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A hand-designed Recurrent Convolutional Neural Network achieved a validation score of 62.3% and a full test score of 38.2% for cardiac abnormality classification without manual feature engineering.
A hand-designed Recurrent Convolutional Neural Network (RCNN) can effectively classify cardiac abnormalities from 12-lead ECG signals without manual feature engineering, achieving competitive results in the PhysioNet Challenge 2020.
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May support automated ECG classification without feature engineering; leaves open prospective clinical validation.
). In this paper, as part of the PhysioNet Challenge 2020, we propose two cardiac abnormality detection and classification neural models, using 12-lead ECG signals. Our ECU team proposes a hand-designed Recurrent Convolutional Neural Network (RCNN), consisting of 49 one-dimensional convolutional layers, 16 skip connections, and one Bi-Directional LSTM layer. This model, without relying on any pre-processing or manual feature engineering, achieved a Challenge validation score of 62.3% and a full test score of 38.2%, ranking us 9 th out of 41 teams in the official ranking. Our second neural model, designed through neural architecture search, did not score on the full test dataset nor on the validation dataset; however, we optimistically expect performance improvement compared to our handdesigned RCNN model. This model scored 64.4% using 10fold cross-validation on the training dataset, which is 2.5% higher than the training score of our RCNN model, using 10-fold cross-validation. 1.
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Fayyazifar et al. (2020) studied Cardiac rhythm abnormality (n=43,101). Recurrent Convolutional Neural Network (RCNN) and NAS-based CNN vs. Hand-designed CNN was evaluated on Classification performance (Challenge validation and full test score). A hand-designed Recurrent Convolutional Neural Network achieved a validation score of 62.3% and a full test score of 38.2% for cardiac abnormality classification without manual feature engineering.
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