MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Authors: Shenda Hong, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments showed MINA achieved PR-AUC 0.9436 (outperforming the best baseline by 5.51%) in real world ECG dataset. |
| Researcher Affiliation | Collaboration | Shenda Hong1,2,5 , Cao Xiao3 , Tengfei Ma4 , Hongyan Li1,2 and Jimeng Sun5 1School of Electronics Engineering and Computer Science, Peking University, China 2Key Laboratory of Machine Perception (Ministry of Education), Peking University, China 3Analytics Center of Excellence, IQVIA, USA 4IBM Research, USA 5Department of Computational Science and Engineering, Georgia Institute of Technology, USA |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/hsd1503/MINA. |
| Open Datasets | Yes | We conducted all experiments using real world ECG data from Physio Net Challenge 2017 databases [Clifford et al., 2017]. |
| Dataset Splits | Yes | We first divided the data into a training set (75%), a validation set (10%) and a test set (15%) to train and evaluate in all tasks. |
| Hardware Specification | Yes | All models were implemented in Py Torch version 0.3.1, and trained with a system equipped with 64GB RAM, 12 Intel Core i7-6850K 3.60GHz CPUs and Nvidia Ge Force GTX 1080. |
| Software Dependencies | Yes | All models were implemented in Py Torch version 0.3.1 |
| Experiment Setup | Yes | In convolutional layers of CNN, CRNN, ACRNN and MINA, we use one layer for each model. The number of filters is set to 64, the filter size is set to 32 and strider is set to 2. Pooling is replaced by attention mechanism. Convα of Kα has one filter with size set to 32, the strider is also 2. In recurrent layers of CRNN, ACRNN and MINA, we also use one single layer for each model, the number of hidden units in each LSTM is set to 32. The dropout rate in the fully connected prediction layer is set to 0.5. In sliding window segmentation, we use non-overlapping stride with T = 50. Deep models are trained with the mini-batch of 128 samples for 50 iterations, which was a sufficient number of iterations for achieving the best performance for the classification task. The final model was selected using early stopping criteria on validation set. ... All models were optimized using Adam [Kingma and Ba, 2014], with the learning rate set to 0.003. |