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AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017
Open accessAbstract
The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9-61 s) ECG recordings performed by patients. A total of 12,186 ECGs were used: 8,528 in the public training set and 3,658 in the private hidden test set. Due to the high degree of inter-expert disagreement between a significant fraction of the expert labels we implemented a mid-competition bootstrap approach to expert relabeling of the data, levering the best performing Challenge entrants' algorithms to identify contentious labels. A total of 75 independent teams entered the Challenge using a variety of traditional and novel methods, ranging from random forests to a deep learning approach applied to the raw data in the spectral domain. Four teams won the Challenge with an equal high F1 score (averaged across all classes) of 0.83, although the top 11 algorithms scored within 2% of this. A combination of 45 algorithms identified using LASSO achieved an F1 of 0.87, indicating that a voting approach can boost performance.
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Cited by
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148
PTB-XL, a large publicly available electrocardiography dataset
Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F. I., Samek, W., & Schaeffter, T. (2020) · Scientific Data · doi:10.1038/s41597-020-0495-6 · Cited by 1,315
Automatic diagnosis of the 12-lead ECG using a deep neural network
Ribeiro, A. H., Ribeiro, M. H., Paixão, G. M. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., Ferreira, M. P. S., Andersson, C. R., Macfarlane, P. W., Meira, W., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15432-4 · Cited by 894
Explainable AI for clinical and remote health applications: A survey on tabular and time series data
Di Martino, F., & Delmastro, F. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10304-3 · Cited by 216
A Review of Recurrent Neural Network-Based Methods in Computational Physiology
Mao, S., & Sejdić, E. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3145365 · Cited by 128
Metadata
- Authors
- Clifford, G., Liu, C., Moody, B., Lehman, L.-W., Silva, I., Li, Q., Johnson, A., & Mark, R.
- Venue
- Computing in Cardiology Conference (CinC)
- Publisher
- Computing in Cardiology
- Published
- 2017-09-14
- Volume
- 44
- ISSN
- 2325-887X (ISSN-L)
- OpenAlex
- W2794550444
- Cited by
- 838
- References
- 0
- FWCI
- 133.25
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Clifford, G., Liu, C., Moody, B., Lehman, L.-W., Silva, I., Li, Q., Johnson, A., & Mark, R. (2017). AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017. Computing in Cardiology Conference (CinC). https://doi.org/10.22489/cinc.2017.065-469
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