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

1Ali Osman ÖZKAN, 2Sadık KARA, 3Ali SALLI 4Mehmet Emin SAKARYA and 5Salih GÜNEŞ*

1Selcuk University, Vocational College of Technical Sciences, 42003, Konya-Turkey

2Fatih University, Institute of Biomedical Engineering, 34500, Istanbul-Turkey

3Selcuk University, Meram Faculty of Medicine, Dept. of Physical Med. and Rehabilitation, Konya-Turkey

4Selcuk University, Meram Faculty of Medicine, Dept. of Radiology, Konya-Turkey

5Selcuk 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 0.3-

1.2 % 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.

Where is the mean frequency of the Doppler spectrum, is the frequency emitted by the

transducer, is the frequency of the returned echo, is the flow velocity, is the Doppler

angle and 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

ultrasound signals taken from the right and left hand Ulnar artery, and (iii) the classification

3

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

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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.

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

of sonograms from which medical information can be extracted [24-25]. A sonogram is

6

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plotted with the frequency components and PSD values sequenced on the timeline [26]. The

AR sonograms of healthy subjects are shown in Figure 4 and patients in Figure 5.

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.

3. Method

7

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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.

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].

The MUSIC method estimates the frequency content of a signal or autocorrelation

matrix using an eigen-space method. This method assumes that a signal, , consists of

8

Classification results RA disease or healthy

Classification using the ANFIS

Measurement of Doppler signals

Acquisition of right and left hand Ulnar arterial Doppler signals

Feature extraction processFeature extraction from right and left hand

Ulnar arterial Doppler signals using the MUSIC method

Classification of right and left hand Ulnar arterial Doppler signals as healthy and RA

disease using ANFIS

complex exponential in the presence of Gaussian white noise. Given an

autocorrelation matrix, , if the eigenvalues are sorted in decreasing order, the eigenvectors

corresponding to the largest eigenvalues spanning the signal subspace [27,28]. The

frequency estimation function for MUSIC is,

where are the noise eigenvectors and

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].

9W2

_

w1_

w2

w1

W2

_f2

A1

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y

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Layer 1 Layer 2 Layer 3 Layer 4 Layer 5

f

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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].

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

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

10

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:

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 Standard deviation

Maximum value

Minimum value

DAS 28 4.804 1.373 7.49 2.16VAS (mm) 51.5 19.81 80 10

Tenderness of joint number 10 9.526 28 1Swollen of joint number 1.3 1.689 7 0

ESR 33.2 18.34 75 3CRP 20.53 21.16 78.5 3

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

11

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

Values of DAS 28 Number of Disease Disease Situation

DAS 28 < 2,6 2 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

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

used. In our dataset, there are 40 patients with RA diseases and 40 healthy subjects. In totally,

12

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

where and 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.

Method Model Degree CA (%) SEN (%) SPE (%)

MUSIC

5 91.25 94.59 88.3710 87.5 87.5 87.515 90 90 9020 95 95 9525 88.75 91.89 86.05

Overall average 90.5 91.8 89.38

Table 4. Obtained ten-fold Cross Validation average test results by ANFIS for classification of left hand

Ulnar artery Doppler signals.

Method Model Degree CA (%) SEN (%) SPE (%)

MUSIC

5 87.5 87.5 87.510 91.25 90.24 92.3115 83.75 86.49 81.420 85 88.89 81.8225 90 90 90

Overall average 87.5 88.62 86.61

Figure 9. ROC curves for model degrees of 5,10,15,20 and 25 of MUSIC spectral analysis

14

AUCMusic - 5 0.913Music - 10 0.875Music - 15 0.888Music - 20 0.95Music - 25 0.888

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.

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 %.

15

AUCMusic - 5 0.875Music - 10 0.913Music - 15 0.838Music - 20 0.85Music - 25 0.9

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.

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