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  ---
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  license: mit
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  ---
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- # 0. FSL_ECG_QA_Dataset Description
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- **FSL_ECG_QA_Dataset** is a **benchmark dataset** specifically designed to accompany the paper [*"Electrocardiogram–Language Model for Few-Shot Question Answering with Meta Learning"*] (https://arxiv.org/abs/2410.14464). It supports research in combining **electrocardiogram (ECG) signals** with **natural language question answering (QA)**, particularly in **few-shot** and **meta-learning** scenarios.
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- ## 0.2 Dataset Highlights
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  - 🧠 **Task Diversification**: Restructured **ECG-QA tasks** promote rapid **few-shot adaptation**.
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  - 🧬 **Fusion Mapping**: A lightweight **multimodal mapper** bridges **ECG** and **language features**.
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  - 🚀 **Model Generalization**: **LLM-agnostic design** ensures broad **transferability** and **robustness**.
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- # 1 Datasets
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- ## 1.1 Loading the Dataset class
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  ```python
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  python load_class.py --base_path /your/actual/path/to/ecgqa/ptbxl/paraphrased --test_dataset ptb-xl
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  python load_class.py --paraphrased_path /your/actual/path/to/ecgqa/mimic-iv-ecg/paraphrased --test_dataset mimic
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  (all tested mimic-iv-ecg dataset is listed in "data/processed_test_30k.json")
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  ```
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- ## 1.2 load ECG-QA-FSL dataset
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  ```python
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  python data_loader.py
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  --test_dataset ptb-xl
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  ```
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- ## 1.3 sample of ECG-QA-FSL dataset
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  ```python
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  episode = {
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  }
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  ```
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- ### 1.4 New Datasets Class distribution
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  | Question Type | Attributes | Answers | Classes (train:test) | Samples | Example |
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  |-----------------|------------|----------------------------------|-----------------------|---------|-------------------------------------------------------------------------|
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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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- # 2 Citation
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  ```
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  @article{tang2024electrocardiogram,
 
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  license: mit
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+ # FSL ECG QA Dataset
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+ **FSL ECG QA Dataset** is a **benchmark dataset** used in paper [*"Electrocardiogram–Language Model for Few-Shot Question Answering with Meta Learning"*] (https://arxiv.org/abs/2410.14464). It supports research in combining **electrocardiogram (ECG) signals** with **natural language question answering (QA)**, particularly in **few-shot** and **meta-learning** scenarios.
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+ ## Dataset Highlights
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  - 🧠 **Task Diversification**: Restructured **ECG-QA tasks** promote rapid **few-shot adaptation**.
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  - 🧬 **Fusion Mapping**: A lightweight **multimodal mapper** bridges **ECG** and **language features**.
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  - 🚀 **Model Generalization**: **LLM-agnostic design** ensures broad **transferability** and **robustness**.
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+ # 1. Datasets
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+ ## 1.1 Load the classes
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  ```python
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  python load_class.py --base_path /your/actual/path/to/ecgqa/ptbxl/paraphrased --test_dataset ptb-xl
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  python load_class.py --paraphrased_path /your/actual/path/to/ecgqa/mimic-iv-ecg/paraphrased --test_dataset mimic
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  (all tested mimic-iv-ecg dataset is listed in "data/processed_test_30k.json")
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  ```
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+ ## 1.2 Load ECG QA FSL dataset
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  ```python
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  python data_loader.py
 
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  --test_dataset ptb-xl
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  ```
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+ ## 1.3 Sample of ECG QA FSL dataset
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  ```python
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  episode = {
 
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  }
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  ```
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+ ### 1.4 Class distribution
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  | Question Type | Attributes | Answers | Classes (train:test) | Samples | Example |
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  |-----------------|------------|----------------------------------|-----------------------|---------|-------------------------------------------------------------------------|
 
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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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+ # 2. Citation
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  ```
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  @article{tang2024electrocardiogram,