1. Data fusion classification method based on Multi agents system
Elhoucine Ben Boussada1, Mounir Ben Ayed2, Adel M.Alimi1
1 Computer Engineering and Applied Mathematics Department
National Engineering School Sfax, Tunisia
2 Computer Science and Communication department
Faculty of Sciences Sfax, Tunisia

2. Data fusion classification method based on Multi agents system
Introduction
Architecture of the system
Experiment results
Conclusion

3. Introduction.
The analysis of electrocardiogram (ECG) has an important role in diagnosing the heart diseases.
The automatic detection of cardiac arrhythmias beats in an ECG provides clinicians information about the patient health.
The bibliographic study of some recent researches shows that we can distinguish two techniques for the ECG classification.
The first type is based on one method of classification
The second type is based on the combination of two or more methods of classification.
The Combining is an approach to improve the performance in classification particularly for difficult problem and those that are high precision. We start with this idea and we said why do not make a multi-agents system with the many combining methodology.

4. Cooperative Intelligent Agents System for ECG Arrhythmia Classification
The main purpose of the proposed system is to classify five types of beat classes of arrhythmia which are: Normal beat (N), Supra Ventricular Ectopic Beat (SVEB), Ventricular Ectopic Beat (VEB), Fusion beat (F), and Unknown beat (Q).
A complete system for ECG classification can be divided in to four subsequent categories here in this work we are interested for the forth step (i.e., the classification)
but the first three steps have significant importance.

5. ECG Pre-processing
An ECG signal consists of three basic waves: P, QRS and T. These waves correspond to a trial depolarization (P wave), ventricular depolarization (QRS waves), and ventricular repolarization (T wave). In the most of literature who treat the problem of ECG classification, authors has based on QRS complex or on RR interval .
Figure 1 shows an example of normal ECG signal.

6. ECG system classifier
the result of classification step is the most important output that measures the accuracy, the sensitivity and specificity of the system. Figure 2 shows the proposed system for ECG classification.

7. The system architecture

8. Table 2. Classification results
The scheduling process
CAg receives a vector of ECG beat and affects a key for each vector.
The CAg request the vector of ECG beat with his key to the three agent’s classifier; also inform (by message) the SAg that the key number of ECG beat vector has transmitted to the agent’s classifier.
Each agent’s classifier begins the classification process and requests his result, to SAg, when finished.
When SAg receive a request from CAg, it records the key and the time, which stopped when it receive the result from each agents classifier.
Finally the decision is gives by the SAg according to a rule base.
The rule base consists as a comparison of three parameters. Which are: Accuracy, Sensitivity and Specificity Also SAg provides a forth parameter witch matching the scheduling time of the classifier.
SM-Ag
MB-Ag
SB-Ag
Accuracy (%)
98.17
96.06
96.21
Specificity (%)
99.07
94.15
92.45
Sensitivity (%)
98.05
95.24
96.19
Table 2. Classification results

10. Conclusion
In this presentation we proposed a multi-data source fusion agent based method for ECG classification. However this approach offers complementary information about the ECG state.
The general architecture of this system was described .
For the simulation we used the ECG records from MIT-BIH data base.
In this study five classes were used.
The result gives high accuracy, specificity and sensitivity of SM-ag.
It is found that this system have an acceptable performance with can be increased by the application of the PSO algorithm.
Although the proposed system can be more developed and compared with others classifier for the same data, all of which are the subject to our future work.