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Conference of the IEEE EMBS

Cité Internationale, Lyon, France

August 23-26, 2007.

Abstract—The anesthetic infusion with propofol influences

EEG activity rather smoothly by changing the amplitude

activity in different frequency bands. This results in a

frequency progression pattern (FPP) which can be related to

the depth of anesthesia. An iterative algorithm is proposed for

the estimation of the shape of this pattern. The presented

method is applied to the data recorded from the start of the

propofol anesthetic infusion to the onset of the burst

suppression pattern (BSP) with nine patients. The results reveal

the underlying FPP and how the onset of the BSP is related to

it. The proposed method offers potential for the development of

automatic assessment systems for the depth of anesthesia.

I. INTRODUCTION

ONITORING the functional suppression of the central

nervous system due to anesthetic infusion is of interest

in clinical practice. No reliable method for that purpose has

been introduced [1]. So far, the level of consciousness has

been evaluated by observing the occurrence of clinical end-

points, such as loss of obeying verbal command (LVC) [2],

eyelash reflex [3] or movement as a response to a skin

incision [4]. However, the clinical end-points are discrete

behavioral signs and occur relatively sparsely. For anesthesia

depth monitoring continuously measurable phenomena

would be more suitable.

Electroencephalographic (EEG) signals, which reflect the

electrical potential differences between different locations on

the scalp, provide an invaluable tool for monitoring the depth

of anesthesia. EEG activity is influenced rather smoothly and

continuously by anesthetic infusion. For example, with

propofol, an initial increase followed by a decrease, that is, a

biphasic effect of high frequency (alpha and beta) EEG

activity has been observed [5], [6]. Simultaneously with the

decrease of activity in high frequencies, an increase in low

frequency (delta) activity can be seen. These phenomena

result in a frequency progression pattern (FPP) specific to

the used anesthetic.

A connection between the FPP and the depth of anesthesia

Manuscript received June 12, 2007. This work was supported in part by

the GETA Graduate School, Tauno Tönning Foundation, and Finnish

Foundation for Economic and Technology Sciences – KAUTE.

J. Kortelainen is with the Department of Electrical and Information

Engineering, BOX 4500, FIN-90014 University of Oulu, Finland (e-mail:

jukka.kortelainen@ee.oulu.fi).

M. Koskinen and T. Seppänen are with the Department of Electrical and

Information Engineering, BOX 4500, FIN-90014 University of Oulu,

Finland.

S. Mustola is with the Department of Anesthesia, South Karelia Central

Hospital, FIN-53130 Lappeenranta, Finland.

has been reported. In [7], the FPP related to propofol

anesthetic infusion was examined from the start of the

infusion to the LVC in a frequency range of 0.5-28 Hz. A

strong relation between the EEG spectral behavior and the

LVC was found. This relation was later utilized successfully

in forecasting the LVC [8]. The frequency progression

occurring after the LVC has not yet, however, been properly

elucidated. In addition, for the illustration of FPP, its general

shape should be determined. This is complicated by the

interindividual variability in response to anesthetic agent: the

FPPs differ in time between patients, as was reported in [7].

In this paper, the FPP after the LVC is presented. A

method is proposed to calculate the general shape of FPP

during induction of anesthesia. The developed method is

applied to real patient data and the FPP from the start of the

propofol infusion to the onset of the burst suppression

pattern (BSP) is determined. Section II describes in detail the

method and the data acquisition procedure. The results are

presented in section III. In section IV, the study is

concluded.

II. MATERIALS AND METHODS

A. Patients and Anesthesia

Nine patients (aged from 18 to 58 years), scheduled for

surgical procedures, were anesthetized by intravenous fixed

rate (30 mg/kg/h) infusion of propofol (Propofol Abbot,

Abbot Lab. Chicago, USA) via the syringe pump (Perfusor,

Braun Melsungen, Germany). The infusion was continued

until the onset of the BSP was observed on screen after

which the anesthesia was continued at the discretion of the

anesthetist. The study was approved by the institutional

Ethics Committee of South Karelia Central Hospital and the

patients gave an informed-written consent to participate.

B. Data Acquisition

EEG was recorded with an Embla polygraphic recorder

(Medcare, Reykjavik, Iceland) with the sampling rate and the

bandwidth of 200 Hz and 0.5-90 Hz, respectively. The

electrode montage used in analysis was Fz with the common

average reference. The montage was formed off-line from

unipolar recordings. EEG was recorded from the start of the

infusion to 10 min after the onset of the BSP.

C. Amplitude Trend Estimation

All the signal processing was performed with Matlab

technical computing language (The MathWorks Inc, Natick,

MA). First, the EEG activity in different frequency bands

EEG Frequency Progression during Induction of Anesthesia: from

Start of Infusion to Onset of Burst Suppression Pattern

Jukka Kortelainen, Miika Koskinen, Seppo Mustola, Tapio Seppänen, Member, IEEE

M

Proceedings of the 29th Annual International

FrA02.1

1-4244-0788-5/07/$20.00 ©2007 IEEE1570

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Filter bankFilter bank

EEG

Amplitude trend estimation

•IPG-FMH filter

•Downsampling

•Savitzky-Golay filter

•Amplitude normalization•Amplitude normalization

Amplitude trend estimation

•IPG-FMH filter

•Downsampling

•Savitzky-Golay filter

Amplitude trends

Fig. 1. The signal processing steps related to amplitude trend

estimation. See text for details.

was determined. The approach presented in [6] was followed

and the performed signal processing steps are illustrated in

Fig. 1. A finite impulse response (FIR) filter bank with eight

filters was applied to the EEG with a frequency range from

0.5 Hz to 28 Hz. The used passbands were 0.5-4 Hz, 4-8 Hz,

8-12 Hz, 12-16 Hz, 16-20 Hz, 20-24 Hz, and 24-28 Hz,

where the limits indicate the -3 dB points. Furthermore, a

passband containing the whole frequency range (0.5-28 Hz)

was included. The delay caused by filtering was corrected by

discarding a number of samples, corresponding to the half of

the filter order, from the beginning of filtered signal.

The bandpass filtering resulted in eight derived signals for

each patient. The signals were converted into absolute value

time-series, which corresponds to multiplying the negative

values by -1. The signal trends were then extracted using an

in-place growing FIR-median-hybrid filter (IPG-FMH) with

a 1500-level median operator [9]. This filtering method was

chosen since it robustly removes transient spikes and short

variations when the filter length is properly selected. To

prevent the loss of data 1500 zeros were padded into the

beginning and ending before filtering. To reduce the number

of samples, the trends were next downsampled to 1 Hz. The

downsampling procedure included application of a lowpass

FIR filter to avoid aliasing. Due to the edge-preserving

characteristics of the IPG-FMH filtering, the resulting time-

series contained step-like noise. Therefore, a Savitzky-Golay

filter [10] with the polynomial order of three and the frame

size of 101 was applied. These parameter values provided

smoothing without modifying the general trend. Finally, to

reduce the interindividual amplitude variation, the traces

were normalized by dividing them by their mean value

between the start of the infusion and the onset of the BSP.

These signal processing steps resulted in eight amplitude

trends for each patient, representing the EEG activity in

different frequency bands.

D. FPP Shape Estimation

The amplitude trends of all nine patients from the start of

the infusion to the onset of the BSP are given in Fig. 2A.

These trends suggest that there is an underlying FPP similar

for all patients. Even though the trends of different patients

resemble each other in shape, the duration of the frequency

progression varies due to the interindividual variability in

response to anesthetic agent. For revealing the general shape

of the trends, an iterative algorithm was developed. The

algorithm consists of the following four steps:

1) Make an initial guess of the general shape.

2) Align the amplitude trends of all patients to the general

shape by time scaling.

3) Determine a new general shape by calculating the

average of the aligned amplitude trends.

4) If the new general shape differs significantly from the

previous one return to step 2, otherwise exit.

In the first step, an initial guess of the general shape is

made. The amplitude trends of any patient are suitable for

this purpose. Because the initial guess acts as a template in

the next step of the algorithm, it should contain the FPPs

from the start of the infusion to the onset of the BSP

occurring in the amplitude trends of the other patients. To

ensure this, the amplitude trends used as an initial guess are

extended to last from the start of the infusion to 5 min after

the onset of the BSP. The first step of the algorithm is

illustrated in Fig. 3A.

In the second step of the algorithm, the amplitude trends

from the start of the infusion to the onset of the BSP are time

scaled to match the general shape. For each patient, an

individual time scale minimizing the mean squared error

between the amplitude trends and the general shape is

determined. Let Y = [y1(n),y2(n),…,y8(n)]T represent the

amplitude trends of one patient and G = [g1(n),

g2(n),…,g8(n)]T the general shape. The operation (·)T denotes

the transpose. The length of the amplitude trends referring to

the optimal time scale is calculated by

(1)

where GN is a matrix containing N first samples of G and the

operation ||·||F stands for the Frobenius norm. Matrix ŶN

contains the amplitude trends of the original Y interpolated

to N samples, that is, scaled in time. For YN, a linear

interpolation is used. Due to the strong Savitzky-Golay

filtering performed before, the aliasing is avoided and no

lowpass filtering is needed. In addition to the length of the

aligned amplitude trends, the parameter N indicates the

position of the onset of the BSP on the general shape. The

mean squared error is calculated in (1) for all N∈ [t1,t2]. The

result, Nˆ, refers to the length yielding the best fit and

therefore reflecting the optimal time scale of the amplitude

trends. Variables t1 and t2 determine the shortest and the

longest length of the aligned amplitude trends, respectively.

The upper limit, t2, should not be greater than the number of

samples in G. Fig. 3B elucidates the second step of the

algorithm.

,

ˆ

8

1

min

[

tN

∈

arg

ˆ

2

],

21

F

NN

t

N

N

GY −=

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02468

0

1

2

0.5−4 Hz

A.U.

A

0

1

2

B

0

1

2

C

02468

0

1

2

4−8 Hz

A.U.

0

1

2

0

1

2

02468

0

1

2

8−12 Hz

A.U.

0

1

2

0

1

2

02468

0

1

2

12−16 Hz

A.U.

0

1

2

0

1

2

02468

0

1

2

16−20 Hz

A.U.

0

1

2

0

1

2

02468

0

1

2

20−24 Hz

A.U.

0

1

2

0

1

2

02468

0

1

2

24−28 Hz

A.U.

0

1

2

0

1

2

024

t (min)

68

0

1

2

0.5−28 Hz

A.U.

0

1

2

0

1

2

Fig. 2. (A) The amplitude trends of the nine patients. The trends are from the beginning of propofol infusion (t = 0) to the onset of the BSP. (B) The

same amplitude trends, now optimally time scaled to match the iteratively calculated general shape. There is no unit on x-axis since the alignment

results in different time scale for each patient. (C) The general shape calculated from the optimally time scaled and extended amplitude trends. The

markers ‘O’ indicate the onsets of the BSP, i.e. the end points of traces in (B).

In step three, a new general shape is formed. This could be

done straight by calculating the average of the optimally

aligned amplitude trends computed in previous step.

However, to avoid the continuous changing of the time scale

of the general shape between iterations, the aligned trends

are first changed to a fixed scale. In this study, the original

time scale of the patient with the greatest Nˆ, i.e. having the

latest onset of the BSP after alignment to the general shape,

is used. The aligned amplitude trends of all patients are time

scaled together so that the trends of the patient with the

greatest Nˆ reach their original time scale. The new general

shape is formed from these trends. The advantage of this

approach is that the time scale of the general trend does not

change between iterations. This is essential for the

convergence of the algorithm. The time scaling is again

performed with linear interpolation like in step two. Since

the new general shape acts as a template in the next iteration

round, it should contain the FPPs from the start of the

infusion to the onset of the BSP occurring in the amplitude

trends of all patients. To ensure this, the amplitude trends

used in the formation of the general shape are extended like

the initial guess in step one (see Fig. 3C). This time the

trends are extended so that the point of 1 min after the onset

of the BSP of the patient with greatest Nˆ is reached. The

new general shape is the average of these extended trends.

In the fourth step, the new general shape is compared to

the previous one and, if no significant difference is found,

the iteration is stopped. Otherwise, the iteration is continued

from step two. The comparison is performed by calculating

the mean squared error between the new general shape (Г =

[γ1(n),γ2(n),…, γ8(n)]T) and the previous one (G =

[g1(n),g2(n),…,g8(n)]T ):

ε

(2)

.

8

1

2

F

KK

K

G

Γ

−=

1572

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0

1

2

0.5−28 Hz

A.U.

0

1

2

0.5−28 Hz

A.U.

0

1

2

0.5−28 Hz

A.U.

0

1

2

0.5−28 Hz

A.U.

a

b

c

A

B

C

D

Fig. 3. The general shape estimation algorithm illustrated in one

passband (0.5-28Hz). Amplitude scales are in arbitrary units (A.U.).

(A) The initial guess of the general shape is made by choosing the

amplitude trends of one patient. The trends are extended (dotted line)

to 5 min after the onset of the BSP. The marker ‘O’ indicates the

onset of BSP. (B) The amplitude trends of all patients are aligned to

the general shape (solid line) by time scaling. The dotted lines

indicate the first (a: N = t1), the best (b:

alignment of one patient. (C) After changed to a fixed time scale, the

aligned amplitude trends are extended (dotted lines) to a consistent

length. The new general shape is the average of these extended trends.

(D) The new general shape (solid line) is compared to the old one

(dotted line) and the decision about the continuation of the iteration is

made.

N

ˆ

N

=

) and the last (c: N = t1)

Matrices ΓK and GK consist of K first samples of Γ and G,

respectively, where K is the number of samples in the shorter

general shape. Therefore, if Γ has fewer samples than G, ΓK

= Γ. The iteration is stopped when ε reaches smaller value

than a predefined threshold. The last step of the algorithm is

clarified in Fig. 3D.

III. RESULTS

The amplitude trends of all nine patients are given in Fig.

2A. Since the duration of the frequency progression and the

onset times of the BSP vary between patients, the underlying

pattern is not very obvious and no general shape can be

directly calculated from these trends. In Fig. 2B the

optimally time scaled amplitude trends are plotted after the

general shape estimation. Because of a proper time scaling,

the underlying FPP is now clearly observable. The iteratively

calculated general shape is illustrated in Fig. 2C. The shape

shows the EEG activity progression from high to low

frequency bands during the induction of anesthesia. Due to

the high level of noise, the amplitude trends of one patient

were excluded from the formation of new general shape. The

noise occurred however after the onset of the BSP and hence

did not affect to the alignment in step two. In the same figure

the onsets of the BSP are shown. With the exception of a few

outliers, the onsets seem to cluster together. The outliers are

mainly from the patients without a clearly distinguishable

onset of the BSP. These results refer to a relation between

the EEG spectral behavior and the onset of the BSP.

IV. CONCLUSION

This paper presented a method for the determination of the

EEG FPP during induction of anesthesia. The method is

based on an iterative algorithm determining the general

shape of the calculated amplitude trends. It was applied to

data recorded from the start of the propofol infusion to the

onset of the BSP with nine patients. The results revealed the

underlying EEG activity progression from high to low

frequency bands and how the onset of the BSP is related to

this progression. The proposed method offers potential for

the development of automatic assessment systems for the

depth of anesthesia.

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