ECG signal diagnoses using intelligent systems


  • Raaed Faleh Hassan Department of Medical Instrument Engineering Techniques, Electrical Engineering Technical College, Middle Technical University
  • Sally Abdulmunem Shaker Department of Medical Instrument Engineering Techniques, Electrical Engineering Technical College, Middle Technical University





ECG, Backpropagation, Fuzzy, MATLAB, Cardiovascular Diseases


Accurate diagnosis of arrhythmias plays a crucial role in saving the lives of many heart patients. The aim of this research is to find the more efficient method to diagnosis electrocardiogram (ECG) diseases. This work presents the use of Backpropagation neural network (BPNN) and fuzzy logic for automatic detection of cardiac arrhythmias based on analysis of the ECG. These a more valuable tool used to classify ECG signals in cardiac patients. Data collected from physioBank ATM. The analysis of the ECG signal is performed in MATLAB environment. In BPNN the results appear that the only two misclassifications happened to result in an accuracy of 90.4%. while in fuzzy inference system the results appear that the classification accuracy is 100%.





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