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This article was downloaded by: [Eindhoven Technical University]On: 19 October 2014, At: 23:06Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK
Instrumentation Science &TechnologyPublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/list20
Determination of FemoralArtery Occlusion UsingPrincipal Component Analysisof Doppler SignalsSadık Kara a & Prof. Semra Kemaloğlu b
a Erciyes University, Department of ElectronicsEngineering , Kayseri, Turkeyb Erciyes University, Department of BiomedicalDevices Technology , Kayseri, TurkeyPublished online: 16 Aug 2006.
To cite this article: Sadık Kara & Prof. Semra Kemaloğlu (2005) Determination ofFemoral Artery Occlusion Using Principal Component Analysis of Doppler Signals,Instrumentation Science & Technology, 33:3, 329-338, DOI: 10.1081/CI-200056144
To link to this article: http://dx.doi.org/10.1081/CI-200056144
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Determination of Femoral Artery OcclusionUsing Principal Component Analysis of
Doppler Signals
Sadık Kara
Erciyes University, Department of Electronics Engineering,
Kayseri, Turkey
Semra Kemaloglu
Erciyes University, Department of Biomedical Devices Technology,
Kayseri, Turkey
Abstract: The aim of this study is to scrutinize the ability of principal component
analysis (PCA) over power spectral densities (PSD) for common femoral artery
blood flow study. Doppler femoral artery signals of patients with occluded arteries and
of healthy subjects were recorded. Then, power spectral densities of these signals were
obtained using the Welch method. To clearly determine the difference between the
groups of occluded patients and healthy subjects, PCA was implemented with
patients and healthy matrices derived from PSD. The basic differences between
the healthy and occluded patients were acquired with 1st principal component. The
use of PCA of physiological waveforms is presented as a powerful method likely to
be incorporated into future medical signal processing.
Keywords: Femoral artery occlusion, Power spectral density, Principal component
analysis
INTRODUCTION
Doppler ultrasound is widely used as a non-invasive method for the assess-
ment of blood flow, both in the central and peripheral circulation. It may be
Address correspondence to Prof. Sadık Kara, Erciyes University, Department of
Electronics Engineering, Biomedical Eng. Group, 38039 Kayseri, Turkey. E-mail:
kara@erciyes.edu.tr
Instrumentation Science and Technology, 33: 329–338, 2005
Copyright # Taylor & Francis, Inc.
ISSN 1073-9149 print/1525-6030 online
DOI: 10.1081/CI-200056144
329
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used to estimate blood flow, to image regions of blood flow, and to locate sites
of arterial disease, as well as flow characteristics and resistance of common
femoral arteries.[1–4]
Doppler systems are based on the principle that ultrasound, emitted by an
ultrasonic transducer, is returned partially towards the transducer by the
moving targets, thereby inducing a shift in frequency proportional to the
emitted frequency and the velocity along the ultrasound beam. The results
of the studies in the literature have shown that Doppler ultrasound evaluation
can give reliable information on both systolic and diastolic blood velocities of
arteries and have supported that Doppler ultrasound is useful in screening
certain hemodynamic alterations in arteries.[1–4]
In recent years, color Doppler ultrasonography (CDU) has found increa-
sing use in the assessment of vascular disease in lower limb circulation,
displaying high sensitivities when compared to arteriography.[5–7] CDU is
relatively cheap, quick, non-invasive, and safe, whereas angiography carries
significant risks of morbidity and mortality.[5]
Doppler ultrasound is usually the first-line investigation of lower limb
arterial disease and, thus, identifies patients with treatable peripheral vascular
disease.
The accuracy of the interpretation of any changes present in the shape of
the common femoral arterial (CFA) waveform becomes all the more vital,
either in confidently excluding the presence of significant proximal disease
(and, thus, ensuring that the patient does not undergo an unnecessary
angiogram with its concomitant risks)[8] or in identifying and assessing any
proximal disease and, thus, indicating further investigation and/or treatment.
Spectral analysis and signal decomposition continue to find wide use in a
multitude of biomedical signal processing applications. Spectral analysis of the
Doppler signals produces information concerning the blood flow in the arteries.
The maximum frequency envelope from the Doppler waveforms obtained
from the common femoral artery retrospectively are analyzed using a mathe-
matical feature extraction technique, i.e., principal component analysis (PCA).
The results were compared with the arteriographic findings and showed that
PCA represents a significant improvement in diagnostic accuracy when
compared with other techniques.[9]
Guo et al. compared time-frequency distributions of femoral artery
Doppler signals using various methods. The results showed that the Bessel
distribution performed the best, but the Choi-Williams distribution and auto-
regressive modeling are also techniques which can generate good time-
frequency distributions of Doppler signals.[10]
Eiberg et al. studied common femoral Doppler waveforms and demon-
strated that only three simple waveform characteristics belonging to common
femoral arteries were necessary in order to characterize the waveform as
normal or abnormal and to predict upstream aorto-iliac disease.[11]
Wright and Gough applied artificial neural networks (ANNs) to the
problem of the diagnosis of aorto-iliac arterial disease on the basis of the
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profile of the common femoral artery (CFA) Doppler flow velocity waveform.
Thus, they demonstrated the ability of an ANN to identify the severity of
aorto-iliac disease from the CFA waveform.[12]
Smith et al. compared network classification results with a Bayesian
classifier following a principal component analysis of the waveforms.[13]
Several authors have reported good results using, during the past decades,
the diagnostic performance of ultrasound complex analysis of the common
femoral waveform.[14,15] However, none of these methods have gained wide-
spread use, mainly because of their complexity and need for additional
equipment.
Nevertheless, subjective visual examination of the CFA waveform profile
has a potential for inter- and intra-observer variability. To overcome this,
several methods of numerical analysis have been explored. Of these, the
most straightforward are the waveform pro-file indices, such as the pulsatility
index (PI)[16] and the Pourcelot or resistance index (RI).[17] More sophisticated
methods have also been developed, such as the Laplace transform[18] and
principal components analysis (PCA).[19] But, neither the simple nor the
more complex analytical techniques have yielded an acceptable diagnostic
accuracy to make them commonplace in the vascular clinic.
The aim of this study was to apply and evaluate a principal component
analysis method to power spectral density acquired with Welch’s averaged,
modified periodogram of femoral artery Doppler signals, because more
reliable method was revealed to diagnose femoral artery congestion.
EXPERIMENTAL
Subjects
In this study, common femoral artery Doppler signals were obtained from 54
subjects. The group consisted of 22 females and 32 males, with ages ranging
from 20 to 65 years and mean age 36.5 years (standard deviation, SD ¼ 9.1).
Color Doppler Ultrasound was used during examinations and sonograms were
taken under study. According to examination results, 30 of 54 subjects
suffered from femoral artery occlusion and the rest were healthy subjects.
The group having femoral artery occlusion consisted of 12 females and 18
males, with a mean age 37.5 years (SD ¼ 7.8, range 22–65) and the
healthy subjects consisted of 10 females and 14 males with a mean age 34.5
years (SD ¼ 8.0, range 20–60).
Measurement of Common Femoral Artery Doppler Signals
Flow signals from common femoral arteries were recorded via a color Doppler
Ultrasound unit (Toshiba PowerVision 6000, Osaka, Japan). A linear
Determination of Femoral Artery Occlusion Using PCA 331
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ultrasound probe of 10MHz was used to transmit pulsed ultrasound signals to
arterials. The subjects were lying supine and breathing naturally during the
data sampling. In all recordings, the insonation angle and the presetting of
the ultrasound were kept constant. The ultrasonic transducer was applied,
on a horizontal plane, to the inguinal using a water-soluble gel as a
coupling agent. Care was taken not to apply pressure to the inguinal in
order to avoid artifacts. The insonation angle was adjusted, via electronic
steering methods, and manually, in order to keep a constant value of 45
degrees on a longitudinal view. The amplification gain was carefully set to
obtain a clean spectral output with minimal background noise on the
spectral display. The audio output of ultrasound unit was sampled at
44,100Hz and then sent to a PC via an input-output card.[20] Thus, the
system hardware was composed of Digital Doppler Ultrasound unit that can
operate in the pulsed mode, a linear ultrasound probe, an input-output card,
and a personal computer (PC) which was used for storage, display, and
spectral analysis of the acquired data (Figure 1).
Welch Method for Spectral Analysis
The FFT based Welch method is defined as a classical (nonparametric)
method. In the Welch method, signals are divided into overlapping
segments; each data segment is windowed. Thus, the segments can be
defined as
xmðnÞ ¼ xðnþ mDÞ n ¼ 0; 1; . . . ; L� 1 m ¼ 0; 1; . . . ; K � 1 ð1Þ
where D is the size of the segments and D = L. According to this segmen-
tation, A second modification is made, which is to window data segments prior
to computing the periodogram.[21] As a result
PðmÞPERð f Þ ¼
1
LU
XL�1
n¼0
xmðnÞwðnÞ expð�j2pfnÞ
�����
�����
2
ð2Þ
Figure 1. Block diagram of the system hardware used to acquire Doppler data.
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where
U ¼1
M
XM�1
n¼0
w2ðnÞ ð3Þ
and the Welch spectrum estimate is the average of these modified periodo-
grams,
PW ð f Þ ¼1
K
XK�1
m¼0
PðmÞW ð f Þ ð4Þ
In this study, X is divided with 50% overlap, each section is windowed
with a Hamming window, and modified periodograms are computed and
averaged.
Principal Component Analysis for Doppler Spectral Waveforms
Principal Component Analysis (PCA) is a way of identifying patterns in data,
and expressing the data in such a way as to highlight their similarities and their
differences. Since patterns among data can be hard to find for high dimen-
sional data, where the luxury of graphical representation is not available,
PCA is a powerful tool for analyzing the data. The other main advantage of
PCA is that, once you have found these patterns in the data, you can
compress the data, ie., by reducing the number of dimensions, without
much loss of information.[22]
In this paper, the use of PCA for the characterization and interpretation of
physiological signals was explained. Principal components are calculated
using eigenvectors and eigenvalues of covariance matrices or correlation
matrix.
CovðXijÞ ¼1
n� 1
Xn
k¼1
ðXik � �XiÞðXjk � �X jÞ ð5Þ
n: total number of signals (30 for patients, 24 for healthy), i ¼ 1, 2, . . . ,m,j ¼ 1, 2, . . . ,m (m signal dimension). Xi is the average signal of the
population.
First, three principal components were computed by the solution of
Cwp ¼ lpwp p ¼ 1; 2; 3: ð6Þ
C, covariance matrix, wp, pth principal component (eigenvector), and lp is
the corresponding eigenvalue. The lp’s are positive values which are
proportional to the fraction of the total variance accounted for by each
component wp which has the important property of forming an orthogonal set.
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The coefficients of the principal components of qth signal are then
given by
aqp ¼Xm
i¼1
xqiwip p ¼ 1; 2; 3: q ¼ 1; 2; . . . ; n: ð7Þ
Data matrices were formed from the power spectral densities of femoral
artery Doppler signals belonging to patients and healthy subjects, separately.
PCA was applied to these matrices. Matlab 6.5 was used for signal processing
and power spectral density procedures, and SAS 9.1.2 data analysis software
was used for the PCA.
Although only the first three components have been computed, the first
component indicate that 97.9 and 98.2 percent of the total variance of X is
explained by this reduced set of components for patients and healthy
subjects, respectively, and therefore, only minimal information is lost by
ignoring higher order components.
RESULTS AND DISCUSSION
The demonstration of Doppler shift frequency magnitude on a time domain
belonging to femoral artery occlusion patients and healthy persons is given
in Figure 2. In this figure, the signals represented in time domain seem
similar to each other and there is no obvious difference between them.
Figure 3 shows power spectral density graphics of a femoral artery
occlusion patient (patient, no. 28) and healthy person (healthy, no. 19). In
the graphics of patients who have femoral artery occlusion; the blood
velocity falls sharply from maximum to minimum. Conversely, the graphics
Figure 2. Time domain graphics of common femoral artery Doppler signals.
(a) Healthy person (healthy, no. 19); (b) patient with occlusion (patient, no. 28).
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of healthy subjects have stepped fall characteristics and a tendency to form
a second peak.
To determine the difference between the groups of patient and healthy
subjects, PCA was implemented with patient and healthy matrices derived
from PSD. Afterwards, the principal components of Doppler ultrasound for
the femoral artery was analyzed. While the 1st principal component of
the data matrix acquired from patients represents 97.6% total variance, the
1st principal component of the healthy data matrix represents 98.2% total
variance.
Therefore, the 1st principal component clearly shows the basic differ-
ence between the healthy people and occluded patients. While the
Figure 3. Power Spectral Density graphics of common femoral artery Doppler sig-
nals. (a) Healthy person (healthy, no. 19); (b) patient with occlusion (patient, no. 28).
Figure 4. Healthy and patient subjects’ first two principal component graphics vs.
time axis. (a) 1st principal component; (b) 2nd principal component.
Determination of Femoral Artery Occlusion Using PCA 335
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standard deviation of the 1st principal component of healthy subjects is
0.0149, the patient subjects have 0.0303 standard deviation in the 1st
principal component. First and second principal components are depicted
in Figure 4. Second and later principal components exhibited appearances
similar to each other for healthy and patient subjects.
The graphic of the 2nd principal component vs. 1st principal component
of healthy subjects and patient subjects is given in Figure 5. As a result,
the patient and healthy groups are separated clearly from each other.
CONCLUSION
In this paper, the application of PCA to the spectral waveform of Doppler
signals of femoral arteries belonging to patients with occlusion and healthy
subjects was presented. The capability of PCA over PSD was examined in
terms of distinguishing between patients with occlusion and healthy
subjects. The basic differences between the healthy and occluded patients
were observed with the 1st principal component. The use of PCA of the
physiological waveform is presented as a powerful method likely to be
incorporated into future medical signal processing.
Figure 5. Graphics of 2nd principal component vs. 1st principal component of
healthy subjects and patient subjects.
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ACKNOWLEDGMENT
This project was supported as Post-Graduate Education and Research Project
by Erciyes University (Project no. FBT-04-25).
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Received October 25, 2004
Accepted November 15, 2004
Manuscript 1492
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