Download Clarifying the Physiological Processes behind Foucault Cardiogram

Conference “Biomedical Engineering“
Clarifying the Physiological Processes behind Foucault
Cardiogram Waveform
G. Savustjan1, J. Vedru2
Institute of Physics, University of Tartu, Estonia
1
E-mail: [email protected]
2
E-mail: [email protected]
Introduction. Foucault cardiography [3] is an induction method for
measurement and recording electrical bioimpedance, which is used to probe the heart
with eddy or Foucault currents induced in the cardiac area by high-frequency magnetic
fields. Correspondingly, the signal considered in this paper was named Foucault
cardiogram (FouCG). It has been shown that the technique is safe for conducting
investigations on humans [3].
Spatial redistribution of impedance due to contraction and dilation of the heart is
considered to be a mechanism of the FouCG origination. The FouCG waveform,
recorded with a single-coil sensor positioned with its centre slightly below the apexbeat, is promisingly similar [5] to ventricular volume curves typically found in medical
books. To clarify the physiological processes behind the FouCG waveform, we tried to
compare it with MRI, which is considered to be highly accurate method for obtaining
ventricular volume-time curves.
Methods. Cardiac cine MRI was performed on a healthy male volunteer (26
years old, height 185 cm, weight 94 kg) with 1.5 tesla Signa HDe (GE Healthcare,
UK) at West Tallinn Central Hospital. Images were acquired at supine position using
ECG gating. 18 short-axis slices, encompassing whole heart from the base to the apex
were produced with the slice thickness of 10 mm and spatial resolution of 1.44 mm. 20
frames per a cardiac cycle, followed the R-wave with 44 ms time step, were recorded
for each slice,. It took us 40 minutes to perform the MRI recording, which yielded 360
DICOM images in total.
Images were processed with MATLAB. High-frequency noise was removed from
images by using 2D median filter. Then we implemented special algorithm for locating
the heart on every frame of each slice [1]. The ventricular contours were determined
using MATLAB implementation of Chan-Vese active contours algorithm [6] extended
to work with 4D image data. After coarse contours detection, we applied selfdeveloped correction algorithm, to adjust artefacts appearing due to high intensity of
pixels surrounding the heart area. To assess the quality of detected contours, movie
showing the frames of each slice of the ventricles with the detected contour has been
produced. Finally, the algorithm separated the left and right ventricle contours from
each other. Area of the left ventricle SLV in a particular slice was counted from the
number of pixels inside the contour cLV and the area corresponding to a pixel Spix.
Ventricular volume-time curve was calculated by adding up volumes of the cylinders
formed thus in all slices:
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Conference “Biomedical Engineering“
N
V LV = ∑ S LV d ,
n=1
n
(1)
where N is the number of slices and d is their thickness. Volume-time curve of the
right ventricle was calculated in the same manner.
The FouCG was recorded from the same volunteer by the means of Foucault
cardiograph described in [3], 20 minutes after MRI procedures. The frequency of
probing eddy currents was 7.7 MHz; the diameter of single-turned inductor coil was
135 mm; mean amplitude of the voltage on inductor was 1.1 V and the corresponding
current 0.1 A. Based on volunteer's internal topography obtained during the MRI
scanning (see Fig.1), we positioned the Foucault inductor slightly below the apex-beat
point. Trying to preserve patient's state similar to the one obtained during MRI scan,
we recorded the FouCG and ECG (Einthoven's lead II) simultaneously for
120 seconds.
Fig. 1. Position of the Foucault inductor
The recorded raw FouCG signal was processed using algorithm based on fast
Fourier transform. Special filter has been constructed to pass the frequencies higher
than 0.90 Hz, reject 50 Hz and 100 Hz bands [4]. As the result, all main types of
noises as well as the respiration influence were suppressed. Ensemble-averaged
waveform of the FouCG [2] was calculated.
Results and discussion. It is known that FouCG lacks of calibration against
volume units and has a constant arbitrary bias of the signal in Y-axis [5]. Still we have
compared the ensemble-averaged waveform obtained from the FouCG with the
volumetric curves of the left (LV) and right (RV) ventricles on the assumption that the
MRI was a precise method and could be considered as the standard (Fig. 2). In the
process of comparison, the extents of the curves were normalized to 1 and couples of
the FouCG and MRI signal cycles matched at the centre of the R-wave of the ECG
were formed. Due to a small difference of heart periods, the MRI and FouCG curves
showed slight desynchronization with the increase of time distance from the matching
point. Similarity index [2] of value 0.86 was obtained for the LV volume (Fig. 2a). It
is noticeable, that FouCG reflects isovolumic contraction by nearly horizontal curve
behaviour during the QRS complex. Both curves show great similarity during the
systolic phase. The FouCG curve exhibits small oscillation at the beginning of T-
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Conference “Biomedical Engineering“
wave, which is a deviation from the LV volume. Both curves reach minima close to
the end of systole, showing finished ejection of blood from the ventricle. During the
early diastole, when the ventricular volume increases, the FouCG signal grows with
the same rate as the MRI curve. But a quite noticeable difference appears at the Pwave later on, allowing us to conclude that the FouCG signal does not reflect the enddiastolic behaviour accurately enough.
Fig. 2. Comparison of the FouCG (gray) with MRI ventricular volume-time curves (black).
ECG shown as reference. a) LV, b) RV, c) sum of LV and RV.
It is reasonable to assume that both ventricular components (due to LV and RV)
are present in the FouCG. Still, it turns out that the LV influence is much greater than
the RV one.
As for the RV volume (see Fig. 2b), its similarity with the FouCG equals to 0.52,
showing the lowest similarity yet. Again, the FouCG curve reflects systolic behaviour
rather accurately, but when diastole starts, a significant deviation can be seen which
reduces only at the beginning of the Q-wave.
Similarity index equal to 0.76 was obtained from the comparison of the sum of
the both MRI curves (LV+RV, i.e. at equal contributions by the both) with the FouCG
signal (see Fig. 2c). Thus, the contribution of the RV into FouCG ought to be less than
50%, because similarity has rather greatly decreased when 50% contribution of RV
has been introduced..
The reason for the discussed features might be in disposition of ventricles in the
thorax while the eddy currents weaken rapidly with the distance from the inductor.
Fig. 1 shows that the LV occupies greater area in the frontal image than the RV. The
RV being more distanced from the inductor influences the FouCG signal less than the
LV. It is possible that FouCG cannot reflect the diastolic process accurately. We can
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Conference “Biomedical Engineering“
suggest a hypothesis, that the diastolic FouCG signal gets distorted because the device
senses not only changes of blood content in the heart, but also its displacements during
the cardiac cycle. The MRI curve does not suffer from these displacements since they
were eliminated at the image segmentation stage. Still the FouCG signal does depict
diastolic phase by additional increase in its value, allowing us to conclude that the
signal reacts to the ventricular blood inflow together with a possible displacement of
the heart towards the transducer.
Algorithm for automatic segmentation of MRI images implemented in MATLAB
showed itself to be reliable and robust. Overall similarity between manually and
automatically segmented images resulted in 0.94.
Conclusions and future work. Our study showed that FouCG waveform
resembles similarity with ventricular blood content in ejection and filling process. We
have observed deviation of FouCG from the corresponding MRI curves during diastole
and attempted to explain it. Still it is likely that FouCG enables monitoring of systolic
ventricular hemodynamics. Tracking relative variation of stroke volume and
associated quantities should be possible.
For a future work, we suggest to study cardiac displacements by MRI or 3D
ultrasound techniques in order to clarify their affects on the FouCG signal. The
calibration of the FouCG signal in volume units also remains as a hard problem of
great importance to be solved in the future.
References
1. Savustjan G. Comparison of Foucault Cardiogram with MRI Ventricular Volume-time
Curve // Master's thesis, Univ. of Tartu, 2010.
2. Vedru J., Makarova O., Tina V. Averaged Waveforms of Electrical Bioimpedance:
Construction and Comparison // Proc. XI Int. Conf. Electrical Bio-impedance, Oslo, 2001. – P.
631–635.
3. Trolla J., Vedru J. On the Safety of Foucault Cardiography // Proc. XI Int. Conf.
Electrical Bio-impedance, Oslo, Norway, 2001. – P. 649–652.
4. Savustjan G. Separation of Ventricular Rate from the Heart Rate in Foucault
Cardiogram // Bachelor's thesis, Univ. of Tartu, 2005.
5. Ovetchko N., Tchironova N., Ollik R., Kivimäe R., Vedru J. Ascertaining the link
between the heart motion and Foucault cardiogram // Proc. Int. Conf. Biomed. Engineering,
Kaunas, Oct. 2005, – Kaunas, 2005. – P. 115-118.
6. Chan T., Vese L. Active Contours without Edges // IEEE Trans. Image Processing,
2001, Vol. 10, No. 2.– P. 266–277.
Clarifying the physiological processes behind Foucault cardiogram waveform
G. Savustjan, J. Vedru
Institute of Physics, University of Tartu, Estonia
Monitoring of mechanical parameters of the heart provides essential information to medical doctors for
patient health assessment. There is a lack of cheap and robust method for continuous monitoring of these
parameters. Currently used methods do allow neither automation nor continuous monitoring without skilled
operators. Purpose of the current research is to clarify physiological processes behind FouCG origination, by
comparing its signal with volume-time curve, obtained from segmented cardiac MRI images.
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