Electrocardio Panorama: Synthesizing New ECG views with Self-supervision
Authors: Jintai Chen, Xiangshang Zheng, Hongyun Yu, Danny Z. Chen, Jian Wu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments verify that our Nef-Net performs well on Electrocardio Panorama synthesis, and outperforms the previous work on the auxiliary tasks (ECG view transformation and ECG synthesis from scratch). |
| Researcher Affiliation | Academia | Jintai Chen1 , Xiangshang Zheng1 , Hongyun Yu1 , Danny Z. Chen2 , Jian Wu3 1College of Computer Science and Technology, Zhejiang University, Hangzhou, China 2 Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, USA 3 The First Affiliated Hospital, and Department of Public Health, Zhejiang University School of Medicine, Hangzhou, China jtchen721@gmail.com, {xszheng,yuhongyun777,wujian2000}@zju.edu.cn, dchen@nd.edu |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The codes and the division labels of cardiac cycles and ECG deflections on Tianchi ECG and PTB datasets are available at https://github. com/WhatAShot/Electrocardio-Panorama. |
| Open Datasets | Yes | We conduct experiments using the MIT-BIH dataset [Moody et al., 2001], PTB dataset [Bousseljot et al., 1995], and Tianchi ECG dataset3. The Tianchi dataset provides a link: https://tianchi.aliyun.com/competition/entrance/231754/ information?lang=en-us |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' partitions (e.g., 'randomly partitioned into a training set and a test set with probabilities 0.8 and 0.2, respectively' for PTB and Tianchi, and specific sample counts for MIT-BIH), but does not explicitly describe a 'validation set' or its split. |
| Hardware Specification | Yes | We report the means and standard deviations over 3 runs with an RTX2080Ti GPU for all the experiments. |
| Software Dependencies | Yes | We use Py Torch 1.7.1 to implement Nef-Net. |
| Experiment Setup | Yes | In training, the batch size is 32. Nef-Net is run 150 epochs in training. The learning rate is initialized to 0.1, and is reduced by 10 at the 50-th and 100-th epoch. We use SGD as the optimizer with momentum 0.9. |