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The dataset is a structured reorganization of the existing ECG-QA dataset, adapted to suit meta-learning tasks. It draws samples from ECG sources such as [PTB-XL](https://physionet.org/content/ptb-xl/1.0.3/) and [MIMIC-IV-ECG](https://physionet.org/content/mimic-iv-ecg/1.0/), and [ECG-QA dataset](https://github.com/Jwoo5/ecg-qa?tab=readme-ov-file) organizes them into diverse task sets based on question types including **verify(yes/no)**, **choice(Condition_A/Condition_B)**, and **query(open-ended)** question in table 2.2. and clinical attributes (e.g., SCP codes, noise type, axis deviation) used to describing the ECG. This structure enables models to rapidly adapt to new diagnostic tasks with limited annotated examples.
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### 1.1.1 Source Datasets Instances
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```python
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| Question Type | Attributes | Answers | Classes (train:test) | Samples | Example |
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|-----------------|------------|----------------------------------|-----------------------|---------|-------------------------------------------------------------------------|
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| Single-Verify | 94 | yes/no | 156 (124:32) | 34,105 | Q: Does this ECG show 1st degree av block? <br> A: yes/no |
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| Single-Choose | 165 | both/none/attr_1/attr_2 | 262 (209:53) | 47,655 | Q: Which noise does this ECG show, baseline drift or static noise?
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| Single-Query | 30 | attr_1/attr_2/.../attr_n | 260 (208:52) | 63,125 | Q: What direction is this ECG deviated to? <br> A: Normal axis/... |
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| All | 206 | yes/no/both/none/.../attr_n | 678 (541:137) | 144,885 | ... |
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The dataset is a structured reorganization of the existing ECG-QA dataset, adapted to suit meta-learning tasks. It draws samples from ECG sources such as [PTB-XL](https://physionet.org/content/ptb-xl/1.0.3/) and [MIMIC-IV-ECG](https://physionet.org/content/mimic-iv-ecg/1.0/), and [ECG-QA dataset](https://github.com/Jwoo5/ecg-qa?tab=readme-ov-file) organizes them into diverse task sets based on question types including **verify(yes/no)**, **choice(Condition_A/Condition_B)**, and **query(open-ended)** question in table 2.2. and clinical attributes (e.g., SCP codes, noise type, axis deviation) used to describing the ECG. This structure enables models to rapidly adapt to new diagnostic tasks with limited annotated examples.
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| Task Type | Question | Answer Options |
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|-----------------|--------------------------------------------------------------------------|------------------------------------------|
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| Single-Verify | Q: Does this ECG show 1st degree AV block? | A: yes / no |
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| Single-Choose | Q: Which noise does this ECG show, baseline drift or static noise? | A: baseline drift / static noise |
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| Single-Query | Q: What direction is this ECG deviated to? | A: Normal axis / ... |
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### 1.1.1 Source Datasets Instances
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```python
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| Question Type | Attributes | Answers | Classes (train:test) | Samples | Example |
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|-----------------|------------|----------------------------------|-----------------------|---------|-------------------------------------------------------------------------|
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| Single-Verify | 94 | yes/no | 156 (124:32) | 34,105 | Q: Does this ECG show 1st degree av block? <br> A: yes/no |
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| Single-Choose | 165 | both/none/attr_1/attr_2 | 262 (209:53) | 47,655 | Q: Which noise does this ECG show, baseline drift or static noise? <br> A: baseline drift /static noise |
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| Single-Query | 30 | attr_1/attr_2/.../attr_n | 260 (208:52) | 63,125 | Q: What direction is this ECG deviated to? <br> A: Normal axis/... |
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| All | 206 | yes/no/both/none/.../attr_n | 678 (541:137) | 144,885 | ... |
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