A study on several critical problems on arrhythmia detection using varying-dimensional electrocardiography.

. This work tries to provide answers to several critical questions on varying-dimensional electrocardiography (ECG) raised by the PhysioNet/Computing in Cardiology Challenge 2021 (CinC2021): can subsets of the standard 12 leads provide models with adequate information to give comparable performances for classifying ECG abnormalities? Can models be designed to be effective enough to classify a broad range of ECG abnormalities?. To tackle these problems, we (challenge team name 'Revenger') propose several novel architectures within the framework of convolutional recurrent neural networks. These deep learning models are proven effective, and moreover, they provide comparable performances on reduced-lead ECGs, even in the extreme case of 2-lead ECGs. In addition, we propose a 'lead-wise' mechanism to facilitate parameter reuse of ECG neural network models. This mechanism largely reduces model sizes while keeping comparable performances. To further augment model performances on specific ECG abnormalities and to improve interpretability, we manually design auxiliary detectors based on clinical diagnostic rules.. In the post-challenge session, our approach achieved a challenge score of 0.38, 0.40, 0.41, 0.40, 0.35 on the 12, 6, 4, 3, 2-lead subsets respectively on the CinC2021 hidden test set.. The proposed approach gives positive answers to the critical questions CinC2021 raises and lays a solid foundation for further research in the future on these topics.