Two Might Do: A Beat-by-Beat Classification of Cardiac Abnormalities Using Deep Learning with Domain-Specific Features

Abstract

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.