Cardiac arrhythmia detection from 2d ecg images by using deep learning technique


Izci E., Ozdemir M. A., Degirmenci M., Akan A.

2019 Medical Technologies Congress, TIPTEKNO 2019, İzmir, Turkey, 3 - 05 October 2019 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/tiptekno.2019.8895011
  • City: İzmir
  • Country: Turkey
  • Keywords: Arrhythmia Detection, Convolutional Neural Networks, Deep Learning, ECG Images, Electrocardiogram, CLASSIFICATION, ELECTROCARDIOGRAMS
  • Van Yüzüncü Yıl University Affiliated: Yes

Abstract

© 2019 IEEE.Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of them require a lot of time and depends on experience of clinicians. This paper proposes deep learning-based novel 2-D convolutional neural network (CNN) approach for accurate classification of five different arrhythmia types. The performance of the proposed architecture is tested on Electrocardiogram (ECG) signals that are taken from MIT-BIH arrhythmia benchmark database. ECG signals was segmented into heartbeats and each of the heartbeats was converted into 2-D grayscale images as an input data for CNN structure. The accuracy of the proposed architecture was found as 97.42% on the training results revealed that the proposed 2-D CNN architecture with transformed 2-D ECG images can achieve highest accuracy without any preprocessing and feature extraction and feature selection stages for ECG signals.