Medical diagnosis of rheumatoid arthritis disease from right and
left hand Ulnar artery Doppler signals using adaptive network based
fuzzy inference system (ANFIS) and MUSIC method
1
Ali Osman ÖZKAN, 2Sadık KARA, 3Ali SALLI
4
Mehmet Emin SAKARYA and 5Salih GÜNEŞ*
1
Selcuk University, Vocational College of Technical Sciences, 42003, Konya-Turkey
2
Fatih University, Institute of Biomedical Engineering, 34500, Istanbul-Turkey
3
Selcuk University, Meram Faculty of Medicine, Dept. of Physical Med. and Rehabilitation, Konya-Turkey
4
Selcuk University, Meram Faculty of Medicine, Dept. of Radiology, Konya-Turkey
5
Selcuk University, Dept. of Electrical and Electronics Eng., 42035, Konya-Turkey
ABSTRACT:
Rheumatoid arthritis (RA) is a multi-systemic autoimmune disease that leads to substantial morbidity
and mortality. In this paper, as spectral analysis methods of Multiple Signal Classification (MUSIC)
method is used in order to extract the significant features from the right and left hand Ulnar artery
Doppler signals for the diagnosis of RA disease. The MUSIC method has been used as subspace
method. To extract features from Doppler signals obtained from the right and left hand Ulnar arterial
the MUSIC method model degrees of 5, 10, 15, 20, and 25 were used. Then, an adaptive network
based fuzzy inference system (ANFIS) was applied to features extracted from the right and left hand
Ulnar artery Doppler signals for classifying RA disease. In the hybrid model, the combination of
MUSIC and ANFIS yielded classification accuracies of 95% (for a model degree of 20) using the right
hand Ulnar artery and classification accuracies of 91.25 % (for a model degree of 10) using left hand
Ulnar artery Doppler signals in the diagnosis of RA disease. The proposed approach has potential to
help with the early diagnosis of RA disease for the specialists who study this subject.
Keywords: Rheumatoid arthritis disease; Ulnar artery; MUSIC method; Adaptive network based fuzzy
inference system.
1. Introduction
1
Rheumatoid arthritis (RA) is observed in all races worldwide with varying frequency.
Genetic factors play an important role and likely account for about 50 % of disease
susceptibility [1]. RA is a chronic disease with symmetrical polyarticular involvement and
systemic symptoms, such as fatigue and low level fever [2]. RA is an autoimmune disease that
causes chronic inflammation in the joints. RA can also cause inflammation of the tissue
around the joints, as well as in other organs in the body. Autoimmune diseases are illnesses
that occur when the body's tissues are mistakenly attacked by their own immune system [3].
RA is a systemic chronic inflammatory disorder that mainly affects diarthrodial joints. It is
characterized by inflammatory activity of synovium leading to the destruction of bone and
joint cartilage along with periarticular structures like tendons and ligaments. It is the most
common form of inflammatory arthritis and the world prevalence of RA is approximately 0.31.2 % in a female/male ratio of 2.5/1. It is most common in patients aged 40 - 70 years old
and its incidence increases with age [4-6].
The Ulnar artery is the main blood vessel of the medial section of the forearm. It arises
from the brachial artery and terminates in the superficial palmar arch, which joins with the
superficial branch of the radial artery. It is palpable on the anterior and medial section of the
wrist [7].
RA disease activity and its therapeutic response is predominantly measured using
clinical assessments and laboratory tests for serum markers of inflammation, such as C
reactive protein (CRP) or erythrocyte sedimentation rate (ESR). Tenderness and swollen joint
counts are essential for physical examinations and evaluating disease activity. They also
comprise the Disease Activity Score 28 (DAS 28), which was developed for evaluating
disease activity in RA. However, clinical evaluation of joint pain and swelling has not been
sufficiently reliable [8]. Direct radiography can be used for evaluating established erosions,
but gives us little information on synovial inflammation and early erosions [9]. However,
color Doppler ultrasound (CDU) displays blood flow in the tissues and can be a marker of the
inflammatory response. Thus, the amount of CDU activity in the inflamed synovium can be
used to quantify the inflammatory activity in RA [10].
2
The Doppler Effect is used in ultrasonic Doppler devices for the measurement and
imaging of blood flow transcutaneous. In these devices, ultrasonic waves are launched into a
blood vessel by an ultrasonic transducer and the scattered radiation from the moving red cells
is detected by either the same or a separate transducer. Appropriate instrumentation is
incorporated to extract the Doppler frequency, which is proportional to the red cell velocity
[11].
The rebounded echoes are Doppler shifted. The Doppler shift is related to the flow
velocity by.
f
d
= f t - fr =
2 υ Cosθ
ft
c
(1)
Where f is the mean frequency of the Doppler spectrum, f t is the frequency emitted by the
d
transducer, f r is the frequency of the returned echo, υ is the flow velocity, θ is the Doppler
angle and c is the velocity of sound in blood. For an ultrasound transmitting at frequencies
between 1&15 MHz [11], blood flow velocities in the human body generate Doppler-shifted
echo frequencies in the audio range.
Recent literature compares the Doppler Ultrasound images of healthy subjects and
patients having RA disease, and calculates the resistive index (RI) and pulsalite index (PI) of
these images [12-16].. Therefore, this study is a novel study using Doppler ultrasound signals
on the diagnosis of RA disease. When we look at the studies, it has been observed that doctors
have often worked with devices such as Doppler ultrasound and MR images in diagnosing RA
disease. Therefore, our study is novel as it is a signal processing from the Ulnar artery
Doppler signal.
In this study, as spectral analysis method the MUSIC method has been used to extract
the significant features from the right and left hand Ulnar artery Doppler signals for
diagnosing the RA disease. The detection of RA disease is comprised of three phases: (i)
acquisition of the right and left hand Ulnar arterial Doppler signals, (ii) feature extraction
using the MUSIC method power spectral density (PSD) graphics obtained from Doppler
3
ultrasound signals taken from the right and left hand Ulnar artery, and (iii) the classification
of RA disease as healthy and patient using ANFIS. The MUSIC method model with degrees
of 5, 10, 15, 20, and 25 were used in the process of feature extraction from the Doppler
signals belonging to the right and left hand Ulnar artery. Later, ANFIS was used to classify
the Doppler signals belonging to the right and left hand Ulnar arterial to find out whether the
patient had RA or not. ANFIS is hybrid learning algorithms combining the adaptive features
of artificial neural networks with fuzzy logic qualitative feature extraction [17-18]. ANFIS
uses a hybrid learning algorithm combining the slopped decrease and the least squares
method. While the least squares method provides a fast learning, slopped decrease changes
membership functions generating the basic functions of the least squares method [17-18].
2. Material
2.1 Hardware and Demographic Acknowledgments
The Ulnar arterial Doppler ultrasound signals were obtained from the right and left
hand Ulnar arteries of 40 patients with RA diseases and 40 healthy volunteers. The patients
are comprised of 8 males and 32 females, between 38 and 70 years of age, with a mean age
and standard deviation of 51 ± 9.6 years. The healthy volunteers are comprised of 10 males
and 30 females, between 44 and 73 years of age, with a mean age and standard deviation of
57 ± 9.1 years.
The study was approved by the local ethical committee. All subjects gave their written
informed consent prior to the study.
Doppler signal acquisition was accomplished with a General Electric LOGIQ S6
Power Doppler Ultrasound Unit from the Radiology Department in the Meram Faculty of
Medicine of Selcuk University. The system hardware was comprised of a Power Doppler
Ultrasound unit that can work in the pulsed mode, a linear ultrasound probe (12 MHz) and a
personal computer (Figure 1). A personal computer was used for storing, displaying and
performing spectral analysis of the obtained Doppler data.
4
Figure 1. Block diagram of the system hardware used to obtain Doppler data.
Before Doppler data was recorded, a color and pulsed Doppler ultrasound examination
of the right and left hand Ulnar arterial was performed in order to exclude the presence of a
hemodynamically significant stenosis. A linear ultrasound probe of 12 MHz was used to
transmit pulsed ultrasound signals into the right and left hand Ulnar arterial. Signals reflected
from the arterial were recorded to extract the Doppler shift frequencies. In all tests performed
on the patients and healthy subjects, the insonation angle and the presetting of the ultrasound
were kept fixing. The insonation angle was adjusted both manually & via electronic steering
methods to keep a constant value of 60 degrees on a longitudinal view. The sampling volume
was placed within the center of the arterial. The amplification gain was carefully set to obtain
a clean spectral output with minimized background noise on the spectral display [19-23]. The
audio output of the ultrasound units was sampled at 44.1 kHz and then sent to a computer.
Figure 2 shows the Doppler signals for a healthy subject on the right and left hand
Ulnar artery, while Figure 3 shows the Doppler signals for a patient having RA disease.
Transforming the Doppler signals from the time domain to the frequency domain using the
MUSIC method RA disease has been successfully diagnosed.
5
0.5
0
-0.5
-1
0
1
2
3
4
5
Normalized Sound Signal Amplitude
Normalized Sound Signal Amplitude
1
1
0.5
0
-0.5
-1
0
1
Time Axis (sec.)
(a)
5
2
3
4
Time Axis (sec.)
(b)
Figure 2. Doppler signals for a subject (no:12) with a healthy
(b) left hand Ulnar artery.
1
0.5
0
-0.5
Normalized Sound Signal Amplitude
Normalized Sound Signal Amplitude
(a) right hand Ulnar artery
1
0.5
0
-0.5
-1
0
1
2
3
4
Time Axis (sec.)
(a)
5
-1
0
1
5
2
3
4
Time Axis (sec.)
(b)
Figure 3. Doppler signals for a patient (no:10) with RA disease on
(a) the right hand Ulnar artery
(b) the left hand Ulnar artery.
The development of quantitative parameters of Doppler flow signals based on spectral
analysis has much value in diagnosing arterial disease. Using spectral analysis techniques, the
variations in the shape of the Doppler spectra as a function of time are presented in the form
6
of sonograms from which medical information can be extracted [24-25]. A sonogram is
plotted with the frequency components and PSD values sequenced on the timeline [26]. The
600
500
450
375
Frequency (Hz)
Frequency (Hz)
AR sonograms of healthy subjects are shown in Figure 4 and patients in Figure 5.
300
150
250
125
0
0
0
3
1
2
4
Time Axis (sec.)
(a)
5
0
1
2
3
4
Time Axis (sec.)
(b)
5
Figure 4. AR sonograms developed for a subject (no:12) with a healthy
(a) right hand Ulnar artery
(b) left hand Ulnar artery.
700
700
525
Frequency (Hz)
Frequency (Hz)
525
350
175
350
175
0
0
0
1
3
2
4
Time Axis (sec.)
(a)
5
0
1
2
3
4
Time Axis (sec.)
(b)
5
Figure 5. AR sonograms developed for a patient (no:10) having RA disease on
(a) the right hand Ulnar artery
(b) the left hand Ulnar artery.
7
3. Method
In this paper, a system with three stages is proposed: the first stage acquires the right
and left hand Ulnar arterial Doppler signals; the second stage extracts features using the
MUSIC method and the third stage classifies RA diseases using ANFIS based on the right and
left hand Ulnar artery Doppler signals. Figure 6 shows the flowchart of the classification
systems. The proposed method will be explained in more detail in the following subsections.
Measurement of Doppler
signals
Acquisition of right and left hand Ulnar
arterial Doppler signals
Feature extraction process
Feature extraction from right and left hand
Ulnar arterial Doppler signals using the
MUSIC method
Classification using the
ANFIS
Classification of right and left hand Ulnar
arterial Doppler signals as healthy and RA
disease using ANFIS
Classification results
RA disease or healthy
Figure 6. The flowchart of the classification systems.
3.1 Feature Extraction Process of MUSIC Spectral Analysis Method
As part of the feature extraction process, the MUSIC spectral analysis method is used
to transform Doppler signals from the time domain to the frequency domain. The MUSIC
method was proposed by R. O. Schmidt in 1979 as an improvement to Pisarenko's method. It
is an algorithm that can be used for frequency estimation [27] and emitter location [28]. The
MUSIC method is frequency estimator technique based on eigen-analysis of the
autocorrelation matrix. This type of spectral analysis categorizes the information of a
correlation or data matrix, as either signal or noise subspace [29].
8
The MUSIC method estimates the frequency content of a signal or autocorrelation
matrix using an eigen-space method. This method assumes that a signal, x ( n ) , consists of p
complex
exponential
in
the
presence
of
Gaussian
white
noise.
Given
an
M  M autocorrelation matrix, Rx , if the eigenvalues are sorted in decreasing order, the
eigenvectors corresponding to the p largest eigenvalues spanning the signal subspace [27,28].
The frequency estimation function for MUSIC is,
P
MUSIC
(e
jW
)
1
2
M
H
 e vi
i  p 1
(2)
T
jW 2 jW 3 jW
j (M 1)W 
where vi are the noise eigenvectors and e  1. e
.e
.e
... e


The MUSIC method proposed by Schmidt [30] eliminates the effects of spurious
zeros by using the averaged spectra of all the eigenvectors corresponding to the noise
subspace [31 - 34].
3.2 Classification of Right and Left Hand Ulnar Artery Doppler Signals Using ANFIS
In this study, we have used ANFIS to classification of right and left hand Ulnar artery
Doppler signals. ANFIS was proposed by Jang in 1993 [18]. ANFIS is a class of adaptive
networks that are functionally equivalent to fuzzy inference systems (FIS). FIS is the process
of formulating the mapping from a given input to an output using fuzzy logic [38]. There are
two types of FIS, the Mamdani-type model and the Sugeno-type model. The most frequently
investigated ANFIS architecture is the first-order Sugeno-type model, due to its efficiency and
transparency [18, 39]. A representative ANFIS architecture with two inputs (x and y) one
output (f) and two rules is illustrated in Figure 7, which consists of five layers [39].
9
Layer 1
Layer 3
Layer 2
A1
M
x
w1
Layer 4
_
N
x
Layer 5
y
w1
_
w1 f1
A2
S
_
B1
y
M
B2
f
w2
N
W2
_
f2
W2
x
y
Figure 7. ANFIS architecture with two inputs one output and two rules.
An adaptive system of a fuzzy, first-order Sugeno-type model is considered to
facilitate learning and adaptation. Fuzzy if-then rules are [17-18, 40-43].
Rule1: If x is A and y is B then f  p x  q y  r
1
1
1 1
1
1
Rule 2: If x is A and y is B then f  p x  q y  r
2
2
2
2
2
2
(3)
In order to apply FIS in ANFIS, two methods including grid partition and subtractive
clustering are used. In the process of ANFIS training, we apply subtractive clustering as these
partitions the input data according to the dimension of the dataset and automatically tune the
input-output membership functions. The least squares method and hybrid learning algorithm
are used to identify the optimal values of these parameters, including consequent and premise
parameters. We used the hybrid learning algorithm in this process [17-18, 40-43].
4. Results and Discussion
American College of Rheumatology criteria which were used to classify RA diseases
in 1987 are still used today [44]. However, early recognition of the disease depends on low
originality and sensitivity. These criteria were modified in 1994 [45]. Disease activity and
therapeutic response is predominantly based on clinical assessments and laboratory tests for
10
serum markers of inflammation like ESR or CRP. Tender and swollen joint counts are
essential for physical examinations and evaluating disease activity [8]. These are the
components of DAS 28, which have been developed for evaluating disease activity in RA.
Figure 8 shows the location of the 28 joints in our body.
Figure 8. Locations of the 28 joints in our body.
The DAS 28 values of forty RA patients participating in the study were calculated
with the following formula:
DAS 28  0,56. TEN 28  0, 28. SW 28  0, 7.ln( ESR)  0, 014.(VAS )
(4)
where TEN28 is the tenderness of joint number, SW28 is the swollen of joint number, ESR is
the after 1 hour in mm, and PA is the patient’s assessment in mm by a specialist. The average,
standard deviation, minimum and maximum values of the DAS 28, VAS, tenderness of joint
number, swollen of joint number, ESR and CRP values of forty RA patients participating in
the study are given in Table 1.
Table 1. DAS 28, VAS, tenderness of joint number, had swollen of joint number, ESR and
CRP values of 40 RA patients.
Value
Mean
DAS 28
VAS (mm)
Tenderness of joint number
Swollen of joint number
ESR
CRP
4.804
51.5
10
1.3
33.2
20.53
Standard
deviation
1.373
19.81
9.526
1.689
18.34
21.16
Maximum
value
7.49
80
28
7
75
78.5
Minimum
value
2.16
10
1
0
3
3
11
DAS 28 score under 2.6 gives the remission, between 2.6 and 3.2 gives the low
disease activity, between 3.2 and 5.1 gives moderate disease activity and also the score of
above 5.1 gives the high disease activity. The results related to the disease situation
determined according to the DAS 28-values of 40 RA-patients are shown in the Table 2.
Table 2. Distribution of patients according to DAS 28
DAS 28 < 2,6
Number of
Disease
2
Disease Situation
Remission
2,6 < DAS 28 < 3,2
1
low disease activity
3,2 < DAS 28 < 5,1
21
moderate disease activity
DAS 28 > 5,1
16
high disease activity
Values of DAS 28
Doppler signals reflected from the right and left hand Ulnar artery were recorded to
derive out the Doppler shift frequencies as seen in Figure 2 and Figure 3. These signals in the
time domain do not contain extra information about existence of the RA diseases. Therefore,
these signals were analyzed in the frequency domain to reveal differences between the healthy
subjects and patient with RA disease.
In this study, as spectral analysis methods the MUSIC method has been used to extract
the significant features from the right and left hand Ulnar artery Doppler signals for
diagnosing the RA disease.
First, the MUSIC method spectral analysis methods were used to extract the relevant
features from the Doppler signals belonging to healthy subjects and patients having RA
disease. In this part, we have used models of various model degrees (5,10,15,20 and 25) in
applying the MUSIC methods to the Doppler signals. For each model degree, the power
spectral density (PSD) values were obtained. Then, the PSD values were applied to input of
ANFIS to classify the Doppler signals as belonging to either healthy subjects or patients
having RA disease. The feature extraction vectors and the classifiers proposed for
classification of the right and left Ulnar artery Doppler signals were implemented with the
MATLAB software package.
In the training and testing of ANFIS, a data partition of 90-10% (72 -8) train-test was
12
used. In our dataset, there are 40 patients with RA diseases and 40 healthy subjects. In totally,
80 subjects were used to test the diagnosis of RA disease. The training input data set consisted
of 36 normal and 36 RA patients (72 sets x 129 samples), while the test data set was made of
4 normal and 4 RA patients (8 sets x 129 samples). In order to evaluate the performance of the
ANFIS models, we have used three methods: classification accuracy (CA), sensitivity (SEN)
and specificity (SPE) analysis, described in the following equations, respectively
CA 
TP  TN
(%)
TP  FP  TN  FN
SEN 
TP
(%)
TP  FN
SPE 
TN
(%)
TN  FP
(5)
where TP , FP , TN and FN denote true positives, false positives, true negatives and false
positives, respectively [29,46].
(1) True positive (TP): a subject with RA disease is detected as a patient diagnosed with RA
disease.
(2) True negative (TN): a healthy subject is detected as normal.
(3) False positive (FP): a healthy subject is detected as a patient diagnosed with RA disease.
(4) False negative (FN): a subject with RA disease is detected as normal [29, 46].
In order to evaluate the performance of ANFIS model of the right and left hand Ulnar
artery Doppler signals , the classification accuracy, ROC (Receiver Operating Characteristic)
curves, sensitivity and specificity values have been used. Table 3 and Table 4 show the
obtained ten-fold Cross Validation average test results by ANFIS for classification of the right
and left hand Ulnar artery Doppler signals. ROC curves display the relationship between
sensitivity (true positive rate) and 1-specificity (false positive rate) across all possible
threshold values that define the positivity of a disease [47]. We have given the obtained ROC
curves and AUC (Area Under the Curve) values for 5, 10, 15, 20, and 25 in MUSIC method
and showed in Figure 9 and Figure 10.
13
Table 3. Obtained ten-fold Cross Validation average test results by ANFIS for
classification of right hand Ulnar artery Doppler signals.
Model
Degree
5
10
MUSIC
15
20
25
Overall average
Method
CA (%)
SEN (%)
SPE (%)
91.25
87.5
90
95
88.75
90.5
94.59
87.5
90
95
91.89
91.8
88.37
87.5
90
95
86.05
89.38
Table 4. Obtained ten-fold Cross Validation average test results by ANFIS for
classification of left hand Ulnar artery Doppler signals.
Model
Degree
5
10
MUSIC
15
20
25
Overall average
Method
CA (%)
SEN (%)
SPE (%)
87.5
91.25
83.75
85
90
87.5
87.5
90.24
86.49
88.89
90
88.62
87.5
92.31
81.4
81.82
90
86.61
14
Music - 5
Music - 10
Music - 15
Music - 20
Music - 25
AUC
0.913
0.875
0.888
0.95
0.888
Figure 9. ROC curves for model degrees of 5,10,15,20 and 25 of MUSIC spectral analysis
method on the early diagnosis of right hand Ulnar artery Doppler signals.
Music - 5
Music - 10
Music - 15
Music - 20
Music - 25
AUC
0.875
0.913
0.838
0.85
0.9
Figure 10. ROC curves for model degrees of 5,10,15,20 and 25 of MUSIC spectral analysis
method on the early diagnosis of left hand Ulnar artery Doppler signals.
15
5. Conclusion
In this paper the MUSIC spectral analysis method have been used to extract the
significant features from the right and left hand Ulnar artery Doppler signals for diagnosing
the RA disease. RA disease has been diagnosed using an ANFIS classifier with model degrees
of 5, 10, 15, 20 and 25 for the MUSIC spectral analysis methods.
In this study, we developed an expert diagnostic system for the interpretation of the
right and left hand Ulnar artery Doppler signals using MUSIC spectral analysis and ANFIS
method. For right hand Ulnar artery, it can be seen in Table 3 that the model degree 20 of the
MUSIC method gives the highest degree of classification accuracy (95 %). For left hand
Ulnar artery, it can be seen in Table 4 that for a model degree of 10 the MUSIC method gives
the highest degree of classification accuracy 91.25 %.
The proposed method in this paper is a novel study related to diagnosis of RA disease
using right and left hand Ulnar artery Doppler signals belonging to healthy subjects and
patients. In the future, we will increase the number of patients and healthy subjects to further
vindicate the proposed method. Therefore, this work comprises a preliminary study. This
system can help physicians make final decisions for the early diagnosis of RA disease without
hesitation.
Acknowledgment
This work is supported by the Scientific Research Projects of Selcuk University.
16
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FIGURE CAPTIONS
Figure 1. Block diagram of the system hardware used to obtain Doppler data.
Figure 2. Doppler signals for a subject (no:12) with a healthy (a) right hand Ulnar artery
(b) left hand Ulnar artery.
Figure 3. Doppler signals for a patient (no:10) with RA disease on (a) the right hand Ulnar
artery (b) the left hand Ulnar artery.
Figure 4. AR sonograms developed for a subject (no:12) with a healthy (a) right hand Ulnar
artery (b) left hand Ulnar artery.
Figure 5. AR sonograms developed for a patient (no:10) having RA disease on (a) the right
hand Ulnar artery (b) the left hand Ulnar artery.
Figure 6. The flowchart of the classification systems.
Figure 7. ANFIS architecture with two inputs one output and two rules.
Figure 8. Locations of the 28 joints in our body.
Figure 9. ROC curves for model degrees of 5,10,15,20 and 25 of MUSIC spectral analysis
method on the early diagnosis of right hand Ulnar artery Doppler signals.
Figure 10. ROC curves for model degrees of 5,10,15,20 and 25 of MUSIC spectral analysis
method on the early diagnosis of left hand Ulnar artery Doppler signals.
TABLE CAPTIONS
Table 1. DAS 28, VAS, tenderness of joint number, had swollen of joint number, ESR and
CRP values of 40 RA patients.
Table 2. Distribution of patients according to DAS 28
Table 3. Obtained ten-fold Cross Validation average test results by ANFIS for classification
of right hand Ulnar artery Doppler signals.
Table 4. Obtained ten-fold Cross Validation average test results by ANFIS for classification
of left hand Ulnar artery Doppler signals.
22