K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection
Authors: Yuxi Zhou, Shenda Hong, Junyuan Shang, Meng Wu, Qingyun Wang, Hongyan Li, Junqing Xie
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that the proposed method with 0.8125 F1NAOP score outperforms all state-of-the-art deep learning methods for AF detection task by 6.8% |
| Researcher Affiliation | Academia | 1School of Electronics Engineering and Computer Science, Peking University, Beijing, China 2Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing, China 3Medical Informatics Center, Peking University, Beijing, China |
| Pseudocode | Yes | Algorithm 1 K-LABELS(X(i), K, φ) |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | Dataset We carry out experiments on Physio Net/Computing in Cardiology Challenge 2017 Dataset [Clifford et al., 2017] |
| Dataset Splits | Yes | In implementation, we randomly split 80% for model training, and evaluate on remaining 20% testing data. We evaluate the effects of various state-of-art DNN methods using the measurements given above by repeatedly running 20 times using 5-fold cross validation, and report the average results. |
| Hardware Specification | No | The paper mentions software implementations ('Methods are implemented using Python 3.6.2 on Tensor Flow version r1.4.') but does not specify any hardware details like GPU/CPU models or processor types used for experiments. |
| Software Dependencies | Yes | Methods are implemented using Python 3.6.2 on Tensor Flow version r1.4. |
| Experiment Setup | Yes | We set the parameter value of window size and max stride threshold MS to be 6000 and 500 for skewness-driven dynamic data augmentation respectively. Then we set the parameter value of K and N split to be 3 and 300 for training K-margin-based RCR-net using augmented data (see Section 3.2), and identifying the final predicting label of each ECG record by the trained model. |