Changes in the Spectral Characteristics of Plethysmographic Waveforms
Due to PAOD
Irina Mizeva
1
, Andrey Dumler
2
and Nikita Muraviev
2,3
1
Institute of Continues Media Mechanics, ak. Koroleva, 1, Perm, Russia
2
Perm State Medical Academy, Petropavlovskaya str. 26, Perm, Russia
3
The first St. Petersburg State Medical University. named after Acad. Pavlov, Tolstova st., 17, St.Petersburg, Russia
Keywords:
Plethysmography, PAOD, Wavelets, Pulse Wave.
Abstract:
Peripheral arterial occlusive disease (PAOD) of increasing severity can lead progressively to disabling claudi-
cation, ischemic rest pain and gangrene. The blood supply of a limb with peripheral arterial disease is restored
by surgical operations, which treats the critical limb ischemia (CLI) only in 30% of the cases. CLI occurs
when the arterial lumen decreases significantly and the nutritive requirements of the tissues, supplied by mi-
crocirculation, cannot be met. In the present paper, a simple, non-invasive and low-cost technique is proposed
for early screening diagnosis of PAOD. The approach is based on the investigation of the spectral character-
istics of pulse waves measured by photoplethysmography. Painless, versatility and simplicity are significant
merits of the proposed methodology.
1 INTRODUCTION
Atherosclerosis is the most common cause of the
pathology of the major arteries of the lower extrem-
ities. The disease state is associated with the stenosis
of vessels in extremities and the formation of a com-
plex of clinical signs, designated as a chronic periph-
eral arterial obliterative disease. Progression of steno-
sis and (or) occlusion of major arteries results in the
gradual decompensation of the blood flow in the ex-
tremity, increasing thus the rates of mortality and dis-
ability. In this stage of PAOD, critical limb ischemia
takes place, and surgery is recommended. The pur-
pose of vascular surgery is to restore a proper blood
flow and to enlarge the lumen of a stenosed vessel.
However, the postoperative mortality within the first
year after surgery reaches 20%, and approximately
half of the patients, who underwent surgery, need a
second operation (Norgren et al., 2007).
Diagnosis of PAOD includes anamnesis descrip-
tion, physical examination, and medical tests, of
which a Doppler ultrasound test of injured arteries
and ankle brachial pressure index (ABPI) measure-
ment are used most extensively. Doppler ultrasonog-
raphy of lower extremities provides visualization of
arterial injuries and the character of blood flow within
the injured segments and a tentative estimation of the
amount of damage and the degree of stenosis.
X-ray contrast aortoarteriography is a reliable
imaging technique for optimal surgery planning. This
method includes evaluation of the abdominal aorta
with an intracardiac echocardiography probe, intro-
duction of X-ray contrast agents and x-ray imaging of
the vascular bed of the lower extremity. However, the
x-ray technique can only be used in operating rooms,
and is not suitable for fast preliminary diagnostic.
The optimal screening technique to examine
PAOD meets several requirements: noninvasiveness,
simplicity, reliability, reproducibility, and the possi-
bility of obtaining rapid results. The most common
method is based on the determination of the ankle
brachial pressure index ABPI, which is the ratio of
systolic leg blood pressure to systolic arm blood pres-
sure. The value of ABPI< 1 indicates the main blood
flow disorders in lower extremities (Norgren et al.,
2007). For PAOD, the value of ABPI is asymmet-
ric, which is attributed to the various degrees of dam-
age in the extremities. The above method is nonin-
vasive and can be used for primary diagnosis of the
disease. The disadvantage of this method is that it is
prone to the subjectiveness of the practitioner. Be-
sides, blood pressure drifting, since measurements on
extremities are not performed simultaneously, limits
the technique.
Pulse wave is a high blood pressure wave prop-
agating through the aorta and arteries. It is caused
149
Mizeva I., Dumler A. and Muraviev N..
Changes in the Spectral Characteristics of Plethysmographic Waveforms Due to PAOD.
DOI: 10.5220/0004747301490154
In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2014), pages 149-154
ISBN: 978-989-758-011-6
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
by the ejection of blood from the left ventricle of
the heart during contraction of its muscles (systole
phase). The pressure wave propagates along the ar-
terial segment of the vascular system, and a short-
term expansion of the arterial wall can be palpated or
registered as an arterial pulse. The velocity of pulse
wave propagation in the vessels does not depend on
the blood flow rate and is defined by the elasticity and
diameter of the vessel, the thickness of vessel walls,
and the density of blood.
The shape of the volume pulse wave is produced
by the interaction between the left ventricle and the
blood vessels of the systemic circulation. The first
peak of the pulse wave is formed due to the systolic
forward wave, and the second - due to the reflected
wave, which arises from the reflection of the volume
of blood circulating through the aorta and large arter-
ies to the lower extremities and moving back to the
ascending segment of the aorta. The available results
(O’Rourke and Kelly, 1993) have indicated that the
intensity of wave reflection is specified by the tone
of small muscular arteries in the main areas of reflec-
tion. That is why, the pulse waveform analysis can
adequately characterize the functional state and struc-
tural changes of a peripheral arterial bed.
There are different methods to register periph-
eral pulse waves: sphygmogram (Carter, 1968), PPG
(Allen et al., 2008), (Erts et al., 2005), (Lin, 2011),
impedance rheovasography (Schuhfried et al., 2003),
(Sherebrin and Sherebrin, 1990), and Doppler sonog-
raphy. It is known that the disorders of veins and ves-
sels cause qualitative changes in the peripheral pulse
waveform.
In this paper, we address photoplethysmography
(PPG), which is a simple and low-cost optical tech-
nique for blood flow registration. The method is based
on the determination of the blood volume in the tissue
sample (see for the review (Allen, 2007)) placed be-
tween the receiver and the source of optical radiation.
Radiation frequency is chosen so that it can be maxi-
mally absorbed by blood red cells. Strictly speaking,
the signal is proportional to the number of red blood
cells entering the region between the source and the
receiver. Since the change in hematocrit (volume of
blood red cells per unit volume of blood) during a
single measurement is small, the intensity of the light
recorded by the receiver is inversely proportional to
the volume of blood in the lumen vessel area.
The registered signal, called a photoplethysmo-
gram (PPG), is a superposition of the variable compo-
nent (AC), associated with changes in the tissue blood
volume synchronous with a heartbeat, and the slowly
varying component (DC), associated with respiration,
sympathetic nervous system activity, and thermoreg-
ulation (Allen, 2007), (Holohan, 1996). Also, the ab-
sorption of light by bones, skin, tissues, and the blood
volume that remains unchanged in the venous and ar-
terial segments of the microvascular bed add a con-
stant level of PPG. Therefore, the absolute value of
PPG has no physiological meaning, and only the pul-
sations of different frequency bands or the changes in
the PPG level during the physiological tests can be
used in practice.
When a PPG probe is attached distally to the
artery stenosis, the waveform rises slowly, the peak
becomes more rounded, and the second peak (dicrotic
wave) is absent or attenuated. In (Allen et al., 2008),
the authors derived a parameter set for describing
pulse waveforms and performed the quantitative anal-
ysis of these parameters in healthy patients and pa-
tients with PAOD. Note that the spectral analysis of
peripheral pulse waves in patients with PAOD has not
been carried out previously, although the results pre-
sented in (F. Javed et al., 2010) have demonstrated the
effectiveness of the Fourier analysis for the study of
pulse waveforms in healthy volunteers.
Over the past two decades, the wavelet analy-
sis has been used extensively to study the signals of
various nature at different scales (Nesme-Ribes E.
et al., 1995). The method has been developed sig-
nificantly in the analysis of astrophysical data, and in
the last decade introduced in the analysis of biophys-
ical signals, in particular, to assess the status of cen-
tral hemodynamics and peripheral circulation (Leon-
des, 2002), (Bernjak and Stefanovska, 2007). In the
wavelet decomposition procedure, the effects of mul-
tiple harmonics, noise and motion artifacts are essen-
tially weaker than in the case of Fourier decomposi-
tion (Nesme-Ribes E. et al., 1995).
Since the low-frequency fluctuations of the heart
rate is mediated mainly by the sympathetic nervous
system, it is also reasonable to attribute the low-
frequency fluctuations in the baseline and amplitude
of the PPG signal to the same nervous system. Fur-
thermore, the fluctuations in the finger blood volume
are due to the constriction and relaxation of the tissue
blood vessels which are predominantlyaffected by the
sympathetic nervous system (Nitzan et al., 1998).
The purpose of the paper is to investigate the spec-
tral characteristics of PPG signal in LF and HF bands
in the distal parts of lower extremities of patients with
PAOD.
2 MATERIALS AND METHODS
The investigation were conducted for 59 male volun-
teers - 25 healthy (age 55 ±9, ABPI 1.2 ±0.6) and
BIOSIGNALS2014-InternationalConferenceonBio-inspiredSystemsandSignalProcessing
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34 (age 60 ±11) with CLI, treated at the Department
of Cardiovascular Surgery, Perm’s Clinical Hospital
No 4. In the investigation, we did not include patients
with diabetes and autoimmune diseases of blood ves-
sels. The main clinical manifestation, which occurred
in patients selected for the study, was the presence
of intermittent claudication. In this case, the diagno-
sis was made according to the recommendations of
the TASC II in the presence of typical clinical symp-
toms for at least 2 weeks. The clinical examination,
ABPI measurements and radionuclide aortoaretriog-
raphy were used to verify the tentative diagnosis.
In the majority of cases critical limb ischemia pro-
duces different effects on the lower extremities of the
person. In group B (34 records) we included PPG col-
lected from the lower extremities seriously affected
by arterial disease (ABPI= 0.7 ±0.2); the invasive
methods supported the necessity of surgical revas-
cularization. The contralateral extremities were less
amenable to this disease, and PPG were included in
group A (34 records). It should be noted that PAOD
is a manifestation of generalized atherosclerosis, and
so the measurements of the contralateral limb (group
A, ABPI=1.0±0.2) correspond to the atherosclerotic
lesions of major arteries, which do not lead, at the
time of the survey, to peripheral circulatory decom-
pensation. Figure1 presents data corresponding to the
control group and groups A and B.
Figure 1: Characteristic form of pulse waves in the distal
parts of the lower extremities measured in different groups:
control group (top plot), group A (middle plot), and group
B (bottom plot).
To investigate the influence of surgery on the low
frequency pulsations we used in the study long-time
(10 minutes) PPG records collected from 5 healthy
subjects, 5 diseased persons before (B1 group) and 3
days after (the influence of operation anesthesia was
excluded) the revascularization surgery (B2 group).
Registration of the PPG signal was carried out
at controlled temperature (24±1
C ) after a fifteen-
minute adaptation of the patient to the measurement
system. Data were collected from the distal phalnax
of second toe of the subject lying in the supine po-
sition. A standard patient monitor Microlux (Russia)
designed to record the PPG signal in the transparent
mode was used. The Nellcor PPG probe was held
comfortably in place, and the interference from the
external light sources was reduced.
The software was adapted for the purposes of the
study, all signal pre-processing functions were dis-
abled, sampling frequency was 50 Hz. Measurements
were performed for 10 minutes in series with the two
extremities. We did not consider the PPG, which
could not be processed due to poor tissue perfusion
or due to movement artifacts caused by the tremors of
the extremities.
3 CALCULATION
Usually, by the spectral analysis the decomposition
of the signal into a Fourier series of harmonic func-
tions is meant. The harmonic functions are defined
from to +, and in the analysis of real signals
we are dealing with finite realizations. Choosing an
analyzing function in the limited space, yields a gen-
eralization of the Fourier analysis - the wavelet anal-
ysis. This method gives better results in the analy-
sis of short nonstationary data with a low signal to
noise ratio. Since the PPG signal for extremities with
CLI is rather weak and the signal to noise ratio is
low, it seems reasonable to use wavelets. The discrete
wavelet transform of the function f (t) is
W(ν,τ) =
ν
t=
f(t)ψ(t τ,ν). (1)
In (1) t is the time, τ is the time shift, ν ( 1/a,
where a - is time scale) is the frequency, and ψ(t,ν) is
the function called an analyzing wavelet, the form of
which depends on the signal of interest and the pur-
pose of the study.
The wavelet transform (1) of one-dimensional
signal gives a two-dimensional image on the time-
frequency plane. This kind of presentation allows us
to study the variation of the oscillation characteristics
of different scales in time. Since we are dealing here
with the stationary signal investigation, we consider a
global power spectrum, which is obtained by integrat-
ing the power over time:
ChangesintheSpectralCharacteristicsofPlethysmographicWaveformsDuetoPAOD
151
M(ν) =
1
T
T
t=0
|W(ν,τ)|
2
. (2)
Normalization
ν in (1)allows us to compare the
Fourier scalogram and wavelet global spectrum. We
use the complex Morlet wavelet ψ(t) = e
2πit
e
t
2
/(2σ
2
)
(Goupillaud et al., 1984) and the damping parameter
σ = 2. This analyzing function has enough spectral
resolution and is well localized in time.
The wavelet global spectrum (2) of each recording
was performed in the frequency range 0.01-5 Hz by
100 harmonics with logarithmic frequency decompo-
sition. Fig.2 shows a comparison of the Fourier scalo-
gram and wavelet spectra for one PPG from the group
of healthy patients. It is seen that the wavelet spec-
trum reproduces fairly well the main features of the
Fourier spectrum.
Both, the power spectral density (called also the
Fourier spectrum) and the wavelet spectrum demon-
strate the presence of frequency pulsation. Although
the spectra coincide in the frequency range under con-
sideration, it is easier to analyze the wavelet spectrum.
The limitation of the observation time leads to a very
indented Fourier spectrum. The smooth power spec-
tral density can be observed in the case when T ,
but under real conditions the time interval is rather
short. As we intend to create a time-saving method of
screening diagnostics, which will last a few minutes,
we cannot increase significantly the observation time.
We suggest to use the spectral characteristics of
high frequency band for description of the pulse
waveform, namely we use the energy, which is local-
ized in the vicinity of 2ν
HR
frequency. In this fre-
quency band the difference in the spectra of healthy
and diseased people is very strong.
0.05
0.10
0.50
1.00
10
100
1000
10
4
10
5
10
6
Ν,Hz
E, a.u.
Figure 2: Fourier (black thick line) and wavelet (gray thin
line) spectra of PPG signal.
To compare the energy of pulsation at cardiac fre-
quency ν
HR
and at 2ν
HR
, we define the dimensionless
frequency (
˜
ν) and energy
˜
E(
˜
ν), which is maximum in
the range from 0.3 to 5 Hz.
˜
E(
˜
ν) = E(ν)/Max[E(ν)]
˜
ν = ν/ν|
Max[E(ν)]
(3)
Figure 3: Normalized wavelets in the control group (top
panel), subgroup A (middle panel), and subgroup B (bottom
planel).
The observed spectra, which were normalized us-
ing
˜
E and
˜
ν, are shown in Fig.3. In the spectra of sig-
nals obtained in the control group (top panel in Fig.3),
the secondary peak is observed in the frequencyrange
1.7-2.2 Hz. In the group A (middle panel in Fig. 3)
the energy of oscillations within this frequency range
is smaller, which is shown by the peak that is less pro-
nounced at these frequencies. In group B the reflected
wave due to the so called ”early reflection” coincides
with the main peak producingno visible dicrotic wave
(Nichols et al., 2011) (bottom panel in Fig.1). It is
accompanied by a decrease in the energy spectra, as
shown in the bottom panel of Fig.3.
We did not examine the spectra, within which we
could not identify accurately the first harmonic be-
cause of severe arrhythmias and/or poor perfusion of
tissues with blood. So we excluded 5 persons from
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152
Figure 4: Distribution of I for different groups: dots denote
mean values, straight line - medians, and upper and lower
boundaries of boxes correspond to distribution percentiles
(25% - lower, 75% - upper). Hatching indicates different
limits of integration (open rectangles ν
1
= 1.8, ν
2
= 2.2;
gray rectangles ν
1
= 1.7, ν
1
= 2.3; hatched rectangles ν
1
=
1.6, ν
1
= 2.4.
B group and 8 from A group. To quantify the energy
fluctuations in some frequency band, we introduce an
index I defined by
I = log
Z
ν
2
ν
1
˜
E(
˜
νd
˜
ν), (4)
where ν
1
and ν
2
are the boundary frequencies for the
chosen interval. To compare the reflected wave en-
ergy, we take the integral with the limits in the vicin-
ity of ν = 2. Fig.4 illustrates the distribution I for
different combinations of the boundary frequencies
ν
1
and ν
2
. The variation of the frequencies within
certain limits does not cause changes in the statisti-
cally significant properties of the quantities of inter-
est, namely, the differences between the group data
remain reliable even though the integration interval
becomes twice as much.
Table 1 summarizes the mean values of the index
I and ABPI measured in the examined groups. The
obtained results are presented as a M±SD (M - mean
values, SD - standard deviation). The mean values for
different groups were compared in the analysis of re-
liability by applying the Mann-Whitney test. The reli-
ability p < 0.05 is considered statistically significant.
From Table 1 it follows that in the examined groups
the values of index I differ significantly, and the dif-
ference is more reliable for index I than for ABPI.
4 LOW FREQUENCY
PULSATIONS
Fig.5 presents the averaged spectra of long PPG
records. The subjects with PAOD have the higher
low-frequency energy, which can be attributed to
Table 1: Comparison of the ankle-brachial index (ABPI)
with the index (I) for different groups.
groups A B Control
ABPI 1.0±0.2 0.7±0.2 1.2±0.6
p
AB
=0.005 p
AC
= 0.04
p
BC
= 0.04
I 4.1±0.5 4.6±0.4 2.9±0.4
p
AB
=0.02 p
AC
< 0.00
p
BC
< 0.001
Figure 5: Averaged spectra of control group, PAOD with
CLI subjects (B1 group) and the same PAOD subjects 3
days after revascularization surgery (B2 group).
the fact that compensatory mechanisms increase
the blood flow supply. After the revascularization
surgery, the low-frequency pulsations associated with
the sympathetic nervous system are depressed. Since
the PPG were collected 3 day after surgery, the effect
of anesthesia should be negligible.
5 SUMMARY
In this study, the spectral method has been applied
for a quantitative description of the characteristics of
pulse waves in the lower extremities of patients with
PAOD.
It is shown that index I determined as the ratio of
energy of the heart beat frequency νHR and on the
2νHR, obtained by integrating the normalized spectra
in a defined frequency band is significantly different
in the examined subgroups. The experiments have in-
dicated that the proposed technique has high sensitiv-
ity and can be used as the traditional tool (ABPI de-
terming) in screening diagnostics. It has been found
that the ratio of secondary and primary waves in the
limb with CLI is several times smaller than the same
ratio in the healthy limb.
Obtained results may favor further development
of research in this area. The merits of the proposed
methodology such as painless, versatility and simplic-
ChangesintheSpectralCharacteristicsofPlethysmographicWaveformsDuetoPAOD
153
ity, as well as an ever increasing use photoplethysmo-
graphic monitors in clinical practice create conditions
that promote implementation of the proposed method
for screening diagnosis of PAOD.
Restrictions imposed in connection with the use
of PPG are associated with weak signals generated by
motion artifacts or with the limited peripheral blood
flow (F. Javed et al., 2010). The numerical analysis
of the PPG signals proposed in this paper can be ap-
plied to pulse waveforms obtained by other registra-
tion techniques.
The results obtained in the study of LF waves as-
sociated with sympathetic nervous system are also
presented. The spectral analysis of the PPG signal
shows, that LF pulsations of blood flow of subjects
with PAOD are higher then of healthy ones. The
cause of it can be microcirculation adaptation to the
decreased blood flow in the limb. The amplitude of
LF oscillations becomes smaller after surgery. This
can be explained by suppressing active mechanisms,
induced by peripheral nervous fibers trauma.
ACKNOWLEDGEMENTS
The work is undertaken under financial support of
RFBR-Ural 11-01-96018.
REFERENCES
Allen, J. (2007). Photoplethysmography and its application
in clinical physiological measurement. Physiological
Measurement, 28(3):R1.
Allen, J., Overbeck, K., Nath, A. F., Murray, A., and
Stansby, G. (2008). A prospective comparison
of bilateral photoplethysmography versus the ankle-
brachial pressure index for detecting and quantifying
lower limb peripheral arterial disease. Journal of Vas-
cular Surgery, 47(4):794 – 802.
Bernjak, A. and Stefanovska, A. (2007). Importance of
wavelet analysis in laser doppler flowmetry time se-
ries. In Engineering in Medicine and Biology Society,
2007. EMBS 2007. 29th Annual International Confer-
ence of the IEEE, pages 4064–4067.
Carter, S. A. (1968). Indirect systolic pressures and pulse
waves in arterial occlusive disease of the lower ex-
tremities. Circulation, 37(4):624–637.
Erts, R., Spigulis, J., Kukulis, I., and Ozols, M. (2005). Bi-
lateral photoplethysmography studies of the leg arte-
rial stenosis. Physiological Measurement, 26(5):865.
F. Javed, P. M. Middleton, P. Malouf, G.S H Chan, A.V.
Savkin, N.H. Lovell, E. Steel, and J. Mackie (2010).
Frequency spectrum analysis of finger photoplethys-
mographic waveform variability during haemodialy-
sis. Physiological Measurement, 31(9):1203.
Goupillaud, P., Grossmann, A., and Morlet, J. (1984).
Cycle-octave and related transforms in seismic signal
analysis. Geoexploration, 23(1):85–102.
Holohan, T. (1996). Plethysmography: safety, effectiveness,
and clinical utility in diagnosing vascular disease. Di-
ane Publishing Company.
Leondes, C. (2002). Computational Methods in Biophysics,
Biomaterials, Biotechnology and Medical Systems:
Algorithm Development, Mathematical Analysis and
DiagnosticsVolume I: Algorithm TechniquesVolume
II: Computational MethodsVolume III: Mathemati-
cal Analysis MethodsVolume IV: Diagnostic Methods.
Springer.
Lin, C.-H. (2011). Assessment of bilateral photoplethys-
mography for lower limb peripheral vascular occlu-
sive disease using color relation analysis classifier.
Computer Methods and Programs in Biomedicine,
103(3):121 – 131.
Nesme-Ribes E., Frick P., Sokoloff D., Zakharov V., Ribes
J.-C., Vigouroux A., and Laclare F. (1995). Wavelet
analysis of the Maunder minimum as recorded in solar
diameter data. Academie des Sciences Paris Comptes
Rendus Serie B Sciences Physiques, 321:525–532.
Nichols, W. W., O’Rourke, M. F., and Vlachopoulos, C.
(2011). McDonald’s Blood Flow in Arteries, 6th
ed: Theoretical, Experimental and Clinical Princi-
ples. Hodder Arnold Publishers, 6 edition.
Nitzan, M., Babchenko, A., Khanokh, B., and Landau, D.
(1998). The variability of the photoplethysmographic
signal - a potential method for the evaluation of the au-
tonomic nervous system. Physiological Measurement,
19(1):93.
Norgren, L., Hiatt, W., Dormandy, J., Nehler, M., Har-
ris, K., and Fowkes, F. (2007). Inter-society con-
sensus for the management of peripheral arterial dis-
ease (tasc ii). Journal of Vascular Surgery, 45(1,
Supplement):S5 S67. ¡ce:title¿TASC II¡/ce:title¿
¡ce:subtitle¿Inter-Society Consensus for the Manage-
ment of PAD¡/ce:subtitle¿.
O’Rourke, M. F. and Kelly, R. P. (1993). Wave reflection
in the systemic circulation and its implications in ven-
tricular function. Journal of hypertension, 11(4):327–
337.
Schuhfried, O., Wiesinger, G., Kollmitzer, J., Mittermaier,
C., and Quittan, M. (2003). Fourier analysis of
impedance rheography for peripheral arterial occlu-
sive disease. European Journal of Applied Physiology,
89(3):384–386.
Sherebrin, M. and Sherebrin, R. Z. (1990). Frequency anal-
ysis of the peripheral pulse wave detected in the finger
with a photoplethysmograph. Biomedical Engineer-
ing, IEEE Transactions on, 37(3):313–317.
BIOSIGNALS2014-InternationalConferenceonBio-inspiredSystemsandSignalProcessing
154