Two Might Do: A Beat-by-Beat Classification of Cardiac Abnormalities Using Deep Learning with Domain-Specific Features
2021 Computing in Cardiology, CinC 2021, Brno, Czech Republic, 13 - 15 September 2021, vol.2021-September, (Full Text)
- Publication Type: Conference Paper / Full Text
- Volume: 2021-September
- Doi Number: 10.23919/cinc53138.2021.9662935
- City: Brno
- Country: Czech Republic
- Middle East Technical University Affiliated: Yes
Abstract
© 2021 Creative Commons.This paper proposes an efficient convolutional neural network to detect 26 different classes of cardiac activities from different numbers of leads in the Phys-ionetlComputing data in the Cardiology Challenge 2021. The proposed CNN architecture is designed to utilize heart rate variation features from ECG recordings and wave-form morphologies of heartbeats simultaneously. Also, the designed architecture is flexible for the implementation of a different number of leads with a varied length of ECG recordings. The proposed algorithm achieved a score of 0.38 using only 2 channels ofECG on all recordings for the hidden test set of the challenge, placing us 21, 20, 19, 20, 20th (Team name: METU-19) out of 39 teams for 12, 6, 4, 3, and 2-leads respectively. These results show the potential of an efficient, flexible novel neural network for beat-by-beat classification of raw ECG recordings to a complex multi-class multi-label classification problem.