Evaluation of the carotid artery using wavelet-based analysis of the pulse wave signal

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 2, April 2022, pp. 1456~1467
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i2.pp1456-1467  1456
Journal homepage: http://ijece.iaescore.com
Evaluation of the carotid artery using wavelet-based analysis of
the pulse wave signal
Sameh El-Sharo1
, Amani Al-Ghraibah2
, Jamal Al-Nabulsi2
, Mustafa Muhammad Matalgah3
1
Jordan Society for Scientific Research, Innovation and Creativity, Amman, Jordan
2
Department of Medical Engineering, Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan
3
Department of Electrical and Computer Engineering, School of Engineering, University of Mississippi, Oxford, United States
Article Info ABSTRACT
Article history:
Received Oct 18, 2020
Revised Jul 17, 2021
Accepted Aug 14, 2021
The use of pulse wave analysis may assist cardiologists in diagnosing patients
with vascular diseases. However, it is not common in clinical practice to
interpret and analyze pulse wave data and utilize them to detect the
abnormalities of the signal. This paper presents a novel approach to the
clinical application of pulse waveform analysis using the wavelet technique
by decomposing the normal and pathology signal into many levels. The
discrete wavelet transform (DWT) decomposes the carotid arterial pulse wave
(CAPW) signal, and the continuous wavelet transform (CWT) creates images
of the decomposed signal. The wavelet analysis technique in this work aims
to strengthen the medical benefits of the pulse wave. The obtained results
show a clear difference between the signal and the images of the arterial
pathologies in comparison with normal ones. The certain distinct that were
achieved are promising but further improvement may be required in the future.
Keywords:
Arterial pathologies evaluation
Carotid artery
Pulse waveform
Wavelet analysis
This is an open access article under the CC BY-SA license.
Corresponding Author:
Amani Al-Ghraibah
Department of Medical Engineering, Al-Ahliyya Amman University
Amman, Jordan
Email: a.ghraibah@ammanu.edu.jo
1. INTRODUCTION
Pulse wave embeds useful information about human health in general and the cardiovascular system
in particular. Palpation of the pulse waves assesses some pathologies, which has, since antiquity, been one of
the tools in medicine. Mahomed, established the foundation of pulse wave analysis in a short medical lifetime
from 1872 to 1884 [1]–[3]. In 1872, he described the normal radial pressure and carotid waveforms and showed
the differences between them. He also illustrated the differences between hypertension and chronic nephritis
in [1]. Furthermore, he described the effects of arterial degeneration with ageing on the arterial pulse [2], [3].
Real-time recordings of the pulse waveform, referred to as sphygmography, had been well investigated in
medical literature and used to describe heart block, hypertension, and other pathological conditions in addition
to the effects of anti-anginal drugs [4]. Interest in pulse wave methodology had declined in the period of first
half of the last century but has recently been revived with the introduction of computers technology in modern
recording instrumentation. The introduction of new high-tech computing methods allowed for more accurate
recordings and analyses of pulse waves in various parts of the vascular system, taking into consideration that
pulse wave analysis is non-invasive, simple, and with apparent sensitivity [5]–[7]. Obtained pulse waveforms
provide cardiologists with valuable information about the morphological structure and health state of the
corresponding blood vessel. This type of information can be further enhanced by using different analysis tools;
mathematical and computational [8], [9]. Recent clinical studies shown that pulse waveform analysis can help
in the diagnosis of several vascular diseases such as arterial stiffness and its development with aging, which is
considered as one of the most cardiovascular risk factors that may lead to premature death [10], [11]. Several

Int J Elec & Comp Eng ISSN: 2088-8708 
Evaluation of the carotid artery using wavelet-based analysis of the pulse wave signal (Sameh El-Sharo)
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analysis methods have been proposed in literature for extracting specific useful information from pulse wave
signals. These methods include the pulse waveform analysis, Fourier and Wavelet techniques, and pulse wave
imaging methods were used to investigate the cross-sectional relationship between periodontal status and
arterial stiffness [12]. The amplitude parameters of the pulse in the time domain give a good indication of the
left ventricular ejection function and cardiac output [13]. The frequency domain method was also used to
analyze the aortic impedance, which is the relationship between pulsatile pressure and flow in the aorta. This
phenomenon was well-addressed by McDonald in [14] on the basis of wave reflection. Transfer functions were
introduced to characterize properties of vascular beds in the frequency domain, and the validity of linearity
assumption in the arterial tree were also established in [14], [15].
Wavelet transform became a point of interest technique in the medical-related studies especially in
biomedical engineering as early as 1990s (see e.g., [16]–[19]). This is motivated by its ability of detecting and
analyzing important features of the signals, which could not be easily revealed in time-domain, in order to
understand or model physiological systems [20]. Wavelet transform allows complex information in the signals
and patterns to be decomposed into simple forms at different positions and scales and consequently
reconstructed with high precision. Wavelet transform of a function is the enhanced version of Fourier
transform, where the Fourier transform is a powerful tool in analyzing the components of a stationary signal.
But it is failed for analyzing the non-stationary signal whereas wavelet transform allows the components of a
non-stationary signal to be analyzed. Another important advantage of wavelet transform in comparison with
the other signal analysis methods is that it can detect and analyze abrupt changes in signals [21]. To the best of
our knowledge, analyzing carotid arterial pulse wave (CAPW) signal using discrete wavelet transform (DWT)
and continuous wavelet transform (CWT) has not been reported widely in literature. In this paper, we consider
pulse wave signals obtained from the carotid artery for different cardiovascular pathologies such as the cardiac
failure, arteriosclerosis, and hypertension [22], [23] and develop an analysis framework based on the DWT and
CWT. The wavelet analysis technique presented in this paper is presented to strengthen the medical benefits
of the pulse wave signal when analyzed in new dimensions of the wavelet transforms.
The main contribution of this paper is to create a fast-automated diagnosis method that helps
physicians in decision making process as applied to the arterial pulse signal condition whether it is normal or
abnormal using both the discrete and continuous wavelet analyses. The DWT decomposes the CAPW signal
into approximate and details components that reveal hidden information which cannot be observed in the time-
domain CAPW signal, while the CWT creates a 3D surface and contour images that reveal other types of
information in the time-scale domain. The proposed technique is a novel addition to the medical diagnosis
processes as the abnormality of the CAPW can be noticed faster and easier than the conventional diagnosis
processes. Comparisons between the normal and the abnormal CAPW signals are discussed after applying the
proposed system. Furthermore, differences in characteristics between different types of abnormal CAPW
signals could also be easily distinguished. The remainder of the paper is organized as follows: methods of the
analysis are presented in section 2. In section 3, the results and discussion are included, and finally section 4
concluded the main idea of this work.
2. METHOD
Wavelet analysis can be used as a mathematical tool to extract hidden information from different types
of data such as audio signals and images that are not obvious from their time-domain representations. Sets of
wavelets are generally needed to analyze data entirely. A set of "complementary" wavelets will decompose
data without gaps or overlap so that the decomposition process is mathematically reversible [24]–[27]. In this
paper, we use the well-known “Daubechies” wavelets (with ten levels of decomposition) to analyze CAPW
signals.
2.1. Discrete wavelet transform (DWT)
Using DWT, the CAPW signal is decomposed into N levels using DWT, where N must be a strictly
positive integer chosen to be ten levels in this paper as many trials were done at different levels starting from
5 levels to 13 levels, but ten levels gave the best results. In the first step of the DWT-based analysis, the DWT
of the arterial signal 𝑠(𝑡) produces two sets of coefficients: approximation coefficients cA1, and detail
coefficients cD1. These vectors are obtained by convolving the signal s with the low-pass filter Lo_D for
approximation, and with the high-pass filter Hi_D for detail, followed by dyadic decimation (down sampling)
as shown in the block diagram in Figure 1, where the length of each filter is equal to 2N [28]. The next step
splits the approximation coefficients cA1 into two parts following same scheme in the first step by replacing s
by cA1, and hence producing cA2 and cD2; and steps continue as such N times. Following these steps, the
wavelet decomposition of the arterial signal s (analyzed at level N=10) results in the structure: [cA10, cD10,
..., cD1] as shown in Figure 2. Throughout the remaining parts of the paper, we create explanation plots of the
reconstructed approximate and details signals for normal as well as 4 cases of abnormal CAPW signals.

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Int J Elec & Comp Eng, Vol. 12, No. 2, April 2022: 1456-1467
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Figure 1. The first step of DWT
Figure 2. The general structure of DWT of 10 levels
2.2. Continuous wavelet transform
Besides the DWT, the CWT is another method that decomposes the CAPW signal into time-shifted
scaled basis functions (i.e., wavelets). The CWT is a tool that offers an over-complete representation of a signal
by allowing the translation and scale parameter of the wavelets vary continuously [29]. The CWT, of the arterial
signal s(t) at a scale and translational value b is expressed by the integral.
Xw(a,b)=
1
√Ι𝑎I
∫ 𝑠(𝑡)
∞
−∞
(
𝑡−𝑏
𝑎
) dt (1)
Where  (t)is a continuous function in both time and frequency domains called the mother wavelet and the
over bar ( ) denotes the complex conjugate. The main purpose of the mother wavelet is to provide a source
function to create different daughter wavelets for different b and a parameters, which are simply the translated
and scaled versions of the mother wavelet. CWT has shown effective capabilities in determining the damping
ratio of oscillating signals (e.g., identification of damping in dynamic systems) and robust resistance to the
noise embedded in the signal [29], [30]. The output after CWT decomposition is the real matrix XW using the
wavelet type “Daubechies”. The results are interpreted using 3D surface and contour plots representations for
CAPW signals under study in this paper. The contour and 3D plots technically include same information;
however, weak peaks that might be hidden between large peaks in 3D plots can be easily seen in a contour
plot.
2.3. CAPW signals decomposition
A normal CAPW of a healthy person is represented by a number of piecewise continuous functions
over the pulse cycle period [31] and plotted using MATLAB simulation shown in Figure 3(a). Similarly,
CAPWs of four different states of pathology are shown in Figures 3(b)-(e) which also plotted using MATLAB
software with the reference of the pathological signals found in literature [32]. To make it easy to understand
the signals abnormality, they are described by the following sentences: Figure 3(b) is a CAPW with large and
bounding pulses, Figure 3(c) is a CAPW with small and week pulses, Figure 3(d) is a CAPW signal with free
dicrotic notch, and Figure 3(e) is a CAPW signal with flat dicrotic notch [32].

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(a) (b)
(c) (d)
(e)
Figure 3. CAPW signals of normal and 4 pathology cases, (a) normal CAPW signal, (b) CAPW with large
and bounding pulses, (c) CAPW with small and week pulses, (d) CAPW with free dicrotic notch, and
(e) CAPW with flat dicrotic notch
To make use of the indicated up CAPW signals in this work, we reconstructed them based on the
wavelet decomposition structure created in the decomposition step. Figure 4 presents the reconstructed CAPW
signals, using the 3D approximation reconstruction at ten decomposition levels, and time duration of 50 ms.
Figure 4(a) is the normal CAPW signal, Figures 4(b) and 4(c) show the CAPW with large and bounding pulses,
and CAPW with small and week pulses, respectively. Figures 4(d) represents the CAPW with free dicrotic
notch and Figures 4(e) is the CAPW with flat dicrotic notch. This time is chosen because the mean delay of
the first and second carotid arterial sound after aortic valve opening and closer is 43+/-6 ms and 43+/-7 ms,
respectively.

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(a) (b)
(c) (d)
(e)
Figure 4. 3D approximation reconstructions of the CAPW signals, (a) normal CAPW signal, (b) CAPW with
large and bounding pulses, (c) CAPW with small and week pulses, (d) CAPW with free dicrotic notch, and
Figure 5 shows the details reconstruction at the same levels and time duration. Figure 5(a) shows the
normal CAPW signal, while Figures 5(b) and 5(c) show the CAPW with large and bounding pulses and CAPW
with small and week pulses, respectively. Figure 5(d) is the CAPW with free dicrotic notch, and Figure 5(e) is
the CAPW with flat dicrotic notch. The contour of approximation and the contour of details are given in
Figures 6 where Figure 6(a) is the normal CAPW signal. Figures 6(b) and 6(c) shows the CAPW with large
and bounding pulses and CAPW with small and week pulses, respectively. On the other hand, Figures 6(d) and
6(e) are the CAPW with free dicrotic notch, and CAPW with flat dicrotic notch, respectively. Moreover, the
contour of details are given in Figure 7, where Figure 7(a) shows the normal CAPW signal, and Figures 7(b)
and 7(c) are the contour of the CAPW with large and bounding pulses and CAPW with small and week pulses,
respectively. While Figures 7(d) and 7(e) are the CAPW with free dicrotic notch, and CAPW with flat dicrotic

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notch, respectively. Figure 8 (see in appendix) is the CWT of studied CAPW signals, where contour plots are
shown on the left sub-figures, and 3D surface plots with scale versus time in the right sub-figures. Figure 8(a)
shows the Normal CAPW signal, where Figures 8(b) and 8(c) are for CAPW with large and bounding pulses
and for CAPW with small and week pulses, respectively. Figures 8(d) and 8(e) are for the CAPW with free
dicrotic notch, and the CAPW with flat dicrotic notch, respectively. These plots help physicians to decide if
the recorded CAPW signal of the patient is normal or has a particular pathology by comparison among these
images as will be discussed in the sequel. Also, further researches could be done by investigating features from
the resultant images and use the extracted features to classify the input signal as normal or pathology signals
using an automated classification system.
(a) (b)
(c) (d)
(e)
Figure 5. 3D details reconstructions of the CAPW signals, (a) normal CAPW signal, (b) CAPW with large
and bounding pulses, (c) CAPW with small and week pulses, (d) CAPW with free dicrotic notch, and

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(a) (b)
(c) (d)
(e)
Figure 6. The contour of approximation of CAPW signals, (a) normal CAPW signal, (b) CAPW with
large and bounding pulses, (c) CAPW with small and week pulses, (d) CAPW with free dicrotic notch,
and (e) CAPW with flat dicrotic notch

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(a) (b)
(c) (d)
(e)
Figure 7. The contour of details of CAPW signals, (a) normal CAPW signal, (b) CAPW with large and
bounding pulses, (c) CAPW with small and week pulses, (d) CAPW with free dicrotic notch, and (e)
CAPW with flat dicrotic notch

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3. RESULTS AND DISCUSSION
To show the diagnostic merit of the proposed method, four different abnormal arterial pulse signals
are studied and decomposed using DWT and CWT. A software system using MATLAB is built to find the
difference between the normal and abnormal CAPW. In this section, comparisons between the normal and the
abnormal CAPW signals after applying the proposed system are summarized at the end of each subsection
below.
3.1. CAPW signal with large and bounding pulses
One of the abnormal CAPW signals studied is the one with large and bounding pulse due to
vasodilatation; this signal is shown in Figure 3(b). It represents some pathologies like bradycardia, heart block,
and atherosclerosis. It is characterized by amplitude larger than the normal signal seen in Figure 3(a).
Figures 4(b) and 5(b) show the approximation and details reconstruction respectively, after the DWT of this
signal, comparing this case with the normal one Figure 4(a) and 5(a), it is clear that there is an obvious
difference between them, the approximation curve Figure 4(b) has larger amplitude range, while the details
curve Figure 5(b) is not flat and has many peaks more than the normal curve. The contour plots of the
approximation and details are shown in Figures 6(b) and 7(b) respectively, the plots show that the dark areas
in the middle are wider and more complicated than the contour plot of the normal signal as seen in Figures 6(a)
and 7(a). Same conclusion can also be derived by performing the CWT of this large and bounding signal where
the obtained result is shown in Figure 8(b). The comparison of this outcome with that of the normal plot in
Figure 8(a) reveals obvious differences between them where the small peak that appears in middle between the
two strong peaks that was apparent in the normal CAPW case disappears in the abnormal case.
3.2. CAPW signal with small and week pulses
The second abnormal signal studied in this paper is a CAPW with small and week dicrotic notch,
which may indicate a situation of heart failure and severe aortic stenosis. Here the signal’s amplitude is smaller
than the normal signal due to vasoconstriction, this signal is shown in Figure 3(c). Applying the DWT on this
signal, similarly to previous cases, plots in Figures 4(c) and 5(c) are obtained. It is seen that there are some
differences between these plots and those for the normal CAPW or the long and bounding signal, specifically,
Figure 4(c) has lower amplitude range, while the details curve Figure 5 (c) is not smooth and has more peaks
than the normal curve. Figure 6(c) and 7(c) show the approximation and details contours, they have an obvious
difference than the contour of the normal CAPW signal. In contrary, the CWT of this week signal presented in
Figure 8(c) has two ovals with wide and thin shapes which have small dissimilarity than normal images shown
in Figure 8(a).
3.3. CAPW signal with free dicrotic notch
The third abnormal case studied is the cardiac failure characterized by a CAPW signal with free
dicrotic notch depicted in Figure 3(d). The consequence of this distortion leads to a dramatic change in the
corresponding DWT results presents in Figure 4(d) and 5(d). The approximation and details contour plots also
have a big difference than the normal contour as seen in Figures 6(d) and 7(d) where the dark areas are larger
and spread over the plot compared with Figures 6(a) and 7(a). The CWT of this abnormal signal is presented
in Figure 8(d) has two ovals with thinner and wider shapes than normal images which is considered as a large
difference between them and the normal images in Figure 8(a).
3.4. CAPW signal with flat dicrotic notch
Another abnormality of the cardiovascular system, the hypertension with CAPW signal that is
characterized by a flat dicrotic notch as seen in Figure 3(e). After executing DWT on this signal, the same way
as previous signals, a corresponding group of signals is obtained Figure 4(e) and 5(e). Comparing transformed
signals of this abnormal signal with those of the normal pulse signal, some differences between these contours
are derived as it has thinner curve while the amplitude is close to the normal. Figures 6(e) and 7(e) also present
a large variation in the colors than the normal contour and the peaks are close to each other. The CWT of this
abnormal signal is depicted in Figure 8(e) and there is a slightly dissimilar between it and the normal images
shown in Figure 8(a). Table 1 summarizes the obtained results as detected from Figures 4 to 8. The proposed
technique is a novel addition to the medical diagnosis processes as the abnormality of the CAPW is now noticed
faster and easier than the conventional diagnosis processes. The proposed system helps researchers as it
provides a new comparison method to compare between the normal and the abnormal CAPW signals. Also,
the differences in characteristics between the different types of the abnormal CAPW signals become easily
distinguished as well.

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Table 1. Summary of the obtained results as detected from Figures 4 to 8
Features in DWT Signals Features in DWT contour plots Features in CWT 3D plots and
its contour
Approximation
3D plots
Details
3D plots
Approximation
contours
Details
contours
contour 3D plots
Normal
CAPW
Amplitude is in the
range
[0, 1] and the shape
is almost rough.
Amplitude is
in the range
[-0.2, 0.2] and
one sharp
peak with flat
curve
Large number of
colors degrades
(11 colors) and
peaks are close to
each other
Large number
of colors
degrades (11
colors) and 2
thin oval shapes
Two ovals
with wide
and thin
shapes
Amplitude
range
[-2,2.5]
3 peaks at the
bottom
CAPW
With
Large and
Bounding
Pulses
Amplitude is in the
range [0,1.2] and the
shape is narrow and
smoother than
normal signal
Amplitude is
in the range
[-0.3, 0.3] and
not flat curve
with more
peaks
Small number of
colors degrades
(8 colors) and
peaks are far from
each other
Small number
of colors
degrades (7
colors) and 3
wide oval
shapes
Two ovals
with
thinner
shapes than
normal
Amplitude
range
[-1.5,2.5] and 2
peaks at the
bottom
CAPW
With
Small and
Week
Pulses
Amplitude is in the
range
[0, 0.6] and the
shape is narrow and
smoother than
normal signal
Amplitude is
in the range
[-0.1, 0.1] and
not flat curve
with more
peaks
Small number of
colors degrades
(8 colors) and
peaks are far from
each other
Small number
of colors
degrades (6
colors) and 3
wide oval
shapes
Two ovals
with wide
and thinner
shapes than
normal
Amplitude
range
[-0.8,1.3] and 2
peaks at the
bottom
CAPW
With Free
dicrotic
Notch
Amplitude is in the
range [0,1] and the
shape is wider and
smoother the normal
signal
Amplitude is
in the range
[-0.2, 0.2] and
not flat curve
with one peak
Large number of
colors degrades
(12 colors) and
peaks are closer to
each other
Large number
of colors
degrades (12
colors) and 3
thin oval shapes
Two ovals
with
thinner and
wider
shapes
Amplitude
range
[-0.8,1.3] and 2
peaks at the
bottom
CAPW
With Flat
dicrotic
Notch
Amplitude is in
range [0,1] and the
shape is thinner and
smoother than
normal signal
Amplitude is
in the range
[-0.2, 0.2] and
not flat curve
with one peak
Large number of
colors degrades
(12 colors) and
peaks are closer to
each other
Large number
of colors
degrades (10
colors) and 3
thin oval shapes
Two ovals
with wide
and thinner
shapes than
normal
Amplitude
range
[-2,2] and 2
peaks at the
bottom
4. CONCLUSION
This study found new concept of using wavelet technique to analyze different forms of arterial pulse
wave signal and to augment medical information embedded in the carotid pulse. It was seen from the discussion
how this method is capable of differentiating different forms of the analyzed signal which reflects different
pathological cases of the carotid artery. This method can be enriched by extracting numerical values from the
obtained images such as the amplitude and number of peaks and valleys for each case. It is worth to say that
this study can be extended to analyze the pulse wave of other arteries like the radial and femoral arteries.
APPENDIX
(a)
Figure 8. Contour plots (left sub-figures) and 3D surface plot (right sub-figures), (a) Normal CAPW signal,
(b) CAPW with large and bounding pulses, (c) CAPW with small and week pulses, (d) CAPW with free
dicrotic notch, and (e) CAPW with flat dicrotic notch (continue)
0
20
40
60
0
50
100
150
200
-2
-1
0
1
2
3
Time
CWT of Normal Signal at scale: 200
Scale
Amplitude
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5

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(b)
(c)
(d)
(e)
Figure 8. Contour plots (left sub-figures) and 3D surface plot (right sub-figures), (a) Normal CAPW signal,
(b) CAPW with large and bounding pulses, (c) CAPW with small and week pulses, (d) CAPW with free
dicrotic notch, and (e) CAPW with flat dicrotic notch

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