Automated Detection of Arrhythmia in ECG Signals using CNN


  • Ghousia S. Begum Assistant Professor, Department of Electronics and Communication Engineering, RNS Institute of Technology, Bengaluru, India.
  • Vipula Singh Professor, Department of Electronics and Communication Engineering, RNS Institute of Technology, Bengaluru, India.


Cardiovascular Disease, Electro Cardio Gram, MIT-BIH, Convolution Neural Network Based Continual Normalization Classifier.


In this world, around 31% of the deaths are commonly caused because of cardiovascular diseases. Around 80% of sudden deaths occur due to cardiac arrhythmias and heart diseases. The mortality rate has increased for cardiac disease and therefore early heart disease detection is significant to preclude patients from dying. At the initial phase, the heart disease is detected by analyzing abnormal heartbeats. The existing models failed to select the features before performing the extraction of features. The developed model examined various databases to surpass the overfitting issue. Therefore, in the present research work, the CNN based Continual Normalization (CN) classifier is used to speed up the training to a higher learning rate to enable simpler learning for the standard deviation of the neurons' output. The extracted features were used to classify ECG signals into 5 important classes named as N, S, V, F & Q which denote the kinds of arrhythmia. The findings revealed that the proposed CNN based Continual Normalization technique obtained an accuracy of 99.2 % which is better when compared with the existing research namely the Dual Fully Connected Neural Network that obtained 93.4 % of accuracy, and the Optimization-Enabled Deep Convolutional Neural Network that accomplished 93.19 % of accuracy.


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How to Cite

Begum, G. S. ., & Singh , V. . (2024). Automated Detection of Arrhythmia in ECG Signals using CNN. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 491–502. Retrieved from



Research Article