ECG signal classification based on Deep Learning by using Convolutional Neural Network (CNN)

  • Aqeel M. Hamad alhussainy university of thi-Qar
  • Ammar D. Jasim

Abstract

Cardiovascular diseases (CVDs) are consider  the main cause  of death today According to World Health Organization (WHO),and because that ECG signal is very important tool in monitoring and diagnosis of these disease , different automatic methods were proposed based on this signal. [1]. The manual analysis of ECG signals is suffered different challenges such as differeculty of detecting and classify waveform of this signal, So, many machine learning methods  are  explored to describe  the anomalies ECG signal accurately . Deep learning (DL) can be used in ECG classification, it can improve the quality of the automatic classification system. In this paper , we have proposed a deep learning classification system by using  different layers of convolution, rectifier and pooling operations  that can be used to increase feature extraction of ECG signal.

       We have proposed two models, one is used for input signal of 1-D, in which we designed model for classification csv type of data for ECG signal, while in the second proposed system, we used model for 2-D signal after convert it from its csv type .  2-D signal (ECG image) is used in order to augment the two dimensional signal with different methods to increase the accuracy of the model by training it with geometric transformation of the original input images such as rotation, shearing etc.The results are compared with AlexNet and other  models  based on the metrics, which are    used to measure the performance of the proposed work, the result show that, the proposed models improve the efficiency of the classification  in the two systems.

Published
2020-12-31
How to Cite
Hamad alhussainy, A. M., & Jasim, A. D. (2020). ECG signal classification based on Deep Learning by using Convolutional Neural Network (CNN). Iraqi Journal of Information & Communications Technology, 3(3), 12-23. https://doi.org/10.31987/ijict.3.3.106