Diabetic diagnose test based on PPG signal and identification system

doi:10.4236/jbise.2009.26067

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Vol.2, No.6, 465-469 (2009)

doi:10.4236/jbise.2009.26067

JBiSE

Diabetic diagnose test based on PPG signal and

identification system

Hadis Karimipour1, Heydar Toossian Shandiz1, Edmond Zahedi2

1School of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran; 2School of Electrical Engineering, Sharif

University of technology, Tehran, Iran.

Email: [email protected]; [email protected]

Received 11 May 2009; revised 29 June 2009; accepted 6 July 2009.

ABSTRACT

In this paper, photoplethysmogram (PPG) sig-

nals from two classes consisting of healthy and

diabetic subjects have been used to estimate

the parameters of Auto-Regressive Moving Av-

erage (ARMA) models. The healthy class con-

sist s of 70 healthy and the diabetic classes of 70

diabetic patients. The estimated ARMA parame-

ters have then been averaged for each class,

leading to a unique representative model per

class. The order of the ARMA model has been

selected as to achieve the best classification.

The resulting model produces a specificity of

%91.4 and a sensitivity of, %100. The proposed

technique may find applications in determining

the diabetic state of a subject based on a

non-invasive signal.

Keywords: PPG Signal; Diabetic; Identification;

ARMA Model

1. INTRODUCTION

Diabetes has been recognized as fourth leading cause of

death in developed countries. Prediction based on re-

corded data in health centers worldwide shows that it is

reaching epidemic proportions in many developing and

newly industrialized nations [1].

When the body has difficulty regulating the amount of

glucose in the blood stream Diabetes Mellitus has been

occurred. Rising of the blood sugar lev el which will lead

to hyperglycemia or hypoglycemia is due to the glucose

accumulates in the bloodstream [2-3]. Glucose level

above 150- 160mg/dl for long time poses significant

health risk with possible long lasting effect [4]. Easy,

low cost and on time recognizing diabetic with simple

method and portable technology for the primary care and

community-based clinical settings is the main goal of

researchers in this area. The PPG technology has been

used in a wide range of commercially available medical

devices for measuring oxygen saturation, blood pressure

and cardiac output [5]. Due to change in glucose level,

the amount of blood volume in the figure changes, this

variation can be measured by PPG. When a fixed source

of infrared radiation is used, the variation of blood vol-

ume act as a phototransistor and the receive signal is

changed. This is why we use the PPG signal for recog-

nizing the diabetic. In this work by filtering on pho-

toplethysmography (PPG) signal and estimate ARMA

model for healthy and patient, a method for recognizing

diabetic is proposed. Field data shows this method work

properly.

2. METHODOLOGY

Identification systems methods are the best way for find-

ing mathematic description of a black box. Figure 1

shows such a system in which only input and output ter-

minals are introduced.

If there is no a noise sourc e insi d e th e s ys te m or me as -

urement input and output are noise free, the number of

unknown parameters in the system can determine the

number of required measurements. As the real systems

mathematically describe with finite parameter, every-

body can determine the mod el easily. Perturbation in the

system parameter and noise in the measurements lead to

parameter estimation of the system.

Figure 2 shows the flow chart of each system identi-

fication method. The prior knowledge is used in all part

of the model calculation. Based on the prior knowledge

the experiment is set up to produce the data. The data

have to be as much as informative. The observer adjusts

the frequency content and amplitude of the input signals

in the system with external input or chose the probab ility

density function (PDF) in the system with unknown in-

put.

Figure 1. A black box system.

H. Karimipour et al. / J. Biomedical Science and Engi neering 2 (2009) 465 -469

466

Figure 2. System identification flow chart.

Figure 3. Modeling disturbance.

Choosing model set is second step in system identifi-

cation based on prior knowledge. Different set of model

is chosen, by considering number of input and output,

linearity or nonlinearity, coupling or uncoupling between

inputs and outputs, discrete or continues in time, fre-

quency domain or time domain, application of calculated

model for simulation, simplifying, controlling or inverse

engineering.

Figure 3 shows a linear, time invariant, single input

and output (SISO) model in which disturbance in system

parameters and noise in measurement are modeled as

additive in output [6]. This kind of model set is de-

scribed as:

 

tvktubktyaty ba n

k   01



(1)

In which is output and is input,







tv is

disturbance which is modeled as:

 







kktectv

(2)

where,





te is white noise .

The produced data are used to calculate the coeffi-

cients and of the model.

ii ba ,i

Next step is using criteria. Least square mean error

(LSE) is used as the positive and negative error is the

same and small error becomes smaller and big error be-

come much bigger. The model rewrite as follow:













tty



(3)

In which data are put in





and



contain all un-

known parameter.

As the data is mixed with the measurement noise only

an estimation of



can be calculated. There are many

criteria, we chose minimization of error between real

output and model output as follow:







Nte



(4)

In which N

is input - outp ut m easured data and













ttyte 



(5)

Therefore



(6)



ZV ,

min

arg







The last step in model estimation is model validation.

If in some sense the output of the model is fitted on the

output of the system the estimated model is accepted

otherwise the process is repeated again.

Model in Figure 3, is called autoregressive moving

average extra input (ARMAX). Autoregressive means





ty depend on previous amount of it. X stand for ex-

ternal input





tu and MA stand for moving average refer

to last term in Figur e 3.

In the following subsections the proposed method is

explained.

2.1. Model Selection

As there is no exact information about causes which

affected PPG signal, ARMA model is used to model

healthy and patient. The output of such system called

time series. Figur e 4 shows such systems.

In order to modeling the system mathematically equa-

tion 1 reduced t o 7:

Figure 4. Modeling time series.

H. Karimipour et al. / J. Biomedical Science and Engi neering 2 (2009) 465 -469

467

 

 



ca n

kktecktyaty

(7)

In which is PPG signal as output and white

noise whit zero mean and variance





as input.

This is like other time series analysis, such as vocal sys-

tem, weather system, in which an effect without cause is

in our hand.

The process of finding model is as follow:

Each patient and healthy data is used to calculate an

ARMA model individually then the average of all mod-

els for each group is evaluated as the ARMA model for

that category. After testing polynomials with deference

dimensions, it was found that the 11 15



ca nandn

10 15

for patient model’s parameter and



ca nandn

for healthy model’s parameter, give the best result.

(a)

(b)

Figure 5. (a) recorded PPG signal and (b) one stable part with

1000 sample of it.

Figure 6. Result of applying moving average on healthy

PPG signal.

(a)

(b)

Figure 7. Result of applying (a) Patient PPG signal and

(b) healthy PPG signal on the healthy model.

H. Karimipour et al. / J. Biomedical Science and Engi neering 2 (2009) 465 -469

468

(a)

(b)

Figure 8. Result of applying (a) Patient PPG signal

and (b) healthy PPG signal on the patient model.

70 7580859095100

100

fitness on healthy model

f i t ness on pati ent m odel

healt hy signal

pati ent si gnal

Figure 9. Result of applying PPG signal on the patient and

healthy model.

2.2. Experimental Setup

There are two array groups of PPG signals. Pathologic

arrays contain data from all subject tested in th e hospital

who were diabetic (39-64 years ages). Healthy arrays

contain all data from healthy subjects (22-52 years old).

Figure 1 shows a recorded PPG signal for a diabetic

patient .

In each file, the only variable is a (50x24750) which is

the raw PPG data:

- Each row is one particular lead.

- First column: subject number: Sb

- Second column: lead number: Ld

- Third column: Age

- Fourth column up to end: raw PPG data

- The length of the files has been limited to 90 sec

(sampled at 275 Hz, this gives 24750 sample points)

- The number of rows (records) has been limited to 50

per file to limit file size

- The format of the data is uint32 to save on space.

2.3. Criteria and Model Validation

The MATLAB identification toolbox is used to calculate

model parameter. The LSE is used as criteria and output

matching as model validation.

3. PRACTICAL RESULTS

Figure 5 shows a sample of PPG signal of healthy and

patient which is recorded in a hospital.

The additive noise corrupts the PPG signals. Moving

average filter is used to remove disturbance from signal.

Figure 6 shows output of the filter.

Figure 7 and 8 show the result of applying Healthy

and patient PPG signal on estimated model for healthy

and patient samples.

There are two areas in Figure 9, if data fit on healthy

model better than patient model, there is a point under

the line (subject is healthy) an d inverse .the point on the

line shows possibility of wrong classification.

As Table 1 shows the proposed method can calcify

healthy person and patient by 100% and 94%, respec-

tively.

Simulation result shows that, specificity, sensitivity,

negative predictive value (NPV) and positive predictive

Table 1. Contingency table.

H. Karimipour et al. / J. Biomedical Science and Engi neering 2 (2009) 465 -469

469

value (PPV) is % 91.4, % 100, % 100, and % 92.1

spectfully.

4. CONCLUSIONS

The proposed technique using non-invasive PPG signal

is able to separate healthy subjects from pats using

an ARMA model. One potial applicatn of these

eeded to be injected to diabetic people.

MENTS

ussels:

http://www.eatlas.idf.org/webdata/docs/Atlas%202003-S

ummary.pdf.

re-[2] H. L. Wee, H. K. Ho, and S. C. Li, (2002) Public

awareness of diabetes mellitus in singapore, J .Singapore

[3] Romanillos Palerm, (2003) Drug infusion

Med 43(3), 128–134.

Cesar Carlos

s control: An extended direct model reference adaptive

control strategy, PhD thesis, Rensselaer Polytechnic

Institute, Tro y, N ew Y

tien

ioten ork.

[4] V. Carmen Doran, H. Nicolas Hudson, T. Katherine, J.

Moorhead, Geoffrey Chase, M. Geoffrey Shaw, and E.

Chris Hann, (2004) Derivative weighted active insulin

control modelling and clin

models could be to estimate the state of diabetes of pa-

tients and eventually be used in controlling the amount

of insulin nical trials for ICU patients,

Elsevier ju, Medical Engineering & Physics.

[5] V. K. Jayasree, T. V. Sandhya, and P. Radhakrishnan

(2008) Non-invasive studies on age related parameters

using a blood volume pulse sensor, Measurment Science

Reveiw, 8(4), Section 2.

5. ACKNOWLEDGE

The authors gratefully acknowledge the PPG signals kindly provided

by the Faculty of Engineering and Built Environment, University Ke-

bangssan Malaysia.

REFERENCES

[1] D. Gan, editor, (2003) Diabetes atlas, 2nd Edition, Br

Openly accessible at

International Diabetes Federation,

[6] L. Ljung, (1999) System identification: theory for user,

Second edition, Prentice Hall.