Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram
Authors: Yeongyeon Na, Minje Park, Yunwon Tae, Sunghoon Joo
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | ST-MEM outperforms other SSL baseline methods in various experimental settings for arrhythmia classification tasks. Moreover, we demonstrate that ST-MEM is adaptable to various lead combinations. Through quantitative and qualitative analysis, we show a spatio-temporal relationship within ECG data. In this section, we examine the results of our experiments, evaluating them both quantitatively and qualitatively to verify the effectiveness of ST-MEM. Additional experimental results are reported in Appendix B. |
| Researcher Affiliation | Industry | Yeongyeon Na , Minje Park , Yunwon Tae , and Sunghoon Joo VUNO Inc. {yeongyeon.na, minje.park, yunwon.tae, sunghoon.joo}@vuno.co |
| Pseudocode | No | The paper describes the method conceptually and visually (Figure 3) but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/bakqui/ST-MEM. |
| Open Datasets | Yes | PTB-XL, Chapman, Ningbo and CPSC2018 : https://physionet.org/content/ challenge-2021/1.0.3/ CODE-15 : https://zenodo.org/record/4916206#.YUG9MStxe Ul Physio Net2017 : https://physionet.org/content/challenge-2017/1. 0.0/ |
| Dataset Splits | Yes | Dividing downstream datasets into train, validation and test set. Finally, regarding the downstream datasets, they are divided into training, validation, and test sets, following a 70-10-20 configuration. Table 10 provides the preprocessing steps for PTB-XL, along with information about the utilized train, validation, and test sets. Likewise, Table 11 presents information regarding CPSC2018, while Table 12 outlines details concerning Physio Net2017. |
| Hardware Specification | Yes | For environment details, all experiments examined with Ubuntu 20.04.6, AMD EPYC 7502 32Core Processor, and NVIDIA Ge Force RTX 3080 Ti. |
| Software Dependencies | Yes | The version of the libraries we used in all experiments are 3.9.13 for Python and 1.11.0 for Py Torch. |
| Experiment Setup | Yes | Further details of hyperparameters used in each pre-training is shown in Table 6. Table 6: Hyperparameter settings. Pre-training Fine-tuning Linear evaluation Backbone Vi T-B Vi T-B Vi T-B Learning rate 0.0012 0.001 0.001 Batch size 2048 1024 32 Epochs 800 100 100 Optimizer Adam W Adam W Adam W Learning rate scheduler Cosine anealing Cosine anealing Cosine anealing Warump steps 40 5 5 |