order of the ﬁtted regression mod-

els were based on previous investigations (Nicolaou

et al., 2012). The performance of the HMM for each

patient is then estimated via the average speciﬁcity

(4), sensitivity (5) and accuracy (6) estimated over

B = 50 bootstrap repetitions. T

P(T

N) is the num-

ber of true positives (negatives), and T

P(T

N) is the

total number of ‘ground truth’ positive (negative) ex-

amples of each class.

SP =

(4)

SE =

(5)

AC =

∑

b=1

∑

b=1

(6)

3 RESULTS AND DISCUSSION

Figures 1 and 2 show examples of the marginal proba-

bilities for wakefulness and anaesthesia obtained from

one of the 50 bootstrap repetitions and for two ran-

domly chosen patients (S20 and S2 respectively). For

visualisation purposes a moving average ﬁlter (n =

10) was applied to the marginal probabilities. The

probabilistic framework allows the anaesthetist to as-

sociate a likelihood to a particular decision. It can

be seen from the ﬁgures that the marginal probabil-

ities track the transitions between wakefulness and

anaesthesia well: as expected, the marginal probabil-

ity for wakefulness is close to 1 prior to anaesthetic in-

duction and after recovery of consciousness, while it

drops below 0.5 during surgical anaesthesia (and vice

versa for the marginal probability for anaesthesia).

This implies that the Granger Causality-based fea-

tures provide a good representation of the two states

and that the HMM is able to track the successions of

the two states successfully. The presence of artefacts,

such as the use of diathermy, may cause brief distor-

tion in state estimations. This can be seen in ﬁgure 1,

where artefacts caused by diathermy introduce spu-

rious brief changes in the estimated marginal proba-

bilities. In such cases, it is easy for the anaesthetist

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Figure 1: Estimated state probabilities for wakefulness (black) and anaesthesia (maroon) for patient S20. The state with a

probability greater than ‘0.5’ is the classiﬁed state. Large peaks in the marginal probabilities during anaesthesia are caused by

diathermy artefacts, while the peaks after induction are caused by tracheal intubation.

Figure 2: Estimated state probabilities for wakefulness (black) and anaesthesia (maroon) for patient S2. The state with a

probability greater than ‘0.5’ is the classiﬁed state. Large peaks in the marginal probabilities during anaesthesia are caused by

diathermy artefacts.

to assess whether such changes are true or whether

they are indeed spurious, e.g. if diathermy is being

utilised at the particular moment. However, the pres-

ence of artefacts does not necessarily induce spurious

MonitoringDepthofHypnosisunderPropofolGeneralAnaesthesia-GrangerCausalityandHiddenMarkovModels

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state changes, as the marginal probabilities may not

go over/under the 0.5 threshold; this can also be seen

in ﬁgures 1 and 2. Moreover, changes in the marginal

probabilities that are persistent rather than transient

is another cause of alert for the anaesthetist, as such

changes could be another indication that the patient

may be regaining consciousness.

Table 1 shows the mean performance of the pro-

posed method for each patient and overall. Patient-

wise, a mean sensitivity, speciﬁcity and accuracy of

0.98, 0.85 and 0.92 is obtained. This is comparable to

results from other studies, which range from 64-93%

(for more details see (Nicolaou et al., 2012) and ref-

erences within). For 12 patients the overall accuracy

is more than 90%. The lower speciﬁcity (performance

for anaesthesia) compared to sensitivity (performance

for wakefulness) is expected if we take into account

that no artefact removal has been performed, thus

some misclassiﬁcation due to artefacts during surgery

is expected. For 3 patients the mean speciﬁcity is be-

tween 0.63-0.66. This could be mainly attributed to

the small number of training features resulting from

the small number of available features for wakeful-

ness for the particular patients. Thus, this has a neg-

ative effect on the generalisation ability of the HMM

classiﬁer for the anaesthesia state, given that the small

Table 1: Mean performance of GC-based HMM classiﬁca-

tion for 20 patients (S1,...S20). SE: Sensitivity, SP: Speci-

ﬁcity, AC: Accuracy. The maximum (best) classiﬁcation is

indicated by ’1’ (corresponding to 100%).

PATIENT SE SP AC

S1 1.00 0.98 0.99

S2 1.00 0.94 0.97

S3 0.89 0.93 0.91

S4 1.00 0.93 0.97

S5 1.00 0.93 0.97

S6 1.00 0.66 0.83

S7 1.00 0.96 0.98

S8 1.00 0.87 0.94

S9 1.00 0.81 0.91

S10 0.95 0.88 0.92

S11 0.95 0.79 0.87

S12 0.94 0.65 0.80

S13 0.98 0.94 0.96

S14 0.95 0.77 0.86

S15 1.00 0.63 0.82

S16 0.96 0.78 0.87

S17 1.00 0.90 0.95

S18 1.00 0.78 0.89

S19 0.91 0.87 0.89

S20 0.99 0.92 0.96

TOTAL 0.98 0.85 0.92

number of training features cannot be expected to cap-

ture all feature attributes associated with anaesthesia.

An important advantage of the proposed method-

ology is its clinical applicability. Individual patient

variability is taken into account through calibration

of the BCI system for each patient, as opposed to

commercially available systems that employ a univer-

sal 0-100 scale without system calibration to patient

speciﬁcs. This calibration is also possible due to the

ability of HMMs to learn incrementally, thus facili-

tating a more higher-level learning through the addi-

tion of new information. The probabilistic framework

adds credibility by associating a probability likeli-

hood to each decision. This strengthens the anaes-

thetist’s decisions regarding the assessment of the un-

derlying patient state of hypnosis by allowing a given

degree of certainty to their actions.

The proposed methodology has some limitations.

These are mainly related to the particular features

utilised: Granger Causality (GC) has been the recip-

ient of some criticism regarding the effects of sta-

tionarity, volume conduction, ﬁltering and regression

model order to the estimated GC values (Bressler and

Seth, 2010; Florin et al., 2010). However, the na-

ture of causality itself implies that causality-based

measures likely capture more general mechanisms of

anaesthetic action. If these speciﬁc issues are taken

into account causality can, thus, constitute a robust

and reliable feature of anaesthetic-induced changes

in spontaneous brain activity. A more extensive dis-

cussion on GC, general limitations posed by GC and

potential solutions can be found in (Nicolaou et al.,

2012).

In addition, as previously mentioned, no artefact

removal was performed in this study. Prior to anaes-

thetic induction the EEG signals may contain artefacts

originating both from the patient, such as body move-

ment, eye blinks and speech, and external sources,

such as placement of monitoring equipment by the

hospital staff. During induction the EEG is usually

contaminated by artefacts due to tracheal intubation.

During anaesthesia the administered anaesthetics and

muscle relaxants ensure that there is no muscle, eye

or movement activity from the patient. Artefacts

during anaesthesia are mainly caused by the use of

diathermy equipment, but some artefacts due to other

surgical stimuli could also be present. The latter is not

likely as we excluded surgical procedures performed

in close proximity to the EEG sensors, such as ear-

nose-throat surgeries. Despite having performed no

artefact removal, it is not likely that discrimination

between wakefulness and anaesthesia is performed

solely due to the presence of these artefacts. On the

contrary, the performance of the proposed method

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could be improved by removal of artefacts prior to

feature estimation, as the differences between the two

states would be more prominent.

4 CONCLUSIONS

The spontaneous EEG activity of anaesthetised pa-

tients can be used to assess their underlying state of

hypnosis. This type of a BCI system can revolutionise

routine surgery and aid towards avoidance of intra-

operative awareness. The use of causality-based fea-

tures in a probabilistic framework adds to the reliabil-

ity and robustness of such a system. State decisions

are supported by related probabilities, thus strength-

ening the weight of each individual state assessment

by both the system and the anaesthetist. Future work

will investigate whether causality features obtained

from individual electrode pairs can provide similar

discriminatory ability in order to eliminate the need

for a full set of EEG sensors to capture anaesthetic-

induced changes in causality.

ACKNOWLEDGEMENTS

The authors would like to thank Dr. Pandelitsa

Alexandrou and Dr. Saverios Hourris (Depr. of

Anaesthesia, Nicosia General Hospital, Cyprus), the

hospital staff and the anonymous volunteers who par-

ticipated in this study. This work was co-funded by

the Republic of Cyprus and the European Regional

Development Fund through the Cyprus Research Pro-

motion Foundation (DESMI 2008). Grants: ‘New In-

frastructure Project/Strategic/0308/26’ and ‘DIDAK-

TOR/DISEK /0308/20’.

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