A Novel Convolutional Neural Network for Arrhythmia Detection From 12-lead Electrocardiograms
| Autor: | Lirui Xu, Hao Zhang, Xianxiang Chen, Pengfei Zhang, Zhengling He, Weisong Li, Zhongrui Bai, Pan Xia |
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| Rok vydání: | 2020 |
| Předmět: |
Arrhythmia detection
Artificial neural network Computer science business.industry Deep learning 0206 medical engineering Pattern recognition 02 engineering and technology Residual 020601 biomedical engineering Convolutional neural network Task (project management) 03 medical and health sciences 0302 clinical medicine Test score Artificial intelligence Lead (electronics) business 030217 neurology & neurosurgery |
| Zdroj: | CinC |
| ISSN: | 2325-887X |
| DOI: | 10.22489/cinc.2020.196 |
| Popis: | Electrocardiogram (ECG) is a widely medical tool used in the clinical diagnosis of arrhythmia, numerous algorithms based on deep learning have been proposed to achieve automatic arrhythmia detection. In PhysioNetlComputing in Cardiology Challenge 2020, inspired by the deep residual learning and attention mechanism, we proposed a novel neural network to accomplish this classification task. The backbone of the network is a carefully designed 2-D convolutional neural network (CNN) with residual connection and attention mechanism, and it can adapt to multi-lead ECG signals as input. The first 10 seconds of records from all leads are extracted and preprocessed as input for end-to-end training, and the prediction probabilities of 27 categories are output. The proposed algorithm was firstly verified and adjusted via 5-fold cross-validation on officially published datasets from 4 multiple sources. Finally, our team (MetaHeart) achieved a challenge validation score of 0.616 and full test score of 0.370, but were not ranked due to omissions in the submission. |
| Databáze: | OpenAIRE |
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