Abstract— A method is presented for the automatic determination of a patient’s level of sedation from the EEG. Six bipolar channels of EEG recorded from 12 adult patients sedated with low-dose propofol (2, 6-disopropylphenol) were used to develop a linear discriminant based system for depth of sedation monitoring using a number of quantitative EEG measures. A cross fold validation estimate of the performance of the algorithm as a patient independent system yielded a sensitivity of 74.70% and a specificity of 81.67%. It is hoped that the methodology reported here could lead to fully automated systems for depth of sedation monitoring.

I. INTRODUCTION ONCIOUS sedation allows patients to tolerate unpleasant procedures by alleviating anxiety and

discomfort. Sedation may also expedite the conduct of procedures particularly those that require the patient does not move [1]. With sedation comes risk, in particular those of cardiovascular and respiratory compromise that accompany excess hypnotic drug use [2]. Currently many practitioners rely on intuition and experience to judge depth of sedation. In the research setting sedation scales [such as the Modified Observer’s Assessment of Alertness/Sedation Score (MOAA/S)] [3] are used to quantify depth of sedation. The disadvantage of this method when applied to clinical practice is that it requires continuous stimulation (verbal or tactile) of the patient. Paradoxically this may increase the amount of hypnotic drug used. Thus the need exists for a reliable, passive depth-of-sedation monitor.

The electroencephalographic (EEG) is the obvious starting point. The changes that occur in the EEG during pharmacologic or physiologic drowsiness, when viewed by an experienced clinician are unequivocal and objective [4, 5]. These changes include alpha wave drop out in the posterior brain regions and increased frontal beta activity during propofol sedation (Table 1) [6, 7]. A number of commercially available devices including BIS™, Entropy™,

Manuscript received April 2nd 2007. This work was supported in part by

Science Foundation Ireland (SFI/05/PICA/1836). B.R. Greene is with the Department of Electrical & Electronic

Engineering, University College Cork, Ireland (phone +353-21-490-3793; e-mail: barryg@rennes.ucc.ie).

P. Mahon is with the department of Anaesthesia and Intensive Care Medicine, Cork University Hospital, Cork, Ireland. (e-mail: rsimahon@hotmail.com).

B. McNamara is with the department of Clinical Neurophysiology, Cork University Hospital, Cork, Ireland.

G.B. Boylan is with the School of Medicine, University College Cork, Ireland (e-mail: g.boylan@ucc.ie).

G. Shorten is with the department of Anaesthesia and Intensive Care Medicine, Cork University Hospital, Cork, Ireland. (e-mail: George.Shorten@mailp.hse.ie).

and Narcotrend™ are used in the context of depth-of-anesthesia monitoring. Each of these monitors is based on the calculation of one or more quantitative EEG measures [8, 9], that are thought to contain anesthesia/sedation-specific information. General anesthesia is very deep sedation with the absence of a cortical response to pain. Some of these devices [particularly the Bispectral or BIS™ monitor (Aspect Medical)] have been studied in the context of light sedation and are unable to reliably distinguish between light and deep sedation [1]. We set out to determine if the combination of a number of quantitative EEG measures or ‘features’ could be used to automatically determine a patient’s level of sedation.

II. METHOD

A. Data set We used an independently validated dataset of EEG recordings from 12 healthy patients aged 37+/-9yrs (M:F::4:8), who underwent sedation prior to anesthesia using propofol (2,6-disopropylphenol). The study protocol was approved by the Clinical Research Ethics Committee of the Cork Teaching Hospitals. All patients gave prior written informed consent.

B. Clinical Protocol A target-effect site propofol infusion was commenced to

provide a target-effect site concentration of 0.5 µgml-1. The target-effect site was brain. The target effect concentration was increased in 0.5 µgml-1 increments every four minutes to a maximum of 2 µgml-1. A period of 4 minutes was allowed at each concentration level to allow adequate equilibrium to be achieved across the blood-brain-barrier. For each patient the data acquisition period was thus 16 minutes.

C. EEG Acquisition Nineteen channels of EEG were recorded for each patient

using a NicVue™ digital EEG machine. For the purposes of analysis and comparison we calculated each quantitative EEG measure or ‘feature’ in the following bipolar channels:

Automated Estimation of Sedation depth from the EEG

B.R. Greene, P. Mahon, B. McNamara, G.B. Boylan, G. Shorten,

C Grade Criterion

0 Awake alpha rhythm 1 Decrease in α amplitude and α drop out

/ β activity ± theta activity 2 Theta wave activity, sleep spindles / K

complexes 3 Generalised delta activity 20-50%

epoch duration Table 1: EEG criteria for assignment of sedation grade

Proceedings of the 29th Annual InternationalConference of the IEEE EMBSCité Internationale, Lyon, FranceAugust 23-26, 2007.

FrP2A1.18

1-4244-0788-5/07/$20.00 ©2007 IEEE 3188

• 'P4-O2' • 'P3-O1' • 'Fp1-Fp2' • 'F3-C3' • 'F4-C4' • 'F3-F4'

Even numbers by convention refer to electrodes placed on the right side of the scalp. The occipito-parietal leads were chosen as alpha rhythm which originates in the posterior cortex is best seen in these leads. Fp1 – Fp2 represents a prefrontal location likely to be contaminated by EMG artifact from the frontalis muscle. Activity in the three other channels is representative of frontal and central lobe activity.

D. Sedation Measurement The EEG recordings were retrospectively assessed by a

clinical neurophysiologist blinded to the clinical sedation score. A sedation grade was assigned to each time period of four minutes corresponding to a set propofol concentration. The sedation grade was assigned according to preset criteria set out in table 1. For the purposes of this analysis any four minute period in which the sedation score was greater then zero was deemed to be ‘sedated’. There were 48 four minute intervals in twelve patients. Twenty nine had evidence of sedation with 19 ‘non-sedated’. Each record contained 19 EEG channels and was sampled at 250Hz. Records had a mean duration of 19.8 minutes. The dataset contained a total of 192 minutes of 19 channel EEG. 116 minutes of EEG were assigned the labeled ‘sedated’ while 76 minutes of EEG were assigned the label ‘non-sedated’. Table 2 summarizes the characteristics of each of the recordings.

E. Feature Extraction The EEG for each channel was low-pass filtered using a

type II Chebyshev IIR filter with a corner frequency of 34Hz to remove power line noise along with out-of-band noise. The EEG for each channel was then considered in epochs of 2 seconds duration. The following features were extracted

for each 2 s EEG epoch for each channel. • Spectral Entropy (HS) • Spectral Edge frequency (SEF) • Relative Alpha band power (Pα) • Relative Beta band power (Pβ) • Relative Delta band power (Pδ) • Relative Theta band power (Pθ)

Many of these measures have been used in previously reported studies [7, 10] and are currently in use in commercially available depth of anesthesia monitors [9, 11]. The EEG spectral entropy (HS) was calculated for 2 second epoch using Eqn.1, which normalizes HS to the range 0-1 [9].

∑−=f

feff

S XPXPN

X )(log)(log

1)(H (1)

Pf(X) is an estimate of the probability density function (PDF) and is calculated by normalizing the power spectral density (PSD) estimate with respect to the total spectral power. The PSD is calculated in the range 0.5-32Hz for each epoch using a 500-point fast fourier transform (FFT). Nf is the number of frequency components in the PSD estimate.

Spectral Edge frequency (SEF) was calculated according to the methodology discussed by a number of authors [12-14]. The total EEG power in the range 0.5-32Hz was calculated for each 2 second EEG epoch. The SEF was then calculated as that frequency (in Hertz) below which 90% of the total spectral power resides. The SEF features for each channel and for each patient were then normalized to have zero mean and unity standard deviation.

A number of authors have investigated the variation of EEG power bands with sedation and anesthesia [7, 15]. The spectral power in a number of EEG sub-bands was then calculated as the total spectral power in a given frequency range from the power spectral density estimate for each epoch. The relative power feature for each band was then the ratio of the power in a given sub-band to the total power in the 0.5-32 Hz band. The Alpha sub-band is defined as the spectral power in the range 8-13Hz, similarly the Beta sub-band is defined in the range 14-40Hz. The Delta and Theta sub-bands are defined in the ranges 0.5- 4Hz and 4-7Hz respectively [16].

Six features for each of the six channels were then combined into a feature vector (of length 36) which was then applied to the classifier.

F. EEG Artifact Rejection The EEG signal often contains variety of biological and

non-biological artifacts [17]. In this paper we have attempted to reject two kinds of artifact from subsequent analysis, namely: ‘movement’ artifacts which are large signal spikes caused by movement of the electrodes, and ‘zero-signal’ artifact caused by the amplifier being ‘powered-off’ in the course of a recording.

To identify the artifact sections of each EEG channel a

Patient #

Record Length (mins)

Sedated Time (mins)

Non-sedated

time (mins)

1 16.3 16 0 2 16.6 4 12 3 16.6 4 12 4 16.2 8 8 5 16.3 12 4 6 17.6 8 8 7 22.6 16 0 8 21.6 12 4 9 17.0 0 16 10 19.8 12 4 11 22.7 12 4 12 34.9 12 4

Mean: 19.8

Total: 116

Total: 76

Table 2: Record information

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zero mean EEG signal was first calculated by subtracting the mean of the EEG from each sample and then processing this signal as follows:

• The standard deviation of the absolute value of signal was calculated and any signal samples greater than six times the standard deviation were flagged as ‘movement’ artifact.

• Any 10 sample epoch whose mean was 100 times smaller than the 5% trimmed mean of the signal was flagged ‘zero-signal artifact’

Any 10 sample epoch on a given channel containing artifact was assigned the value 1. All epochs for each channel that did not contain artifact was assigned the value 0. If the mean artifact value across all channels exceeded an empirically derived threshold, the epoch was considered to contain artifact.

G. Classifier Model A linear discriminant (LD) based classifier model was

employed in this study. A linear discriminant classifier model, (based on the Mahalanobis distance), is defined completely by a mean vector for each class and a common covariance matrix. An LD model assumes normal class distributions and the same variance across classes. The class conditional mean vectors and a common covariance matrix were estimated entirely from the training data. Weighting of the class conditional mean vectors and common covariance matrix by the duration of each record was implemented as discussed by Greene et al. [18]. This ensures that records of differing lengths contribute equally to the training of the classifier.

H. Classifier Performance Estimation The automated depth of sedation monitoring system was

considered as a patient-independent or ‘generalized’ classifier. The performance of the patient-independent system was estimated using cross validation across all records. This involved training the classifier model on 11 of the 12 records and using the 12th record to test the classifier performance and then rotating through the 12 possible combinations of training and test sets. The mean of the results for all iterations is taken as a patient-independent classifier performance estimate. This test provides a measure of the systems’ ability to generalize from the training set and classify ‘unseen’ records as ‘sedated’ or ‘non-sedated’.

I. Algorithm Performance Measures

The classification accuracy (Acc) is defined as the percentage of 2 s epochs correctly classified by the system. The sensitivity (Sens) is defined as the percentage of ‘sedated’ epochs (as labeled by a Clinical Neurophysiologist) correctly identified as ‘sedated’ epochs by the system. Similarly, the specificity (Spec) is defined as the percentage of epochs labeled ‘non-sedated’ correctly classified as ‘non-sedated’ by the system. A receiver operating characteristic (ROC) curve is a graphical representation of class sensitivity against specificity as a threshold parameter is varied. The area under the ROC curve (ROC Area), is used as an index of sedated/non-sedated class discrimination for the patient independent classifier. A random discrimination will give an area of 0.5 under the curve while perfect discrimination between the two classes will give unity area under the ROC curve.

III. RESULTS Table 3 gives the classification performance for each

feature taken individually as well as all features combined, in classifying epochs of EEG as ‘sedated’ or ‘non-sedated’. On a patient independent basis using the combined features, 74.70% of sedated epochs were correctly classified as sedated (sensitivity of 74.70%) while 81.67% of non-sedated epochs were correctly classified as non-sedated (specificity of 81.67%).

Fig.1 shows an ROC plot for the output discriminant value of the patient independent classifier. The area under the

ROC curve was 0.86.

Feature Combined SEF H Pα Pβ Pδ Pθ

Measure Acc (%) 77.45 61.28 67.89 70.27 66.21 69.38 60.90 Sens (%) 74.70 62.32 75.28 69.07 65.49 70.98 65.52 Spec (%) 81.67 59.70 56.55 72.10 67.32 66.92 53.81 ROC Area 0.86 0.63 0.71 0.77 0.71 0.77 0.63

Table 3: Overall performance results for each feature taken individually as well as all features combined together and classified using a linear discriminant classifier model.

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Table 4 gives the individual cross-validated performances for each of the twelve patient recordings. Patients 1 and 7 contained no ‘non-sedated’ epochs and so have no associated specificity metric, while patient 9 contained no ‘sedated’ epochs and so has no associated sensitivity metric.

IV. DISCUSSION A system for the automated estimation of a patients’ level

of consciousness is presented here. The EEG for each patient was dichotomized by an experienced Clinical Neurophysiologist into two classes, sedated and non-sedated. Six quantitative EEG measures per EEG channel were used in this study. Each quantitative EEG measure has been used previously in depth of sedation / anesthesia research. Classifying each epoch using a LD classifier model led to a sedation sensitivity of 74.70% with an associated specificity of 81.67%.

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[7] L. D. Gugino, R. J. Chabot, L. S. Prichep, E. R. John, V. Formanek, and L. S. Aglio, "Quantitative EEG changes associated with loss and return of consciousness in healthy adult volunteers anaesthetized with propofol or sevoflurane," Br. J. Anaesth., vol. 87, pp. 421-428, 2001.

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[12] R. J. Hudson, Stanski, D.R., Saidman, L.J., Meathe, E., "A model for studying depth of anesthesia and acute tolerance to thiopental.," Anesthesiology, vol. 59, pp. 301–308., 1983.

[13] T. E. Inder, L. Buckland, C. E. Williams, C. Spencer, M. I. Gunning, B. A. Darlow, J. J. Volpe, and P. D. Gluckman, "Lowered Electroencephalographic Spectral Edge Frequency Predicts the Presence of Cerebral White Matter Injury in Premature Infants," Pediatrics, vol. 111, pp. 27-33, 2003.

[14] J. Fell, J. Roschke, K. Mann, and C. Schaffner, "Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures," Electroencephalogr Clin Neurophysiol., vol. 98(5):401-10, 1996.

[15] G. S. Schneider, S, Kochs, E., "Does bispectral analysis add anything but complexity? Bis subcomponents may be superior to BIS for detection of awareness," Br. J. Anaesth., vol. 93, pp. 596-597, 2004.

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[17] P. J. Durka, H. Klekowicz, K. J. Blinowska, W. Szelenberger, and S. Niemcewicz, "A simple system for detection of EEG artifacts in polysomnographic recordings," IEEE Trans. Biomed Eng., vol. 50, pp. 526-528, 2003.

[18] B. R. Greene, P. de Chazal, G. B. Boylan, S. Connolly, and R. B. Reilly, "Electrocardiogram based Neonatal Seizure detection," IEEE Trans. Biomed. Eng., vol. 54, pp. 673-682, 2007.

0 10 20 30 40 50 60 70 80 90 1000

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80

90

100

Sen

sitiv

ity [%

]

Specificity [%]

Patient Independent ROC curve

Figure 1: Receiver operating characteristic curve for all features combined into a patient independent classifier. Area under the ROC curve was 0.86.

Patient # Acc (%) Sens (%) Spec (%)

1 82.18 82.18 -

2 85.92 58.82 94.96

3 76.42 20.83 95.21

4 77.47 73.95 81.01

5 66.18 59.22 87.29

6 67.78 55.65 79.92

7 98.12 98.12 -

8 78.24 71.31 99.16

9 74.58 - 74.58

10 75.10 78.55 64.71

11 71.85 72.91 68.64

12 75.52 86.87 41.67

Table 4: Patient independent classifier results for each patient. Six features were extracted for six EEG channels and classified as sedated or non-sedated using a linear discriminant classifier model.

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