@article{(Open Science Index):https://publications.waset.org/pdf/2239,
	  title     = {MITAutomatic ECG Beat Tachycardia Detection Using Artificial Neural Network},
	  author    = {R. Amandi and  A. Shahbazi and  A. Mohebi and  M. Bazargan and  Y. Jaberi and  P. Emadi and  A. Valizade},
	  country	= {},
	  institution	= {},
	  abstract     = {The application of Neural Network for disease
diagnosis has made great progress and is widely used by physicians.
An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which
was the great motivation towards our study. In our work, tachycardia
features obtained are used for the training and testing of a Neural
Network. In this study we are using Fuzzy Probabilistic Neural
Networks as an automatic technique for ECG signal analysis. As
every real signal recorded by the equipment can have different
artifacts, we needed to do some preprocessing steps before feeding it
to our system. Wavelet transform is used for extracting the
morphological parameters of the ECG signal. The outcome of the
approach for the variety of arrhythmias shows the represented
approach is superior than prior presented algorithms with an average
accuracy of about %95 for more than 7 tachy arrhythmias.},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {6},
	  number    = {11},
	  year      = {2012},
	  pages     = {618 - 621},
	  ee        = {https://publications.waset.org/pdf/2239},
	  url   	= {https://publications.waset.org/vol/71},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 71, 2012},
	}