Fuzzy learning vector quantization particle swarm optimization (FLVQ-PSO) and fuzzy neuro generalized learning vector quantization (FN-GLVQ) for automatic early detection system of heart diseases base.

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2012 Proceedings of SICE Annual Conference

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Akita University, Akita, Japan

August 20-23, 2012

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Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy

Neuro Generalized Learning Vector Quantization (FN-GLVQ) for Automatic Early Detection

System of Heart Diseases based on Real-time Electrocardiogram

M. Febrian Rachmadi

1

, M. Anwar Ma’sum

1

, I Made Agus Setiawan

2

, and Wisnu Jatmiko

1

Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia (E-mail : muhammad.febrian@ui.ac.id, wisnuj@cs.ui.ac.id) 2

Computer Science Department, Udayana University, Bali, Indonesia

Abstract: Automatic heart beats classification has attracted much interest for research recently and we are interested to determine the type of arrhythmia from electrocardiogram (ECG) signal automatically. This paper will discuss thoroughly about study and implementation of FLVQ-PSO, an extension from FLVQ algorithm which use MSA and PSO method, and FN-GLVQ, an extension from GLVQ algorithm which use fuzzy logic concept, to classify ECG signals. By using 10-Fold Cross Validation, the algorithm produced an average accuracy 84.02%, 98.25%, 99.00%, and 97.70%, respectively for FLVQ, FLVQ-PSO, GLVQ, and FN-GLVQ.

Keywords: Arrhythmia Classification, Biomedical Signal Processing, FLVQ, FLVQ-PSO, FN-GLVQ, GLVQ.

1. INTRODUCTION

Coronary heart disease is currently listed as the most life-threatening disease in the world. Over 80% Cardiovascular Disease (CVD) occur in developing countries. In particular, the percentage of deaths caused by heart diseases and blood vessels in Indonesia increased from 9.1% in 1986 to 26.3% in 2001. Lack of proper medical devices for cardiac signal detection, such as cardiograph, was indicated as a cause for CVD to become the deadliest disease. In addition, limited number of cardiovascular specialists also contributes in this problem. As an illustration, the ratio between number of cardiologists in Indonesia and Indonesia’s total population reached 1:665.730 in 2011. Furthermore, medical devices from abroad make the cost of health care services become expensive, hence most of the people cannot get proper services.

The main objective in this research is to develop an automatic early detection system for heart diseases. This system can detect heart disease by its symptoms based on electrocardiogram (ECG) signal. It will be attached in a portable device, hence it can be brought along anywhere by people to monitor their current cardiac health. Android smartphone, which is connected to mini-ECG sensor, will be used as a hardware module in this portable device.

Many algorithms have been proposed for automatic classifier of life-threatening arrhythmia based on ECG data. There are a lot of works applying artificial neural network (ANN) and its variant as an arrhythmia detection based on ECG and some of them are combining wavelet transform, Principal Component Analysis, or Fuzzy C-Mean with ANN or LVQ-NN for classifying the signal [1], [2], [3]. Some researchers are also applying fuzzy theory on arrhythmia detection [4], [5], [6]. There are also others who use Support Vector Machine (SVM) as a classifier [7], or combining SVM with Genetic Algorithm [8] or combining SVM with Particle Swarm Optimization (PSO) [9]. In our previous study we applied Generalized Learning Vector Quantization (GLVQ) and Fuzzy Neuro Generalized

Learning Vector Quantization (FN-GLVQ) to classify arrhythmia beat types [10] using data from MIT-BIH arrhythmia database [11].

This system implements some of neural network algorithms, including Fuzzy Learning Vector Quantization (FLVQ) and Generalized Learning Vector Quantization (GLVQ). Furthermore, we also implement and use two neural network algorithms which are developed based on FLVQ and GLVQ, they are Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ). These four algorithms will classify any ECG signals detected by ECG sensor into classes which describe cardiac health. This paper will also discuss thoroughly about the study and implementation of FLVQ-PSO and FN-GLVQ to classify ECG signals.

The contribution of this research is an implementation of smart portable device for early detection system of heart diseases. Furthermore, this research also implements some learning algorithms such as FLVQ, GLVQ, FLVQ-PSO, and FNGLVQ for heartbeat classification. We also used real-time data from patient simulator which can generate human’s heartbeat signals.

The rest of this paper is organized as follows. Section 2 discusses how our early detection system of heart diseases is formed. The explanation in this section includes system’s architecture, system’s modules, and heartbeat data processing. Section 3 discusses algorithms which are used in the system. They are FLVQ-PSO and FNGLVQ. Section 4 shows all of experiments and results in this research, and section 5 draws a conclusion.

2. STATE OF THE ART: EARLY DETECTION

SYSTEM OF HEART DISEASES

2.1 System architecture

Early detection system of heart diseases is composed of hardware module and software module. The

August 20-23, 2012, Akita University, Akita, Japan

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-465-hardware module is composed of an electrocardiograph device, a digital circuit of microcontrollers, and a serial bluetooth adapter. This module has a function to capture human heartbeat signal and convert it to digital data whereas the software modules of this system are built based on Java platform and Android platform. Each of these software modules, either Java platform or Android platform, can visualize the human heartbeat wave which is captured by hardware module and perform classification to the heartbeat data into several classes of health condition of the heart. Both hardware module and software module are working together as a system.

2.2 Hardware Module

As described before, hardware module in this system is composed of an electrocardiograph device, a digital circuit of microcontrollers for analog to digital converter, and a serial bluetooth adapter. Electrocardiograph in this hardware module consists of several types of electronic circuits including two amplifiers (INA118 and OP07), three filters (one high pass filter and two low pass filter), and one adder. This hardware module is responsible for reading heartbeat from human body, converting heartbeat analog data to digital data, and passing all digital data to software module using serial bluetooth adapter.

2.3 Software Module

Software module in this system is built for computers and mobile devices. In the first version of software module, we use Java platform for computers and Android platform for mobile devices. Software module is used to visualize heartbeat wave taken from hardware module and to classify heart’s health condition. Software module also provides some information about classes of heart diseases, classifying methods, and user guide on how to use the application.

2.4 Heartbeat Signals Processing

In this research, we use The PS400 Patient Simulator from Fluke Biomedical Corporation to get real-time heartbeat data. This patient simulator can generate up to 12 classes of arrhythmia and 5 classes of normal heartbeat, which are based on the beat per minute (bpm). In this research, we use 10 classes of heartbeat data, they are;

1) Right Bundle Branch Block (RBBB), 2) Premature Atrial Contraction (PAC), 3) Premature Ventricular Contraction (PVC), 4) Ventricular Tachycardia (V-Tach), 5) Ventricular Fibrillation (V-Fib), 6) Paced (P),

7) Atrial Fibrillation (A-Fib), 8) Normal beat with 120 bpm, 9) Normal beat with 180 bpm, and 10) Normal beat with 240 bpm.

Heartbeat data that have been caught by the hardware module have 8 bits integer data format and range in value from 0 up to 255. Each of these human heartbeat data has 850 sample points. All of heartbeat data are

sent continuously without any separator. These integer data will be converted to floating point format number which data ranges from -1 to 1. This conversion process uses Eq. (1).

݂݈݋ܽݐ݅݊݃݌݋݅݊ݐ ൌ ቀ௜௡௧௘௚௘௥ൈଶ_ଶହହ ቁ െ ͳ (1)

Continuous heartbeat data will be separated from each other, so each data will represent a human heartbeat. The separation process is conducted by searching for the culmination of human heartbeat or R-point. Because of a human heartbeat has 850 sample points, we search and approximate the R-point for every beat and get 424 points to the left of R-point and 425 points of the right of the R-point.

After the separation process, each heartbeat will be transformed by using wavelet algorithm. Wavelet algorithm is used to simplify heartbeat data so each of human heartbeat just has 55 sample points, but it does not change the shape of the heartbeat wave. Mother wavelet that we used in this research is Daubechies order 4 level 4. Heartbeat data that have been obtained after 4 level wavelet processes will be the data input for training process of artificial neural network algorithms.

3. ALGORITHM

3.1 Fuzzy Learning Vector Quantization Particle Swarm Optimation (FLVQ-PSO)

Fuzzy Learning Vector Quantization (FLVQ) is a pattern recognition algorithm which is developed from Learning Vector Quantization (LVQ) algorithm. FLVQ uses fuzzy theory in initialization process of initial reference vectors, training process, and determination of winning reference vectors. Using these two methods, LVQ and fuzzy, FLVQ has advantages such as fast computation and high rate of pattern recognition just like backpropagation.

Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) is an algorithm which is developed from FLVQ and combines main concept from Matrix Similarity Analysis (MSA) and Particle Swarm Optimization (PSO). Differences between FLVQ and FLVQ-PSO happen when these two algorithms are doing the training process. In the training process, reference vectors in FLVQ-PSO are updated using both of FLVQ training method and PSO method respectively. FLVQ has several clusters rather than has several hidden layers. These clusters are used as particles in PSO algorithm.

FLVQ-PSO has several advantages from additional applied methods. The advantages are the ability to do fast computation from FLVQ method, to determine fitness value with MSA, and to determine optimal solution with PSO. In subsections follow, we will discuss about combining FLVQ with MSA and combining FLVQ with PSO.

3.1.1 FLVQ with MSA

In FLVQ training process, the algorithm stops when

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-466-maximum number of epoch is reached. Unfortunately, there are some probabilities where FLVQ yields non-optimal solution at the end of final epoch or FLVQ has reached optimal solution when the maximum number of epoch is not reached yet. To optimize number of epoch, we need an analysis method to determine when the algorithm should stop the training process. MSA can be used to determine average value of reference vectors in each of epochs. The average value of reference vectors in matrix similarity will determine how well the reference vectors that are yielded by the training process in a particular epoch. An ideal condition for FLVQ to stop its training process is when the value of MSA is as close as an identity matrix. We completion of epoch. The value of similarity matrix can be obtained by using Eqs. (2) ~ (3) below,

݉௜௝ൌ _ேଵσே௞ୀଵܿ௜௝ǡ ݇ ൌ ͳǡ ʹǡ ǥ ǡ ܰ (2)

ܿ௜௝ൌ ൜ͳ ݂݅ݔ߳ܥ_{Ͳ ݋ݐ݄݁ݎݓ݅ݏ݁}௜݈ܽ݊݀ܿܽݏݏ݂݅ݕሺݔሻ ൌ ܥ௝ (3)

where i is input class, j is output class, N is number of input vectors for one class, x is one input vector, and Cj

is a member of class j.

All of reference vectors will be computed using Eq. (2) and yield a similarity matrix for FLVQ. Eq. (4) shows the form of similarity matrix of MSA,

ൌ ൥ڭଵଵ ڮ ڰ ڭ୬ଵ

ଵ୬ ڮ ୬୬

൩ (4)

where M is similarity matrix with ݊ ൈ ݊ of size and ݊ is number of output classes.

3.1.2 FLVQ with PSO

Particle Swarm Optimization (PSO) was introduced by R. C. Eberhart and J. Kenndey (1995) [12]. PSO is an algorithm to search optimum solutions from a particular problem. There are two key elements in PSO, they are particles and solutions. In FLVQ-PSO algorithm, particles are clusters and the solutions are reference vectors.

FLVQ-PSO is developed to reduce FLVQ algorithm dependency on initialization of initial reference vectors. Initialization of initial reference vectors in FLVQ-PSO is conducted by creating initial reference vectors as much as the clusters. This initialization is conducted randomly based on the output class of the input vector,

so we can get a good initial reference vectors.

FLVQ-PSO uses fitness value to determine local best and global best for each cluster. Fitness value is obtained from MSA where the fitness value is sum of value in the main diagonal of matrix similarity of MSA minus sum of value in the non-main diagonal of similarity matrix of MSA. Suppose a similarity matrix of MSA is formed of mij elements where i and j is

integer from 1 to n, and size of similarity matrix of MSA is n x n, so fitness value for the k-th cluster is obtained from Eq. (5) below.

݂݅ݐ݊݁ݏݏ௞ ൌ σ௡௜ୀଵ݉௜௜െൣσ௜ୀଵ௡ σ௝ୀଵ௡ ݉௜௝݂݅݅ ് ݆൧ (5)

In PSO algorithm, each of particles has local best and global best, so has FLVQ-PSO. Local best and global best is used to update reference vectors. Local best in FLVQ-PSO is the best reference vector from each of clusters, whereas global best is the best reference vector from local best which has the best fitness value. If the old fitness value of reference vector in one cluster is better than the new one, the old one is preserved as a reference vector rather than using the new one. Whereas the new fitness value of reference vector in one cluster is better than the old one, the new one will substitute the old one. Global best and local best can be obtained by using Eqs. (6) ~ (7) below,

ܩ ൌ ሺܮሻ (6)

ܮ ൌ ሺ݂݅ݐ݊݁ݏݏଵǡ ݂݅ݐ݊݁ݏݏଶǡ ǥ ǡ ݂݅ݐ݊݁ݏݏ௞ሻ (7)

where G is the global best and L is a group of local best from each of clusters.

3.1.3 Reference Vectors Update Processes

As described at the previous section, reference vectors in FLVQ-PSO are updated using both of FLVQ training method and PSO method. First of all, we update the reference vectors using Eqs. (8) ~ (9) below,

ݓ௜௝ሺݐ ൅ ͳሻ ൌ ݓ௜௝ሺݐሻ ൅ ݒ௜ሺݐ ൅ ͳሻ (8) update process which is experienced by reference vectors.

Velocity vector of particle affects how big particle will move from its initial position. Some other aspects

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-467-that affect velocity value of particle are cognitive value and social value. Suppose cognitive value of a particle is greater than social value, then the particle tends to move closer to its local best. If social value is greater than cognitive value, then the particle tends to move closer to its global best.

After average value of reference vectors are updated, minimum value and maximum value of reference vectors can be updated using Eqs. (10) ~ (12) below,

݀௜௝ൌ ݓ௜௝ሺݐሻ െ ݓ௜௝ሺݐ ൅ ͳሻ (10)

ݓሺ݈ሻ௜௝ሺݐ ൅ ͳሻ ൌ ݓሺ݈ሻ௜௝ሺݐሻ ൅ ݀௜௝ (11)

ݓሺݎሻ௜௝ሺݐ ൅ ͳሻ ൌ ݓሺݎሻ௜௝ሺݐሻ ൅ ݀௜௝ (12)

where ݓሺ݈ሻ_௜௝ is the minimum value of a reference vector, ݓሺݎሻ_௜௝ is the maximum value of a reference vector, ݐ is a state before update process, ݐ ൅ ͳis a state after update process, and ݀_௜௝ is the difference value between old average value and new average value of reference vector.

Fig.1 Illustration of the velocity vector calculation in FLVQ-PSO.

3.2 Fuzzy Neuro Generalized Learning Vector Quantization (FNGLVQ)

On previous study [10], I Made Agus et al. introduce an extension of GLVQ, which employed fuzzy theory as discriminant function. This method did not use crisp value but fuzzy membership function and as the reference vector. This algorithm approach adopting Fuzzy-Neuro LVQ that developed by Kusumoputro Budiarto and Jatmiko W[13]. The conceptual architecture of FNGLVQ as described on Fig. 2.

The result value of discriminant function replaced with similarity value on fuzzy concept. Each of crisp input value is feed into the network. Reference vector is formed by membership function that represents the distribution for each feature. We use triangular function as membership function. The similarity value between each crisp input and reference vector are calculated by seeking the degrees of membership of each feature to each membership function. GLVQ's winner-take-all rule

Fig. 2 Illustration of FN-GLVQ algorithm for ECG classification.

applied to the average of membership degree or similarity value for each reference vector. The membership function define as ݄_௜௝ሺݔሻ with ݅ ൌ ݂݁ܽݐݑݎ݁ and ݆ ൌ ܿܽݐ݁݃݋ݎݕ.

ߤ௜௝ൌ ݄௜௝ሺݔሻ (13)

The similarity for each reference vector (ߤ_௝) then propagated to next neuron using average operation as shown in Eq. 14

ߤ௝ൌଵ_௞σ௞௜ୀଵߤ௜௝ (14)

To determine the winner (ݓ_௣) in winner-take-all rule, we choose maximum of similarity value (ߤ_௝) using Eq. 15.

ݓ௣ൌ ௝ሺߤ௝ሻ (15)

In FNGLVQ, the update process of the reference vector is not defined by the winner vector, but it is defined by minimum classification error (MCE) as pointed out in Eq. 16.

߮ሺݔሻ ൌ ௗభିௗమ

ௗభାௗమ (16)

We need to complement similarity value into ݀ ൌ ͳ െ ߤ, in which ݀ is dissimilarity. Later on, we substitute it into Eq. 16 and we will obtain Eq. 17.

߮ሺݔሻ ൌ ఓమିఓభ

ଶିఓభିఓమ (17)

where ߤ_ଵ is similarity value between input vector (ݔ) with reference vector from the same category (ܥ_௫ൌ ܥ_௪), ߤଶ is the greatest similarity value between ݔ with

reference vector that are not from the same category as input vector (ܥ_௫് ܥ_୫ୟ୶_ೕ_ሺ௪_ೕ_ሻ). Adjustments are made by similarity term and based on steepest descent method, so we have the derivative of ܵ as shown in Eq. 18.

ఋௌ ఋ௪೔ൌ

ఋௌ ఋఝǤ

ఋఝ ఋఓ೔Ǥ

ఋఓ೔

ఋ௪೔ǡ ݅ ൌ ͳǡ ʹ (18)

where ఋఝ

ఋఓభ and

ఋఝ

ఋఓమ are the derivative of MCE. To obtain ఋఝ

ఋ௪೔ depends on the chosen membership function. In case of triangular function with reference vector

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-468-ݓ௜௝ൌ ሺݓ௠௜௡೔ೕǡ ݓ௠௘௔௡೔ೕǡ ݓ௠௔௫೔ೕሻ ,the membership

function can be defined as Eq. 19.

ߤ ൌ ݄ሺݔǡ ݓ௠௜௡ǡ ݓ௠௘௔௡ǡ ݓ௠௔௫ሻ

Therefore, the derivative of the triangular function against the average weight (ݓ_௠௘௔௡) lead to three conditions and hence the learning rules can be described as Eqs. (20) ~ (24). of ߙ will be decreasing along with iteration.

ߙሺݐ ൅ ͳሻ ൌ ߙሺݐሻൈቀͳ െ_௧ ௧

೘ೌೣቁ (27)

To gain a better recognition performance, we perform additional adjustment in term of the width of reference vector fuzziness through following rules.

• If ߤଵ൐ Ͳߤଶ൐ Ͳ, at least one of two

reference vectors recognize the input, so

- if recognize correctly (߮ ൏ Ͳ) then increase the fuzziness using Eqs. (28) ~ (29).

ݓ௠௜௡ ՚ ݓ௠௘௔௡െ ሺݓ௠௘௔௡െݓ௠௜௡ሻ

ൈ ሺͳ ൅ ሺߚ ൈ ߙሻሻ (28)

ݓ௠௔௫ ՚ ݓ௠௘௔௡൅ ሺݓ௠௔௫െݓ௠௘௔௡ሻ

ൈ ሺͳ ൅ ሺߚ ൈ ߙሻሻ (29)

- if recognize wrongly (߮ ൒ Ͳ) then decrease the fuzziness using Eqs. (30) ~ (31).

ݓ௠௜௡ ՚ ݓ௠௘௔௡െ ሺݓ௠௘௔௡െݓ௠௜௡ሻ

ൈ ሺͳ െ ሺߚ ൈ ߙሻሻ (30)

ݓ௠௔௫ ՚ ݓ௠௘௔௡൅ ሺݓ௠௔௫െݓ௠௘௔௡ሻ

ൈ ሺͳ െ ሺߚ ൈ ߙሻሻ (31)

• If ߤଵൌ Ͳܣܰܦߤଶൌ Ͳ, it means that both

reference vectors cannot recognize the input, so all of reference vectors fuzziness are

4. EXPERIMENT AND RESULT

In this experiment, we use a dataset which consist of 100 heartbeat data from each of 10 classes, so in total we use 1000 heartbeat data. These 1000 data are generated from The PS400 Patient Simulator from Fluke Biomedical Corporation and processed in heartbeat signals processing which are discussed in Section 2. Comparison was performed by using FLVQ, FLVQ-PSO, GNLVQ, and FN-GLVQ. We configured the classifier with the training parameters as can be seen in Table 1.

Table 1. Parameters for the learning process.

Algorithm ߙ Max epoch

FLVQ 0.005 5

FLVQ-PSO 0.005 1

GLVQ 0.05 250

FN-GLVQ 0.05 250

In order to evaluate all of learning methods, we tested them with 10-Fold Cross Validation_{. From Table 2, we}

can see that MSA method and PSO method in FLVQ-PSO (98.18%) make a huge impact to the FLVQ (84.02%) learning method in term of accuracy. In the other side, we can see in Table 3 that FN-GLVQ has better error rate in training process (0.007590056) rather than GLVQ (0.02666667).

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-469-Table 2.

Performance result using 10-Fold Cross Validation.

Fold FLVQ FLVQ-PSO GLVQ FN-GLVQ

Table 3. Error rate of GLVQ and FN-GLVQ.

Algorithm Error Rate In Training

GLVQ 0.026666667

FN-GVLQ 0.007590056

5. CONCLUSION

We have presented in this paper an extension of FLVQ and an extension of GLVQ, which are FLVQ-PSO and FN-GLVQ to improve capability of the system for determining arrhythmia category. We train our dataset using FLVQ, FLVQ-PSO, GLVQ, and FN-GLVQ. From our experiment we found that MSA method and PSO method in FLVQ-PSO can increase the accuracy of classifier compared with original FLVQ. In other hand, FN-GLVQ has better error rate in training proses compared with GLVQ. By using 10-Fold Cross Validation_{, the algorithm produced an average accuracy} 84.02%, 98.25%, 99.00%, and 97.70%, respectively for FLVQ, FLVQ-PSO, GLVQ, and FN-GLVQ.

ACKNOWLEDGMENT

This work is supported by Competitive Research Grant 2010 University of Indonesia No. DRPM/Hibah Riset Kompetensi Universitas Indonesia/2010/I/10246. Besides that, this research is also supported by Grant of Joint Research for Foreign Affairs and International Publication No. 1495/E5.2/PL/2011 by the Ministry of Education, Republic of Indonesia.

REFERENCES

[1] T. Yeap, F. Johnson, and M. Rachniowski, “Ecg beat classification by a neural network,” in Engineering in Medicine and Biology Society, 1990., Proceedings of the Twelfth Annual International Conference of the IEEE, pp. 1457-1458, nov 1990.

[2] Y. Ozbay and B. Karlik, “A recognition of ecg

arrhytihemias using artificial neural networks,” in Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, vol. 2, pp. 1680-1683 vol.2, 2001.

[3] R. Ceylan and Y. Ozbay, “Comparison of fcm, pca

and wt techniques for classification ecg arrhythmias using artificial neural network,” Expert Syst. Appl., vol. 33, pp. 286-295, August 2007.

[4] T. P. Exarchos, M. G. Tsipouras, C. P. Exarchos, C. Papaloukas, D. I. Fotiadis, and L. K. Michalis, “A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree,” Artif. Intell. Med., vol. 40, pp. 187-200, July 2007.

[5] B. Anuradha and V. C. V. Reddy, “Cardiac

arrhythmia classification using fuzzy classifiers,” Journal of Theoretical and Applied Information Technology, 2008.

[6] C. C. Yeh Y.-C., Wang W.-J., “Heartbeat case determination using fuzzy logic method on ecg signals, ”International Journal of Fuzzy Systems, 2009.

[7] H. Zhang and L.-Q. Zhang, “Ecg analysis based on

pca and support vector machines,” in Neural Networks and Brain, 2005. ICNN B ’05. International Conference on, vol. 2, pp. 743-747, oct. 2005.

[8] J. A. Nasiri, M. Naghibzadeh, H. S. Yazdi, and B. Naghibzadeh, “Ecg arrhythmia classification with support vector machines and genetic algorithm,” in Proceedings of the 2009 Third UKSim European Symposium on Computer Modeling and Simulation, EMS ’09, (Washington, DC, USA), pp. 187-192, IEEE Computer Society, 2009.

[9] F. Melgani and Y. Bazi, “Classification of

electrocardiogram signals with support vector

machines and particle swarm

optimization, ”Information Technology in

Biomedicine, IEEE Transactions on, vol. 12, pp. 667-677, sept. 2008.

[10]Setiawan, M.A.; Imah, E.M.; Jatmiko, W.;,

"Arrhytmia classification using Fuzzy-Neuro

Generalized Learning Vector Quantization,"

Advanced Computer Science and Information System (ICACSIS), 2011 International Conference on , vol., no., pp.385-390, 17-18 Dec. 2011.

[11]G. B. Moody, “Mit-bih arrhythmia database.”

http://physionet.org/physiobank/database/html/mitd bdir/mitdbdir.htm. May 1997.

[12]Kennedy, J., Eberhart, R., "Particle swarm

optimization," Neural Networks, 1995.

Proceedings., IEEE International Conference on, vol.4, no., pp.1942-1948 vol.4, Nov/Dec 1995. [13]B. Kusumoputro, H. Budiarto, and W. Jatmiko,

“Fuzzy-neuro lvq and its comparison with fuzzy algorithm lvq in artificial odor discrimination system,”ISA Transactions, vol. 41, no. 4, pp. 395-407, 2002.