ENCASE combines deep neural networks and expert features together for AF Classification from a short single lead ECG recording. It won the First Place in the PhysioNet/Computing in Cardiology Challenge 2017 (https://physionet.org/challenge/2017), with an overall F1 score of 0.83. The original code can be downloaded from https://physionet.org/challenge/2017/sources/shenda-hong-221.zip
Detailed description of ENCASE can be found at http://www.cinc.org/archives/2017/pdf/178-245.pdf. If you find the idea useful or use this code in your own work, please cite our paper as
@inproceedings{hong2017encase,
author = {Shenda Hong and Meng Wu and Yuxi Zhou and Qingyun Wang and Junyuan Shang and Hongyan Li and Junqing Xie},
title = {{ENCASE:} an ENsemble ClASsifiEr for {ECG} Classification Using Expert
Features and Deep Neural Networks},
booktitle = {CinC},
year = {2017},
url = {https://doi.org/10.22489/CinC.2017.178-245},
doi = {10.22489/CinC.2017.178-245}
}
and
@article{hong2019combining,
doi = {10.1088/1361-6579/ab15a2},
url = {https://doi.org/10.1088%2F1361-6579%2Fab15a2},
year = 2019,
month = {Jun},
publisher = {{IOP} Publishing},
volume = {40},
number = {5},
pages = {054009},
author = {Shenda Hong and Yuxi Zhou and Meng Wu and Junyuan Shang and Qingyun Wang and Hongyan Li and Junqing Xie},
title = {Combining deep neural networks and engineered features for cardiac arrhythmia detection from {ECG} recordings},
journal = {Physiological Measurement}
}
Please refer to the Challenge website https://physionet.org/challenge/2017/#introduction and Gari's paper http://www.cinc.org/archives/2017/pdf/065-469.pdf.
Training data can be found at https://archive.physionet.org/challenge/2017/#challenge-data Please use Revised labels (v3) at https://archive.physionet.org/challenge/2017/REFERENCE-v3.csv