World Journal of Cardiovascular Diseases, 2013, 3, 464-470 WJCD
http://dx.doi.org/10.4236/wjcd.2013.37073 Published Online October 2013 (http://www.scirp.org/journal/wjcd/)
Cardiac mapping of electrical impedance tomography by
means of a wavelet model
Harki Tanaka1, Neli Regina Siqueira Ortega2, Andre Hovnanian3,
Carlos Roberto Ribeiro de Carvalho3, Marcelo Britto Passos Amato3
1Center of Engineering, Modelling and Applied Social Sciences, Federal University of ABC, São Paulo, Brazil
2Center of Fuzzy Systems in Health, School of Medicine, University of São Paulo, São Paulo, Brazil
3Respiratory Intensive Care Unit, Hospital das Clínicas, School of Medicine, University of São Paulo, São Paulo, Brazil
Received 25 July 2013; revised 26 August 2013; accepted 14 September 2013
Copyright © 2013 Harki Tanaka et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
To improve the identification of cardiac regions in
Electrical impedance tomography (EIT) pulmonary
perfusion images, a model of wavelet transform was
developed. The main goal was to generate maps of the
heart using EIT images in a controlled animal ex-
periment using a healthy pig and in two human vol-
unteers. The model was capable of identifying the
heart regions, demonstrated robustness and gener-
ated satisfactory results. The pig images were com-
pared to perfusion imag es obtained using injection of
a hypertonic solution and achieved an average area of
the ROC curve of 0.88. The human images were qua-
litatively compared with Computerized Tomography
scan (CT-scan) images.
Keywords: Electrical Impedance Tomography; Heart;
Image Analysis; Image Segmentation; Wavelet
1. INTRODUCTION
Electrical impedance tomography EIT is an imaging
technique, still in develop ment, in which an image of the
conductivity of a transverse section of an object is in-
ferred from electrical measurements performed using a
series of electrodes placed on its surface [1,2]. Despite its
benefits, the method has a major drawback, as its low
spatial resolution hinders the characterization of the ac-
tivity of regions according to their physiological origin in
a dynamic image. This difficulty in the interpretation of
images can be translated as an uncertainty in the identi-
fication of pixels in both anatomical and functional terms.
Important EIT studies focus on the acquisition and inter-
pretation of thoracic images. During the cardio-respira-
tory cycle, air and blood share the same compartment
and rhythmically change their volumes. Thus one major
application of EIT is the monitoring of cardio-resp iratory
functioning [3-8]. The EIT method has several benefits:
it is a non-invasive method with no harmful radiation,
has a high temporal resolution and low cost, and allows
for the bedside monitoring of a patient [6]. The low spa-
tial resolution of EIT images remains an unsolved prob-
lem and provides opportunities for new approaches to
improve EIT imaging applications [6]. Low spatial reso-
lution can be addressed through improvements in the
image construction algorithm [6-9] and through the de-
velopment of systems based on post-constructed images
using dynamical and physiological information. There
are many techniques for the analysis of biomedical sig-
nals. One of the most common is the Fourier transform.
However, despite its vast applicability, the Fourier trans-
form is not able to provide a time-frequency representa-
tion of the signals, which is important in the analysis of
non-stationary signals [10]. The wavelet transform is
emerged as a tool to overcome this limitation [11,12]. In
this work, a new methodology was proposed to improve
the spatial resolution of EIT images of pulmonary perfu-
sion based on wavelet tra ns fo rm.
2. METHODS
To develop a methodology capable of identifying the
heart region in EIT images, a qualitative analysis of bio-
impedance waveforms was performed. In this qualitative
analysis, each pixel signal was grouped into characteris-
tic regions based on the signal’s waveform patterns.
These grouped patterns were discussed with a panel of
experts, taking into account the physiological knowledge
of cardio-respiratory dynamics. This qualitative analysis
provided the characteristic behavior of each region of the
chest used in the algorithm develop ment. The model was
Published Online October 2013 in SciRes. http://www.scirp.org/journal/wjcd

H. Tanaka et al. / World Journal of Cardiovascular Diseases 3 (2013) 464-470 465
developed based on the collected animal experimental
data and evaluated using both the animal and human EI T
data. This study was approved by the Research Ethics
Committee of the School of Medicine of São Paulo Uni-
versity (protocol number 0832/07), and subjects gave
informed consent to the work.
2.1. Data Collection
In this work, four data sets were used:
1) EIT perfusion data extracted from one healthy male
pig, called P1, used for wavelet method development;
2) EIT saline data extracted from the same pig used as
the EIT reference image;
3) EIT perfusion data extracted from two healthy male
humans, called H1 and H2, used for the evaluation of the
developed method; and
4) CT-scan images obtained from both men used as
reference thoracic images.
2.1.1. EIT Data Acquisition in an Animal Experiment
EIT data were collected using the Enlight® (Timpel SA,
Sao Paulo, Brazil). The electrical current used in the
electrodes was 5 mA at 125 KHz. Each EIT image con-
sisted of a matrix of 32 × 32 pixels and was acquired at
an image acquisition rate of 50 frames per second. An
ECG-gated image set was generated to represent the car-
diac cycle and was synchronized with the peak of the
R-wave of the ECG signal [6]. The wavelet transform
was applied to the EIT signals of perfusion images, ob-
tained by means of the ECG-gated temporal averaging of
the EIT raw data. During the experiment, the pig was
submitted to mechanical ven tilation assistance, and three
positive end-expiratory pressure (PEEP) ventilation pa-
rameters were used: 18 cm H2O (PEEP18), 12 cm H2O
(PEEP12) and 0 cm H2O (ZEEP). For each PEEP value,
5 ml of a hypertonic solution (20% NaCl) was injected
through a catheter into the right atrium of the pig during
apnea [6]. Saline was used as a contrast agent for EIT
imaging [13] and allowed for mapping of cardiovascular
regions. This experiment has been previously described
in full in [6].
2.1.2. EIT Data Acquisition in Human Subjects
The same EIT tomography based on the Enlight® tech-
nology was used to collect EIT data of the human thorax.
The data were collected from two volunteers, and in both
subjects, the EIT data were acquired with ECG signals.
Volunteer H1 was placed in a supine rest position, and
thirty-two electrodes were placed circumferentially and
equally spaced around the thorax at three positions (Fig-
ure 1): high level—the great vessels, aorta and pulmo-
nary artery; medium level—the transition area between
the region of the great vessels and the ventricles; and low
level—the ventricles region. Therefore, three sets of EIT
Figure 1. Thirty-two electrodes were placed circumferentially
and equally spaced around the human thorax at three positions.
raw data were collected. The volunteer H2 was placed in
two positions: a supine rest position and a sitting rest
position.
Thirty-two electrodes were placed circumferentially
and equally spaced around the thorax at the level of the
cardiac chambers, and two sets EIT raw data were col-
lected.
2.1.3. Comput ed Tomography (CT) Ima ge Acquisitio n
in Human Subjects
A 16 -channel X-ray CT (Angio CT, General ElectricTM)
was used to generate cross-sectional images of the thorax.
These thoracic images were obtained from the same
volunteers that were submitted to EIT data collection.
The volunteers were healthy; one (H1) was 32 years old,
and the other (H2) was 29 years old. CT angiography of
pulmonary arteries was performed with an injection of
15 mL X-ray contrast into the right antecubital vein. The
image acquisition was performed at the same level as the
EIT electrodes band.
2.2. Model Elaboration
Model development was based on the EIT images of
pulmonary perfusion obtained in the animal experiment
under PEEP18. The images acquired at PEEP12 and
ZEEP were used to improve the method. This multistep
evaluation was important to evaluate the robustness of
the system.
2.2.1. Quali ta t i ve Analysis of EIT Temp oral Signal s
Patterns
Images of one cardiac cycle were produced from the col-
lected pig thorax data with controlled pressure PEEP18
using a coherent mean method [6]. Qualitative analysis

H. Tanaka et al. / World Journal of Cardiovascular Diseases 3 (2013) 464-470 466
of the EIT temporal signals of the pixels was performed
to identify macro regions according to the observed
variations in bio-impedance during the cardiac cycle. To
identify the regions with similar signals, a qualitative
evaluation of the signal patterns was performed. Fol-
lowing this qualitative analysis, the pixels were grouped
into regions based on similarities in their dynamic be-
haviors, and a characteristic pixel for each region was
chosen. After the identification of the regions and their
characteristic signals, each signal was analyzed by quail-
tatively comparing the variation of impedance with the
variation of blood flow during the cardiac cycle. Based
on the experience of experts in the respiratory ICU at the
Clinics Hospital of São Paulo, Brazil, in a consensus
method, we decided whether each pixel more likely be-
longed to the heart or lungs. This analysis required that
the beginning of each EIT signal was synchronized with
the peak of the R-wave of the ECG signal, marking the
beginning of systole, i.e., ventricular contraction.
2.2.2. Wavelet Methods for Identification of Cardiac
Regions
From the qualitative analysis, it was assumed that a ty-
pical pixel within the card iac region has a positive varia-
tion in impedance during the first half of the cardiac cy-
cle. Thus, for a set of images of a complete cardiac cycle,
the pixel with the highest increase in impedance was
selected. This pixel was considered to be the best repre-
sentation of the cardiac region, and the analysis of all
other pixels was conducted relative to the reference pixel.
A wavelet transform was applied to the impedance signal
of the reference pixel using as wavelet-mother the gaus4
to obtain the space of coefficients in the plane disloca-
tion-scale. The values of these coefficients were normal-
ized to the interv al [0,1 ]. After normalization , a thresho ld
of 0.70 was applied to define a region in the disloca-
tion-scale space that reflected typical cardiac behavior.
This region was used as a mask in the dislocation-scale
space to compare all pixels with the reference pixel. For
each pixel, the same processes of the wavelet transform
and the normalization of the space coefficients were per-
formed. The reference mask was applied to each of the
displacement-scale spaces and a set of coefficient values
was selected. This set of coefficients was used to deter-
mine whether each pixel belonged in the cardiac region.
Using these values, it was possible to create a map of
parameters representing all pixels. In this study, two pos-
sible parameters were analyzed: the average values of the
coefficients of the mask (method-1); and the maximum
value of the coefficients of the mask (method-2). The
cardiac images were generated from a z-score normaliza-
tion on the parameter map. In this normalized space, a
threshold value for a pixel belonging to the cardiac re-
gion can be heurist i cal l y establ i shed.
In this case, a threshold value of 0.5 was used.
2.3. Model Evaluation
To evaluate its performance, the model was used to ana-
lyze EIT images from the animal experiment collected at
other values of PEEP. In this analysis, the cardiac regions
obtained by the saline injection method were used as a
reference for comparison, and the system performance
was evaluated using receiver operating characteristic
(ROC) curves. It is important to highlight that the saline
injection image is not the gold standard for imaging of
the heart region. When we compare the results of the
model with the saline data, we are only verifying how
well the model reproduces the contrast data. As saline
images are not feasible for human subjects, it was not
possible to evaluate the system using ROC curves.
Therefore, the EIT images of the heart region in humans
were qualitatively compared with X-ray CT images.
3. RESULTS
3.1. The Dynamics of the EIT Signal Carry
Information That Allows for Pixel
Characterization as Belonging to the
Cardiac Region
Through qualitative analysis of the EIT signal patterns, a
map was generated in which the pixels were grouped
based on their similarities (Figure 2). Tables 1 and 2
show the qualitative analysis, describe the characteristics
of each region and link them to their possible anatomical
regions. In Ta ble 1, pixel 214 shows a typical variation
in impedance of the ventricular region during the cardiac
cycle, and pixel 630 shows a typical variation of imped-
ance of a pulm o nary region.
Table 2 presents examples of pixels where the imped-
ance variation is not typical of either heart or lung re-
Figure 2. EIT Map with the regions found by qualitative analy-
sis of the wave patterns of a pig’s EIT signals.

H. Tanaka et al. / World Journal of Cardiovascular Diseases 3 (2013) 464-470 467
Table 1. Qualitative map and the characteristic EIT signals-regions A, B, C and D.
Regions Characteristic signal Analysis Pixel hypothesis
A - Region out of analysis. -
B - Pixels without significant impedance
variation (dZ < 0.1). -
C
Pixel 214
1-32: full cardiac cycle.
1-5: impedance decrease during the atrial systole.
5-6: isovolumetric contraction of systolic phase.
6-16: Ventricular systole.
6-11: systolic phase—rapid ejection.
11-15: systolic phase—reduced ejection.
15-16: systolic phase—isovolumetric relaxation.
16-32: diastolic phase.
16-22: diastolic phase rapid filling.
22-32: diastolic phase reduced filling.
Pixel of
cardiac region
(ventricle)
D
Pixel 630
The impedance variation is oppo site to that
observed in the previous region. This behavior is
expected in the pulmonary region because of the
pulmonary circulation: the blood flow that leaves
the right ventricle during the systolic phase reaches
the lungs.
Pixel of
pulmonary regio n
gions. Figure 3 shows the pig heart maps obtained using
the wavelet and saline contrast methods. Pixels corre-
sponding to the heart region are located in a superior
central position independent of the PEEP values.
3.2. The Maps Obtained Using the Cardiac
Wavelet Models Are Comparable to the
Maps Obtained by the Cardiac Saline
Method
For comparison of the wavelet heart maps with the image
of the saline cardiac region, ROC curves were elaborated
for each PEEP value (Figure 4). The average areas of the
ROC curves were 0.86 for method-1 and 0.90 for me-
thod-2, demonstrating that these methods were compara-
ble to the saline method.
3.3. Humans Images of the Cardiac Regions
Obtained by Methodology Proposed Were
Qualitatively Comparable with the Angio
CT images Figure 3. Pig heart maps obtained by the wavelet and saline
injection methods for each PEEP value, (white color indicates
he cardiac region). Figure 5 shows the heart maps of the first human (H1) t
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H. Tanaka et al. / World Journal of Cardiovascular Diseases 3 (2013) 464-470 468
Table 2. Qualitative map and the characteristic EIT signals-regions E, F and G.
Regions Characteristic signal Analysis Pixel hypothesis
E
Pixel 208
1-21: impedance variation similar to
systolic phase as in region C.
21-32: different pattern if compared to
regions C and D.
Pixel of
predominantly
cardiac region
F
Pixel 112
1-21: impedance variation similar to
systolic phase as in region C.
21-32: different pattern if compared to
regions C and D.
Pixel possibly of
cardiac region
G
Pixel 364
1-16: impedance variation similar to
systolic phase as in region D.
16-32 different pattern if compared to
regions C and D.
Pixel of
predominantly
pulmonary regio n
obtained using the wavelet methods and the Angio CT
images for three positions of the electro des belt. The two
wavelet methods were able to identify the cardiac region
regardless of the position of the electrode belt.
These regions are qualitatively consistent with the An-
gio CT mages. Figure 6 shows the heart maps of the
second human (H2) obtained using the wavelet methods
and the Angio CT images. Both methods were able to
identify the cardiac region regardless of the body posi-
tion. In this case, the images obtained using method-1
were more similar to the Angio CT images than those
obtained using m e t h od -2.
4. DISCUSSION
Two wavelet methods were developed to identify the
cardiac region of a pig submitted to three different values
of PEEP. Both methods demonstrated adequate quantita-
tive and qualitative results. The best results were ob-
tained at the pressure PEEP12, follo wed by PEEP18 and
finally by ZEEP. One possible hypothesis for this obser-
vation is that variations in the PEEP value may modify
the position of the band of thirty two electrodes in the
cranial-caudal direction, and therefore, the transverse
section that covers the heart. Based on this as sumption,
he pressure PEEP12 favored the identification of the t

H. Tanaka et al. / World Journal of Cardiovascular Diseases 3 (2013) 464-470 469
Figure 4. ROC curves obtained using the wavelet methods
compared with the pig heart region obtained through a saline
injection for each PEEP value.
Figure 5. Human (H1) heart maps obtained by the wavelet
methods for each position of the band of 32-electrodes and the
corresponding Angio CT images (white color indicates the car-
diac region).
cardiac region because extremes of PEEP can result in
the augmentation in pulmonary vascular resistance and
compromise of right ventricular function. The wavelet
methods were applied to two human EIT images and the
models were able to adequately id entify the heart in both
cases. These results are in accordance with the assump-
tion that the hearts of pigs and humans are very similar
anatomically and functionally [14].
Figure 6. Human (H2) heart maps obtained by the wavelet
methods for the two body positions of the subject and the cor-
responding Angio CT image (white color indicates the cardiac
region).
Comparing the two methods, lung perfusion imaging
was observed in images acquired with method-1 but not
in images obtained with method-2. The main difference
between the approaches is the choice of parameter map-
ping pixels. In the first method, the average value of the
coefficients was used, and in the second method, the
maximum value of the coefficients was used. Therefore,
a more pronounced difference between the parameters is
expected when using method-2, which produces a crisper
image. In conclusion, the wavelet method discussed here
is a feasible means of analysis for EIT imaging, which is
a research area to be explored. Methods presented in this
study may be used in two fields of analysis: 1) improve-
ment of EIT imaging by, for example, providing a priori
information for image reconstruction algorithms; and 2)
assisting in the development of systems for clinical ap-
plication such as the estimation of non-invasive cardiac
debt.
5. ACKNOWLEDGEMENTS
We would like to acknowledge all of the experts in the respiratory ICU
at the Clinics Hospital of São Paulo, Brazil who helped in the qualita-
tive analysis of the EIT images. This work was financially supported by
CNPq (484116/2007-0).
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