ECG Arrhythmia Detection with Deep Learning

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

28th Signal Processing and Communications Applications Conference (SIU), ELECTR NETWORK, 5 - 07 Ekim 2020 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu49456.2020.9302219
  • Basıldığı Ülke: ELECTR NETWORK
  • Anahtar Kelimeler: Arrhythmia, Deep Learning, ECG Images, CLASSIFICATION
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

Arrhythmia is any irregularity of heart rate that cause an abnormality in your heart rhythm. Manual analysis of Electrocardiogram (ECG) signal is not enough for quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning approach for detection of five different arrhythmia types based on 2D convolutional neural networks (CNN) architecture. ECG signals were obtained from MIT-BIll arrhythmia database. For CNN architecture, each ECG signal was segmented into heartbeats, then each heartbeat was transformed into 2D grayscale heartbeat image. 2D CNN model was used due to success of image recognition. The proposed model result demonstrate that CNN and ECG image formation give highest result when classified different types of ECG arrhythmic signals.