IDENTIFYING THE CARDIAC REGION IN IMAGES

OF ELECTRICAL IMPEDANCE TOMOGRAPHY

THROUGH WAVELET TRANSFORM

José Pedro de Oliveira, Harki Tanaaka and Neli Regina Siqueira Ortega

School of Medicine, University of São Paulo, LIM01/HCFMUSP, Brazil

Keywords: Electrical Impedance Tomography, Medical decision support system, Wavelet transform.

Abstract: Electrical Impedance Tomography (EIT) is an imaging technique, still in development, in which an image

of the conductivity of part of the body is inferred from surface electrical measurements. Despite the

benefits, its resulting image still does not offer a good spatial resolution. In EIT images of the thorax the

largest challenge is the treatment of the lung perfusion, especially in the identification of the cardiac region.

The current EIT equipments, used in the treatment of ICU (Intensive care unit) patients with respiratory

complications, have a need to better define the cardiac region in dynamic EIT images, in order to

offer a better system of support to the medical decision. In an attempt to overcome this problem,

Tanaka and collaborators proposed a methodology using the theory of fuzzy sets. Fuzzy logic

approach allowed a more appropriated treatment of the uncertainty in identifying the pixel as

belonging to a pulmonary or cardiac region. However, this fuzzy model presented some

limitations when submitted to different clinical conditions, such as PEEP variation. In this work

we present a methodology based on wavelet transform for analysis of the EIT signals of the

pulmonary perfusion obtained in an animal experiment.

1 INTRODUCTION

Electrical Impedance Tomography (EIT) is an

imaging technique, still in development, in which an

image of the conductivity of a transversal section of

an object is inferred from electrical measurements

done in a series of electrodes placed on its surface.

Despite the benefits presented by this technique, it

has some limitations, amongst which we detach the

low spatial resolution. Although the EIT method

presents a high time resolution, the low spatial

resolution hinders the characterization of the activity

of regions according to their physiological origin in

a dynamic image. One of the most important EIT

studies concerns to the images of the thorax, aiming

the monitoring the cardio-respiratory functions. In

that sense, Tanaka et al. proposed the use of the

theory of fuzzy sets to deal with the uncertainty of

identification of the pixels, aiming a segmented

image containing the cardiac and pulmonary regions

(Tanaka et al, 2008). Despite the good results, this

model presented some limitations in identifying the

heart in more complex situations. Therefore, it is

realized that a tool that could extract more

information from EIT signal could be capable to

separate with more clearness the pulmonary and

cardiac regions. One of the tools capable to extract

information of signals in the time and frequency

domains, with possibilities to minimize this

limitation, is the Wavelet Transform.

Wavelet transform emerged as a result of

developments in this type of representation of

signals. Basically, there are three classes of wavelet

transforms: the Continuous Wavelet Transform

(CWT), the Semi-Discrete Wavelet Transform

(SWT) and the Discrete Wavelet Transform (DWT)

(Polikar, 1999). The DWT is a multi-resolution

representation of the original signal and is

particularly useful for noise reduction and data

compression, whereas the CWT is better for feature

extraction purposes. The SWT is simply a

discretization of the CWT in order that this can be

processed by computers and digital equipments.

A typical EIT system has the following

components: a set of current injection electrodes; a

system of measure of electric potentials between the

electrodes; and an algorithm of image reconstruction

(Adler et al, 1997). Among the benefits offered by

this technique, we detach: it is a noninvasive

technique; it presents a high temporal resolution; the

380

Oliveira J., Tanaaka H. and Ortega N. (2009).

IDENTIFYING THE CARDIAC REGION IN IMAGES OF ELECTRICAL IMPEDANCE TOMOGRAPHY THROUGH WAVELET TRANSFORM.

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 380-383

DOI: 10.5220/0002277203800383

 SciTePress

Table 1: Heart maps provided by the systems for each PEEP values: a) the wavelet analysis; b) the fuzzy approach; and c)

the saline injection.

PEEP WaveletAlgorithm 1 WaveletAlgorithm 2 Fuzzy Saline

H2O

(ZEEP)

equipments are cheap, relatively small and can be

installed at the bedside of the patient. As for its

limitations, we can point the low spatial resolution

and the relationship between the differences of

electrical potential measures on the surface and the

internal parameters of the object in analysis is not

linear, which difficult the development of the image

reconstruction algorithm (Tanaka et al, 2008;

Brown, 2003; Noor, 2007).

2 MATERIALS AND METHODS

The image generated by the EIT system used is

formed by 1024 pixels, where each pixel

corresponds to a certain area of the body of the

object under analysis. These pixels have variations

in their impedances due to the flow of blood during

the cardiac cycle. In order to develop a methodology

able of identifying the heart region in EIT images of

a thorax, it is necessary a qualitative analysis of the

information contained in these signals. Firstly, this

qualitative analysis was based on the wave patterns

and after each pattern was discussed with a panel of

experts, taking into account the physiological

knowledge about the cardio-respiratory dynamic.

This analysis was the fundamental importance in the

model development, because it provided the

standard behavior for each region of chest (lungs,

ventricle, atrium, aorta, etc.).

EIT raw data were collected in an animal

experiment (pig) by means of an electrical

impedance tomography, based on the Enlight ®

technology (DIXTAL, Brazil), which uses the image

reconstruction algorithm developed by Lima and

collaborators (Lima et al, 2005). EIT signal was

synchronized with the peak of the wave "R" of the

ECG signal. The pig was submitted to different

values of Positive End-Expiratory Pressure (PEEP):

18cmH2O (PEEP18), 12cmH2O (PEEP12) and

0cmH2O (ZEEP). This is important to evaluate the

robustness of the system developed in different

clinical conditions. For each one of these PEEPs was

done an experiment using a hypertonic solution

(20% NaCl), which serves as contrast to EIT images,

during apnea. The EIT images obtained by means of

this contrast allows localizing

the different

structures for where the flow passed. The objective

with the saline data is to establish a reference base,

so it will be possible to compare the results of the

IDENTIFYING THE CARDIAC REGION IN IMAGES OF ELECTRICAL IMPEDANCE TOMOGRAPHY THROUGH

WAVELET TRANSFORM

381

Table 2: ROC curves obtained for the wavelet and fuzzy approaches, compared with the heart region obtained through the

saline injection, for each PEEP values, where AUC means Area under of Curve.

THE ROC CURVES: __Wavelet ---Fuzzy

PEEP18 PEEP12 ZEEP

WAVELET

ALG1

AUC

Wav

=0.83; AUC

Fuz

=0.88

AUC

Wav

=0.94; AUC

Fuz

=0.86

AUC

Wav

=0.82; AUC

Fuz

=0.95

WAVELET

ALG2

AUC

Wav

=0.91; AUC

Fuz

=0.88

AUC

Wav

=0.97; AUC

Fuz

=0.86 AUC

Wav

=0.83; AUC

Fuz

=0.95

present model with this EIT contrast.

In order to identify the regions with similar

pixels, we began our analysis by a qualitative

evaluation from the patterns presented by EIT

signals. Based on experts’ experience of the

respiratory ICU at the Clinics Hospital of São Paulo,

Brazil, in a consensus method, we took off

conclusions about on which organ (heart or lungs)

each pixel belonged with larger possibility. This

analysis requires that the beginning of each EIT

signal must be synchronized with the peak of the

wave "R" of the ECG signal, marking the beginning

of the systolic phase. It allow us to identify the

pixels with the standard behavior of heart or lung.

According to discussed above, we proposed the

following method to analyze extracted signals of

thorax by means of Electrical Impedance

Tomography in order to identify the cardiac region:

a) We suppose that the pigs are healthy.

Therefore, the cardio-respiratory functions are

performed with certain normality and so we can

consider the same standard behavior found by

qualitative physiological analysis;

b) Two algorithms using the Wavelet Transform

were developed based on finding the pixel that had

the largest positive variation in its impedance during

the first half of the cardiac cycle and decomposing

the signal of the pixel above by means of the CWT

using the wavelet gaus4.

3 RESULTS AND DISCUSSIONS

The analysis of EIT signals of the thorax of the pig

with controlled pressure PEEP18 was used to

identify the macro regions according to variations of

blood flow during the cardiac cycle. After

physiological analysis, we determined a typical

variation of impedance of those found in ventricular

region during the cardiac cycle, in which the

different phases of the cardiac cycle are very well

defined. In order to find the adequate wavelet for our

analysis, several tests were performed. The wavelet

that adjusted better to the EIT signals was the gaus4.

Table 1 shows the heart maps provided by

wavelet and fuzzy approaches, and also the maps

found with the saline contrast. We can note in these

figures that the pixels, corresponding to the heart

region, are located in a superior central position,

independently of the PEEP values. It important to

detach that in this region there is a crossover of the

different cardio-breathing functional structures, such

as ventricle,

atrium, lung, aorta, and so on, which

causes important alterations in the expected pattern

for the dynamics of the cardiac cycle. We can also

note in table 1 that the wavelet approach, mainly the

wavelet algorithm 2, provides an almost crisp heart

map. Thus, in this method the pixel identification

uncertainties are practically vanished. The fuzzy

model provides an image containing pixels of

several magnitudes in the interval [0, 1], so varying

a cutoff value, the area corresponding to the heart

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382

region may contracts or expands. On the other hand,

varying the cutoff in the wavelet approach, the area

correspondent to the heart region practically does

not change. In this sense, the fuzzy model is

dependent of the cutoff value, which clearly is not

desirable in an automatic process. Other fact of great

importance in an automatic process is that the fuzzy

model is dependent of the PEEP settings, i.e., for

each PEEP its parameters need to be changed. This

limitation does not have in the wavelet model; its

configuration is not dependent on PEEP value.

Comparing the heart maps generated from wavelet

and fuzzy models with the image of the cardiac

region obtained by the saline method, we elaborated

ROC curves for each PEEP value. Table 2 presents

these results. We can note that both models

presented excellent agreements with the saline

injection. ROC curves of fuzzy and wavelet models

were practically equivalent. In general, the best

results were found with the wavelet algorithm 2.

Due to the movement of the heart in the

longitudinal axis and to the fact that the lungs should

be more closed in the ZEEP condition, it is expected

that the identification of the pixels as belonging or

not to each anatomical structure becomes more

difficult in this situation. In this sense, we expected

that the agreement would be larger for PEEP18,

followed by smaller values of agreement for

PEEP12 and ZEEP, respectively. However, this

monotonic behavior was not observed. The possible

hypothesis for this fact is that the ventricle was

dislocated to a more central region due to the heart

movement.

4 CONCLUSIONS

The wavelet methodology proposed was able to

identify the heart region from EIT data of perfusion

when compared with the saline method and fuzzy

model. Both methodologies present limitations to be

overcome. In the wavelet approach, we should have

extended our analysis to other cardiac chambers

beside the ventricular region. In the fuzzy model, the

limitations consist in the cutoff and PEEP

dependences in the generation of the heart maps.

These limitations should be understood in a more

deep work.

Finally, the results presented here were

encouraging and indicate that the modeling of EIT

images pos data acquisition, besides the

reconstruction algorithm, is a good way that should

be certainly explored

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WAVELET TRANSFORM

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