In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other.
As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.
 C. M. Bishop, Neural networks for pattern recognition, Oxford: Oxford University Press, 1995.
 G. Dreyfus, Neural networks: methodology and applications, Berlin: Springer, 2005.
 J. M. Zurada, Introduction to artificial neural systems, St. Paul: West Publishing Company, 1992.
 Q. Bai, "Analysis of particle swarm optimization algorithm," Computer and Informatin Science, vol. 3, no. 1, 2010.
 C.R. Hema, M.P. Paulraj, R. Nagarajan, S. Yaacob and A.H. Adom, "Application of Particle Swarm Optimization for EEG Signal Classification," Biomedical Soft Computing and Human Sciences, vol. 13, no. 1, 2008, pp. 79-84.
 B. Doğan, "Parçacık Sürü Optimizasyonuna Dayalı Yeni Bir Aritmi Sınıflama Yöntemi," MasterThesis, Technical University of Istanbul, 2009, Istanbul.
 G. Yan, W. Yaoguang, F. Dongmei, Z. Wei and N. Shurong, "Research and Application of a New Artificial Immune Algorithm Which Based on SOM Neural Network," IEEE, 2006, pp. 1080-1083.
 H. Kahramanlı and N. Allahverdi, "Extracting rules for classiﬁcation problems: AIS based approach," Expert Systems with Applications, vol. 36, 2009, pp. 10494–10502.
 R. Ceylan, "Özellik Çıkarma Teknikleri Ve Yapay Sinir Ağları Kullanarak Bir Tele-Kardiyoloji Sistem Tasarımı," Phd Thesis, University of Selcuk, 2009, Konya.
 I. Daubechies, "The wavelet transform, time frequency localization and signal analysis," IEEE Transactions on Information Theory, vol. 36, no. 5, 1990, pp. 961-1005.
 K. Englehart, B. Hudgin and P.A. Parker, "A Wavelet-based continuous classification scheme for multifunction myoelectric control," IEEE Transactions on Biomedical Engineering, vol. 48, 2001, pp. 302–311
 I. Daubechies, "Orthonormal bases of compactly supported wavelets," Commun. Pure Appl. Math., vol. XLI, 1988, pp. 909–996.
 S.G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, 1989, pp. 674–693.
 A. Cohen and J. Kovacevic, "Wavelets: the mathematical background," Proceeding of the IEEE, vol. 84, 1996, pp. 514–522.
 M. Karabatak and M. İnce, "An expert system for detection of breast cancer based on association rules and neural network," Expert Systems with Applications, vol. 36, 2009, pp. 3465-3469.
 G. Tezel, "Classification Of Biomedical Signals Via A New Artificial Neural Network With Adaptive Activation Function," Phd Thesis, University of Selcuk, 2007, Konya.
 X. Wu, "A density adjustment based particle swarm optimization learning algorithm for neural network design," IEEE, 2011.
 S. Yucelbas, " Diagnosis Of The Heart Rhythm Disorders By Using Hybrid Classifiers," Master Thesis, University of Selcuk, 2013, Konya.
 S. Ozsen, "Development Of A Problem-Based Artificial Immune System For Biomedical Classification Problems And Application On Biomedical Classification Problems," Phd Thesis, University of Selcuk, 2008, Konya.
 C. Yucelbas, "Design Of An Artificial Immune System With Ellipsoidal Recognition Balls And Performance Analysis Of It In Classification Problems," Master Thesis, University of Selcuk, 2012, Konya.