Improving the Speed and Accuracy of Arrhythmia Classification Based on Morphological Features of ECG Signal

  • Mohammad Reza Yousefi Najafabad Branch, Islamic Azad University
  • Kamran Dehghany Habibabadi Najafabad Branch, Islamic Azad University
Keywords: Heart, ECG, Feature Extraction, Morphology, Classification, Evaluation


Electrocardiogram (ECG) signals play a crucial role in determining heart disease. Somehow, by knowing the heart rate on the ECG, one can distinguish the type of arrhythmia and the type of disease. Arrhythmias are a type of heart disease that affects the normal functioning of the heart. The electrical activity of the heart is shown at the peaks of P, QRS, T, and the ST and PR sections. In this study, an effective method for identifying cardiac arrhythmias based on morphological features is presented. The extracted features are classified using SVM and KNN classification and random forest RF. Accuracy, sensitivity, positive predictive rate, negative predictive rate as well as execution time were used to evaluate the proposed method. The results show the superiority of the proposed method.


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How to Cite
Yousefi, M., & Dehghany Habibabadi, K. (2020). Improving the Speed and Accuracy of Arrhythmia Classification Based on Morphological Features of ECG Signal. Majlesi Journal of Telecommunication Devices, 9(4), 151-158. Retrieved from